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[2024] Easy To Download Data-Engineer-Associate Actual Exam Dumps Resources [Q21-Q42]

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[2024] Easy To Download Data-Engineer-Associate Actual Exam Dumps Resources

Uplift Your Data-Engineer-Associate Exam Marks With The Help of Data-Engineer-Associate Dumps

NEW QUESTION # 21
A data engineer has a one-time task to read data from objects that are in Apache Parquet format in an Amazon S3 bucket. The data engineer needs to query only one column of the data.
Which solution will meet these requirements with the LEAST operational overhead?

  • A. Use S3 Select to write a SQL SELECT statement to retrieve the required column from the S3 objects.
  • B. Prepare an AWS Glue DataBrew project to consume the S3 objects and to query the required column.
  • C. Confiqure an AWS Lambda function to load data from the S3 bucket into a pandas dataframe- Write a SQL SELECT statement on the dataframe to query the required column.
  • D. Run an AWS Glue crawler on the S3 objects. Use a SQL SELECT statement in Amazon Athena to query the required column.

Answer: A

Explanation:
Option B is the best solution to meet the requirements with the least operational overhead because S3 Select is a feature that allows you to retrieve only a subset of data from an S3 object by using simple SQL expressions.
S3 Select works on objects stored in CSV, JSON, or Parquet format. By using S3 Select, you can avoid the need to download and process the entire S3 object, which reduces the amount of data transferred and the computation time. S3 Select is also easy to use and does not require any additional services or resources.
Option A is not a good solution because it involves writing custom code and configuring an AWS Lambda function to load data from the S3 bucket into a pandas dataframe and query the required column. This option adds complexity and latency to the data retrieval process and requires additional resources and configuration.Moreover, AWS Lambda has limitations on the execution time, memory, and concurrency, which may affect the performance and reliability of the data retrieval process.
Option C is not a good solution because it involves creating and running an AWS Glue DataBrew project to consume the S3 objects and query the required column. AWS Glue DataBrew is a visual data preparation tool that allows you to clean, normalize, and transform data without writing code. However, in this scenario, the data is already in Parquet format, which is a columnar storage format that is optimized for analytics.
Therefore, there is no need to use AWS Glue DataBrew to prepare the data. Moreover, AWS Glue DataBrew adds extra time and cost to the data retrieval process and requires additional resources and configuration.
Option D is not a good solution because it involves running an AWS Glue crawler on the S3 objects and using a SQL SELECT statement in Amazon Athena to query the required column. An AWS Glue crawler is a service that can scan data sources and create metadata tables in the AWS Glue Data Catalog. The Data Catalog is a central repository that stores information about the data sources, such as schema, format, and location.
Amazon Athena is a serverless interactive query service that allows you to analyze data in S3 using standard SQL. However, in this scenario, the schema and format of the data are already known and fixed, so there is no need to run a crawler to discover them. Moreover, running a crawler and using Amazon Athena adds extra time and cost to the data retrieval process and requires additional services and configuration.
References:
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
S3 Select and Glacier Select - Amazon Simple Storage Service
AWS Lambda - FAQs
What Is AWS Glue DataBrew? - AWS Glue DataBrew
Populating the AWS Glue Data Catalog - AWS Glue
What is Amazon Athena? - Amazon Athena


NEW QUESTION # 22
A data engineer must build an extract, transform, and load (ETL) pipeline to process and load data from 10 source systems into 10 tables that are in an Amazon Redshift database. All the source systems generate .csv, JSON, or Apache Parquet files every 15 minutes. The source systems all deliver files into one Amazon S3 bucket. The file sizes range from 10 MB to 20 GB. The ETL pipeline must function correctly despite changes to the data schema.
Which data pipeline solutions will meet these requirements? (Choose two.)

  • A. Configure an AWS Lambda function to invoke an AWS Glue crawler when a file is loaded into the S3 bucket. Configure an AWS Glue job to process and load the data into the Amazon Redshift tables.
    Create a second Lambda function to run the AWS Glue job. Create an Amazon EventBridge rule to invoke the second Lambda function when the AWS Glue crawler finishes running successfully.
  • B. Use an Amazon EventBridge rule to invoke an AWS Glue workflow job every 15 minutes. Configure the AWS Glue workflow to have an on-demand trigger that runs an AWS Glue crawler and then runs an AWS Glue job when the crawler finishes running successfully. Configure the AWS Glue job to process and load the data into the Amazon Redshift tables.
  • C. Configure an AWS Lambda function to invoke an AWS Glue workflow when a file is loaded into the S3 bucket. Configure the AWS Glue workflow to have an on-demand trigger that runs an AWS Glue crawler and then runs an AWS Glue job when the crawler finishes running successfully. Configure the AWS Glue job to process and load the data into the Amazon Redshift tables.
  • D. Configure an AWS Lambda function to invoke an AWS Glue job when a file is loaded into the S3 bucket. Configure the AWS Glue job to read the files from the S3 bucket into an Apache Spark DataFrame. Configure the AWS Glue job to also put smaller partitions of the DataFrame into an Amazon Kinesis Data Firehose delivery stream. Configure the delivery stream to load data into the Amazon Redshift tables.
  • E. Use an Amazon EventBridge rule to run an AWS Glue job every 15 minutes. Configure the AWS Glue job to process and load the data into the Amazon Redshift tables.

Answer: B,E

Explanation:
Using an Amazon EventBridge rule to run an AWS Glue job or invoke an AWS Glue workflow job every 15 minutes are two possible solutions that will meet the requirements. AWS Glue is a serverless ETL service that can process and load data from various sources to various targets, including Amazon Redshift. AWS Glue can handle different data formats, such as CSV, JSON, and Parquet, and also support schema evolution, meaning it can adapt to changes in the data schema over time. AWS Glue can also leverage Apache Spark to perform distributed processing and transformation of large datasets. AWS Glue integrates with Amazon EventBridge, which is a serverless event bus service that can trigger actions based on rules and schedules. By using an Amazon EventBridge rule, you can invoke an AWS Glue job or workflow every 15 minutes, and configure the job or workflow to run an AWS Glue crawler and then load the data into the Amazon Redshift tables. This way, you can build a cost-effective and scalable ETL pipeline that can handle data from 10 source systems and function correctly despite changes to the data schema.
The other options are not solutions that will meet the requirements. Option C, configuring an AWS Lambda function to invoke an AWS Glue crawler when a file is loaded into the S3 bucket, and creating a second Lambda function to run the AWS Glue job, is not a feasible solution, as it would require a lot of Lambda invocations andcoordination. AWS Lambda has some limits on the execution time, memory, and concurrency, which can affect the performance and reliability of the ETL pipeline. Option D, configuring an AWS Lambda function to invoke an AWS Glue workflow when a file is loaded into the S3 bucket, is not a necessary solution, as you can use an Amazon EventBridge rule to invoke the AWS Glue workflow directly, without the need for a Lambda function. Option E, configuring an AWS Lambda function to invoke an AWS Glue job when a file is loaded into the S3 bucket, and configuring the AWS Glue job to put smaller partitions of the DataFrame into an Amazon Kinesis Data Firehose delivery stream, is not a cost-effective solution, as it would incur additional costs for Lambda invocations and data delivery. Moreover, using Amazon Kinesis Data Firehose to load data into Amazon Redshift is not suitable for frequent and small batches of data, as it can cause performance issues and data fragmentation. References:
AWS Glue
Amazon EventBridge
Using AWS Glue to run ETL jobs against non-native JDBC data sources
[AWS Lambda quotas]
[Amazon Kinesis Data Firehose quotas]


NEW QUESTION # 23
A company has a production AWS account that runs company workloads. The company's security team created a security AWS account to store and analyze security logs from the production AWS account. The security logs in the production AWS account are stored in Amazon CloudWatch Logs.
The company needs to use Amazon Kinesis Data Streams to deliver the security logs to the security AWS account.
Which solution will meet these requirements?

  • A. Create a destination data stream in the production AWS account. In the production AWS account, create an IAM role that has cross-account permissions to Kinesis Data Streams in the security AWS account.
  • B. Create a destination data stream in the production AWS account. In the security AWS account, create an IAM role that has cross-account permissions to Kinesis Data Streams in the production AWS account.
  • C. Create a destination data stream in the security AWS account. Create an IAM role and a trust policy to grant CloudWatch Logs the permission to put data into the stream. Create a subscription filter in the production AWS account.
  • D. Create a destination data stream in the security AWS account. Create an IAM role and a trust policy to grant CloudWatch Logs the permission to put data into the stream. Create a subscription filter in the security AWS account.

Answer: C

Explanation:
Amazon Kinesis Data Streams is a service that enables you to collect, process, and analyze real-time streaming data. You can use Kinesis Data Streams to ingest data from various sources, such as Amazon CloudWatch Logs, and deliver it to different destinations, such as Amazon S3 or Amazon Redshift. To use Kinesis Data Streams to deliver the security logs from the production AWS account to the security AWS account, you need to create a destination data stream in the security AWS account. This data stream will receive the log data from the CloudWatch Logs service in the production AWS account. To enable this cross-account data delivery, you need to create an IAM role and a trust policy in the security AWS account. The IAM role defines the permissions that the CloudWatch Logs service needs to put data into the destination data stream. The trust policy allows the production AWS account to assume the IAM role. Finally, you need to create a subscription filter in the production AWS account. A subscription filter defines the pattern to match log events and the destination to send the matching events. In this case, the destination is the destination data stream in the security AWS account. This solution meets the requirements of using Kinesis Data Streams to deliver the security logs to the security AWS account. The other options are either not possible or not optimal. You cannot create a destination data stream in the production AWS account, as this would not deliver the data to the security AWS account. You cannot create a subscription filter in the security AWS account, as this would not capture the log events from the production AWS account. References:
Using Amazon Kinesis Data Streams with Amazon CloudWatch Logs
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 3: Data Ingestion and Transformation, Section 3.3: Amazon Kinesis Data Streams


NEW QUESTION # 24
A company currently stores all of its data in Amazon S3 by using the S3 Standard storage class.
A data engineer examined data access patterns to identify trends. During the first 6 months, most data files are accessed several times each day. Between 6 months and 2 years, most data files are accessed once or twice each month. After 2 years, data files are accessed only once or twice each year.
The data engineer needs to use an S3 Lifecycle policy to develop new data storage rules. The new storage solution must continue to provide high availability.
Which solution will meet these requirements in the MOST cost-effective way?

  • A. Transition objects to S3 One Zone-Infrequent Access (S3 One Zone-IA) after 6 months. Transfer objects to S3 Glacier Flexible Retrieval after 2 years.
  • B. Transition objects to S3 One Zone-Infrequent Access (S3 One Zone-IA) after 6 months. Transfer objects to S3 Glacier Deep Archive after 2 years.
  • C. Transition objects to S3 Standard-Infrequent Access (S3 Standard-IA) after 6 months. Transfer objects to S3 Glacier Flexible Retrieval after 2 years.
  • D. Transition objects to S3 Standard-Infrequent Access (S3 Standard-IA) after 6 months. Transfer objects to S3 Glacier Deep Archive after 2 years.

Answer: D

Explanation:
To achieve the most cost-effective storage solution, the data engineer needs to use an S3 Lifecycle policy that transitions objects to lower-cost storage classes based on their access patterns, and deletes them when they are no longer needed. The storage classes should also provide high availability, which means they should be resilient to the loss of data in a single Availability Zone1. Therefore, the solution must include the following steps:
Transition objects to S3 Standard-Infrequent Access (S3 Standard-IA) after 6 months. S3 Standard-IA is designed for data that is accessed less frequently, but requires rapid access when needed. It offers the same high durability, throughput, and low latency as S3 Standard, but with a lower storage cost and a retrieval fee2. Therefore, it is suitablefor data files that are accessed once or twice each month. S3 Standard-IA also provides high availability, as it stores data redundantly across multiple Availability Zones1.
Transfer objects to S3 Glacier Deep Archive after 2 years. S3 Glacier Deep Archive is the lowest-cost storage class that offers secure and durable storage for data that is rarely accessed and can tolerate a
12-hour retrieval time. It is ideal for long-term archiving and digital preservation3. Therefore, it is suitable for data files that are accessed only once or twice each year. S3 Glacier Deep Archive also provides high availability, as it stores data across at least three geographically dispersed Availability Zones1.
Delete objects when they are no longer needed. The data engineer can specify an expiration action in the S3 Lifecycle policy to delete objects after a certain period of time. This will reduce the storage cost and comply with any data retention policies.
Option C is the only solution that includes all these steps. Therefore, option C is the correct answer.
Option A is incorrect because it transitions objects to S3 One Zone-Infrequent Access (S3 One Zone-IA) after
6 months. S3 One Zone-IA is similar to S3 Standard-IA, but it stores data in a single Availability Zone. This means it has a lower availability and durability than S3 Standard-IA, and it is not resilient to the loss of data in a single Availability Zone1. Therefore, it does not provide high availability as required.
Option B is incorrect because it transfers objects to S3 Glacier Flexible Retrieval after 2 years. S3 Glacier Flexible Retrieval is a storage class that offers secure and durable storage for data that is accessed infrequently and can tolerate a retrieval time of minutes to hours. It is more expensive than S3 Glacier Deep Archive, and it is not suitable for data that is accessed only once or twice each year3. Therefore, it is not the most cost-effective option.
Option D is incorrect because it combines the errors of option A and B. It transitions objects to S3 One Zone-IA after 6 months, which does not provide high availability, and it transfers objects to S3 Glacier Flexible Retrieval after 2 years, which is not the most cost-effective option.
References:
1: Amazon S3 storage classes - Amazon Simple Storage Service
2: Amazon S3 Standard-Infrequent Access (S3 Standard-IA) - Amazon Simple Storage Service
3: Amazon S3 Glacier and S3 Glacier Deep Archive - Amazon Simple Storage Service
[4]: Expiring objects - Amazon Simple Storage Service
[5]: Managing your storage lifecycle - Amazon Simple Storage Service
[6]: Examples of S3 Lifecycle configuration - Amazon Simple Storage Service
[7]: Amazon S3 Lifecycle further optimizes storage cost savings with new features - What's New with AWS


NEW QUESTION # 25
A manufacturing company wants to collect data from sensors. A data engineer needs to implement a solution that ingests sensor data in near real time.
The solution must store the data to a persistent data store. The solution must store the data in nested JSON format. The company must have the ability to query from the data store with a latency of less than 10 milliseconds.
Which solution will meet these requirements with the LEAST operational overhead?

  • A. Use Amazon Simple Queue Service(Amazon SQS) to buffer incomingsensor data. Use AWS Glue to store thedata in Amazon RDS for querying.
  • B. Use AWS Lambda to process the sensor data. Store the data in Amazon S3 for querying.
  • C. Use a self-hosted Apache Kafka cluster to capture the sensor data. Store the data in Amazon S3 for querying.
  • D. Use Amazon Kinesis Data Streams to capture the sensor data. Store the data in Amazon DynamoDB for querying.

Answer: D

Explanation:
Amazon Kinesis Data Streams is a service that enables you to collect, process, and analyze streaming data in real time. You can use Kinesis Data Streams to capture sensor data from various sources, such as IoT devices, web applications, or mobile apps. You can create data streams that can scale up to handle any amount of data from thousands of producers. You can also use the Kinesis Client Library (KCL) or the Kinesis Data Streams API to write applications that process and analyze the data in the streams1.
Amazon DynamoDB is a fully managed NoSQL database service that provides fast and predictable performance with seamless scalability. You can use DynamoDB to store the sensor data in nested JSON format, as DynamoDB supports document data types, such as lists and maps. You can also use DynamoDB to query the data with a latency of less than 10 milliseconds, as DynamoDB offers single-digit millisecond performance for any scale of data. You can use the DynamoDB API or the AWS SDKs to perform queries on the data, such as using key-value lookups, scans, or queries2.
The solution that meets the requirements with the least operational overhead is to use Amazon Kinesis Data Streams to capture the sensor data and store the data in Amazon DynamoDB for querying. This solution has the following advantages:
It does not require you to provision, manage, or scale any servers, clusters, or queues, as Kinesis Data Streams and DynamoDB are fully managed services that handle all the infrastructure for you. This reduces the operational complexity and cost of running your solution.
It allows you to ingest sensor data in near real time, as Kinesis Data Streams can capture data records as they are produced and deliver them to your applications within seconds. You can also use Kinesis Data Firehose to load the data from the streams to DynamoDB automatically and continuously3.
It allows you to store the data in nested JSON format, as DynamoDB supports document data types, such as lists and maps. You can also use DynamoDB Streams to capturechanges in the data and trigger actions, such as sending notifications or updating other databases.
It allows you to query the data with a latency of less than 10 milliseconds, as DynamoDB offers single-digit millisecond performance for any scale of data. You can also use DynamoDB Accelerator (DAX) to improve the read performance by caching frequently accessed data.
Option A is incorrect because it suggests using a self-hosted Apache Kafka cluster to capture the sensor data and store the data in Amazon S3 for querying. This solution has the following disadvantages:
It requires you to provision, manage, and scale your own Kafka cluster, either on EC2 instances or on-premises servers. This increases the operational complexity and cost of running your solution.
It does not allow you to query the data with a latency of less than 10 milliseconds, as Amazon S3 is an object storage service that is not optimized for low-latency queries. You need to use another service, such as Amazon Athena or Amazon Redshift Spectrum, to query the data in S3, which may incur additional costs and latency.
Option B is incorrect because it suggests using AWS Lambda to process the sensor data and store the data in Amazon S3 for querying. This solution has the following disadvantages:
It does not allow you to ingest sensor data in near real time, as Lambda is a serverless compute service that runs code in response to events. You need to use another service, such as API Gateway or Kinesis Data Streams, to trigger Lambda functions with sensor data, which may add extra latency and complexity to your solution.
It does not allow you to query the data with a latency of less than 10 milliseconds, as Amazon S3 is an object storage service that is not optimized for low-latency queries. You need to use another service, such as Amazon Athena or Amazon Redshift Spectrum, to query the data in S3, which may incur additional costs and latency.
Option D is incorrect because it suggests using Amazon Simple Queue Service (Amazon SQS) to buffer incoming sensor data and use AWS Glue to store the data in Amazon RDS for querying. This solution has the following disadvantages:
It does not allow you to ingest sensor data in near real time, as Amazon SQS is a message queue service that delivers messages in a best-effort manner. You need to use another service, such as Lambda or EC2, to poll the messages from the queue and process them, which may add extra latency and complexity to your solution.
It does not allow you to store the data in nested JSON format, as Amazon RDS is a relational database service that supports structured data types, such as tables and columns. You need to use another service, such as AWS Glue, to transform the data from JSON to relational format, which may add extra cost and overhead to your solution.
References:
1: Amazon Kinesis Data Streams - Features
2: Amazon DynamoDB - Features
3: Loading Streaming Data into Amazon DynamoDB - Amazon Kinesis Data Firehose
[4]: Capturing Table Activity with DynamoDB Streams - Amazon DynamoDB
[5]: Amazon DynamoDB Accelerator (DAX) - Features
[6]: Amazon S3 - Features
[7]: AWS Lambda - Features
[8]: Amazon Simple Queue Service - Features
[9]: Amazon Relational Database Service - Features
[10]: Working with JSON in Amazon RDS - Amazon Relational Database Service
[11]: AWS Glue - Features


NEW QUESTION # 26
A company needs to build a data lake in AWS. The company must provide row-level data access and column-level data access to specific teams. The teams will access the data by using Amazon Athena, Amazon Redshift Spectrum, and Apache Hive from Amazon EMR.
Which solution will meet these requirements with the LEAST operational overhead?

  • A. Use Amazon Redshift for data lake storage. Use Redshift security policies to restrict data access byrows and columns. Provide data accessby usingApache Spark and Amazon Athena federated queries.
  • B. Use Amazon S3 for data lake storage. Use S3 access policies to restrict data access by rows and columns. Provide data access throughAmazon S3.
  • C. Use Amazon S3 for data lake storage. Use Apache Ranger through Amazon EMR to restrict data access byrows and columns. Providedata access by using Apache Pig.
  • D. UseAmazon S3 for data lake storage. Use AWS Lake Formation to restrict data access by rows and columns. Provide data access through AWS Lake Formation.

Answer: D

Explanation:
Option D is the best solution to meet the requirements with the least operational overhead because AWS Lake Formation is a fully managed service that simplifies the process of building, securing, and managing data lakes. AWS Lake Formation allows you to define granular data access policies at the row and column level for different users and groups. AWS Lake Formation also integrates with Amazon Athena, Amazon Redshift Spectrum, and Apache Hive on Amazon EMR, enabling these services to access the data in the data lake through AWS Lake Formation.
Option A is not a good solution because S3 access policies cannot restrict data access by rows and columns.
S3 access policies are based on the identity and permissions of the requester, the bucket and object ownership, and the object prefix and tags. S3 access policies cannot enforce fine-grained data access control at the row and column level.
Option B is not a good solution because it involves using Apache Ranger and Apache Pig, which are not fully managed services and require additional configuration and maintenance. Apache Ranger is a framework that provides centralized security administration for data stored in Hadoop clusters, such as Amazon EMR. Apache Ranger can enforce row-level and column-level access policies for Apache Hive tables. However, Apache Ranger is not a native AWS service and requires manual installation and configuration on Amazon EMR clusters. Apache Pig is a platform that allows you to analyze large data sets using a high-level scripting language called Pig Latin. Apache Pig can access data stored in Amazon S3 and process it using Apache Hive.
However,Apache Pig is not a native AWS service and requires manual installation and configuration on Amazon EMR clusters.
Option C is not a good solution because Amazon Redshift is not a suitable service for data lake storage.
Amazon Redshift is a fully managed data warehouse service that allows you to run complex analytical queries using standard SQL. Amazon Redshift can enforce row-level and column-level access policies for different users and groups. However, Amazon Redshift is not designed to store and process large volumes of unstructured or semi-structured data, which are typical characteristics of data lakes. Amazon Redshift is also more expensive and less scalable than Amazon S3 for data lake storage.
References:
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
What Is AWS Lake Formation? - AWS Lake Formation
Using AWS Lake Formation with Amazon Athena - AWS Lake Formation
Using AWS Lake Formation with Amazon Redshift Spectrum - AWS Lake Formation Using AWS Lake Formation with Apache Hive on Amazon EMR - AWS Lake Formation Using Bucket Policies and User Policies - Amazon Simple Storage Service Apache Ranger Apache Pig What Is Amazon Redshift? - Amazon Redshift


NEW QUESTION # 27
A data engineer maintains custom Python scripts that perform a data formatting process that many AWS Lambda functions use. When the data engineer needs to modify the Python scripts, the data engineer must manually update all the Lambda functions.
The data engineer requires a less manual way to update the Lambda functions.
Which solution will meet this requirement?

  • A. Store a pointer to the custom Python scripts in environment variables in a shared Amazon S3 bucket.
  • B. Package the custom Python scripts into Lambda layers. Apply the Lambda layers to the Lambda functions.
  • C. Assign the same alias to each Lambda function. Call reach Lambda function by specifying the function's alias.
  • D. Store a pointer to the custom Python scripts in the execution context object in a shared Amazon S3 bucket.

Answer: B

Explanation:
Lambda layers are a way to share code and dependencies across multiple Lambda functions. By packaging the custom Python scripts into Lambda layers, the data engineer can update the scripts in one place and have them automatically applied to all the Lambda functions that use the layer. This reduces the manual effort and ensures consistency across the Lambda functions. The other options are either not feasible or not efficient.
Storing a pointer to the custom Python scripts in the execution context object or in environment variables would require the Lambda functions to download the scripts from Amazon S3 every time they are invoked, which would increase latency and cost. Assigning the same alias to each Lambda function would not help with updating the Python scripts, as the alias only points to a specific version of the Lambda function code.
References:
AWS Lambda layers
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 3: Data Ingestion and Transformation, Section 3.4: AWS Lambda


NEW QUESTION # 28
A healthcare company uses Amazon Kinesis Data Streams to stream real-time health data from wearable devices, hospital equipment, and patient records.
A data engineer needs to find a solution to process the streaming data. The data engineer needs to store the data in an Amazon Redshift Serverless warehouse. The solution must support near real-time analytics of the streaming data and the previous day's data.
Which solution will meet these requirements with the LEAST operational overhead?

  • A. Load data into Amazon Kinesis Data Firehose. Load the data into Amazon Redshift.
  • B. Use the streaming ingestion feature of Amazon Redshift.
  • C. Use the Amazon Aurora zero-ETL integration with Amazon Redshift.
  • D. Load the data into Amazon S3. Use the COPY command to load the data into Amazon Redshift.

Answer: B

Explanation:
The streaming ingestion feature of Amazon Redshift enables you to ingest data from streaming sources, such as Amazon Kinesis Data Streams, into Amazon Redshift tables in near real-time. You can use the streaming ingestion feature to process the streaming data from the wearable devices, hospital equipment, and patient records. The streaming ingestion feature also supports incremental updates, which means you can append new data or update existing data in the Amazon Redshift tables. This way, you can store the data in an Amazon Redshift Serverless warehouse and support near real-time analytics of the streaming data and the previous day's data. This solution meets the requirements with the least operational overhead, as it does not require any additional services or components to ingest and process the streaming data. The other options are either not feasible or not optimal. Loading data into Amazon Kinesis Data Firehose and then into Amazon Redshift (option A) would introduce additional latency and cost, as well as require additional configuration and management. Loading data into Amazon S3 and then using the COPY command to load the data into Amazon Redshift (option C) would also introduce additional latency and cost, as well as require additional storage space and ETL logic. Using the Amazon Aurora zero-ETL integration with Amazon Redshift (option D) would not work, as it requires the data to be stored in Amazon Aurora first, which is not the case for the streaming data from the healthcare company. References:
Using streaming ingestion with Amazon Redshift
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 3: Data Ingestion and Transformation, Section 3.5: Amazon Redshift Streaming Ingestion


NEW QUESTION # 29
A retail company has a customer data hub in an Amazon S3 bucket. Employees from many countries use the data hub to support company-wide analytics. A governance team must ensure that the company's data analysts can access data only for customers who are within the same country as the analysts.
Which solution will meet these requirements with the LEAST operational effort?

  • A. Move the data to AWS Regions that are close to the countries where the customers are. Provide access to each analyst based on the country that the analyst serves.
  • B. Load the data into Amazon Redshift. Create a view for each country. Create separate 1AM roles for each country to provide access to data from each country. Assign the appropriate roles to the analysts.
  • C. Register the S3 bucket as a data lake location in AWS Lake Formation. Use the Lake Formation row-level security features to enforce the company's access policies.
  • D. Create a separate table for each country's customer data. Provide access to each analyst based on the country that the analyst serves.

Answer: C

Explanation:
AWS Lake Formation is a service that allows you to easily set up, secure, and manage data lakes. One of the features of Lake Formation is row-level security, which enables you to control access to specific rows or columns of data based on the identity or role of the user. This feature is useful for scenarios where you need to restrict access to sensitive or regulated data, such as customer data from different countries. By registering the S3 bucket as a data lake location in Lake Formation, you can use the Lake Formation console or APIs to define and apply row-level security policies to the data in the bucket. You can also use Lake Formation blueprints to automate the ingestion and transformation of data from various sources into the data lake. This solution requires the least operational effort compared to the other options, as it does not involve creating or moving data, or managing multiple tables, views, or roles. References:
AWS Lake Formation
Row-Level Security
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 4: Data Lakes and Data Warehouses, Section 4.2: AWS Lake Formation


NEW QUESTION # 30
A company is planning to use a provisioned Amazon EMR cluster that runs Apache Spark jobs to perform big data analysis. The company requires high reliability. A big data team must follow best practices for running cost-optimized and long-running workloads on Amazon EMR. The team must find a solution that will maintain the company's current level of performance.
Which combination of resources will meet these requirements MOST cost-effectively? (Choose two.)

  • A. Use Amazon S3 as a persistent data store.
  • B. Use Graviton instances for core nodes and task nodes.
  • C. Use Spot Instances for all primary nodes.
  • D. Use Hadoop Distributed File System (HDFS) as a persistent data store.
  • E. Use x86-based instances for core nodes and task nodes.

Answer: A,B

Explanation:
The best combination of resources to meet the requirements of high reliability, cost-optimization, and performance for running Apache Spark jobs on Amazon EMR is to use Amazon S3 as a persistent data store and Graviton instances for core nodes and task nodes.
Amazon S3 is a highly durable, scalable, and secure object storage service that can store any amount of data for a variety of use cases, including big data analytics1. Amazon S3 is a better choice than HDFS as a persistent data store for Amazon EMR, as it decouples the storage from the compute layer, allowing for more flexibility and cost-efficiency. Amazon S3 also supports data encryption, versioning, lifecycle management, and cross-region replication1. Amazon EMR integrates seamlessly with Amazon S3, using EMR File System (EMRFS) to access data stored in Amazon S3 buckets2. EMRFS also supports consistent view, which enables Amazon EMR to provide read-after-write consistency for Amazon S3 objects that are accessed through EMRFS2.
Graviton instances are powered by Arm-based AWS Graviton2 processors that deliver up to 40% better price performance over comparable current generation x86-based instances3. Graviton instances are ideal for running workloads that are CPU-bound, memory-bound, or network-bound, such as big data analytics, web servers, and open-source databases3. Graviton instances are compatible with Amazon EMR, and can beused for both core nodes and task nodes. Core nodes are responsible for running the data processing frameworks, such as Apache Spark, and storing data in HDFS or the local file system. Task nodes are optional nodes that can be added to a cluster to increase the processing power and throughput. By using Graviton instances for both core nodes and task nodes, you can achieve higher performance and lower cost than using x86-based instances.
Using Spot Instances for all primary nodes is not a good option, as it can compromise the reliability and availability of the cluster. Spot Instances are spare EC2 instances that are available at up to 90% discount compared to On-Demand prices, but they can be interrupted by EC2 with a two-minute notice when EC2 needs the capacity back. Primary nodes are the nodes that run the cluster software, such as Hadoop, Spark, Hive, and Hue, and are essential for the cluster operation. If a primary node is interrupted by EC2, the cluster will fail or become unstable. Therefore, it is recommended to use On-Demand Instances or Reserved Instances for primary nodes, and use Spot Instances only for task nodes that can tolerate interruptions. References:
Amazon S3 - Cloud Object Storage
EMR File System (EMRFS)
AWS Graviton2 Processor-Powered Amazon EC2 Instances
[Plan and Configure EC2 Instances]
[Amazon EC2 Spot Instances]
[Best Practices for Amazon EMR]


NEW QUESTION # 31
A financial company wants to use Amazon Athena to run on-demand SQL queries on a petabyte-scale dataset to support a business intelligence (BI) application. An AWS Glue job that runs during non-business hours updates the dataset once every day. The BI application has a standard data refresh frequency of 1 hour to comply with company policies.
A data engineer wants to cost optimize the company's use of Amazon Athena without adding any additional infrastructure costs.
Which solution will meet these requirements with the LEAST operational overhead?

  • A. Change the format of the files that are in the dataset to Apache Parquet.
  • B. Add an Amazon ElastiCache cluster between the Bl application and Athena.
  • C. Use the query result reuse feature of Amazon Athena for the SQL queries.
  • D. Configure an Amazon S3 Lifecycle policy to move data to the S3 Glacier Deep Archive storage class after 1 day

Answer: C

Explanation:
The best solution to cost optimize the company's use of Amazon Athena without adding any additional infrastructure costs is to use the query result reuse feature of AmazonAthena for the SQL queries. This feature allows you to run the same query multiple times without incurring additional charges, as long as the underlying data has not changed and the query results are still in the query result location in Amazon S31. This feature is useful for scenarios where you have a petabyte-scale dataset that is updated infrequently, such as once a day, and you have a BI application that runs the same queries repeatedly, such as every hour. By using the query result reuse feature, you can reduce the amount of data scanned by your queries and save on the cost of running Athena. You can enable or disable this feature at the workgroup level or at the individual query level1.
Option A is not the best solution, as configuring an Amazon S3 Lifecycle policy to move data to the S3 Glacier Deep Archive storage class after 1 day would not cost optimize the company's use of Amazon Athena, but rather increase the cost and complexity. Amazon S3 Lifecycle policies are rules that you can define to automatically transition objects between different storage classes based on specified criteria, such as the age of the object2. S3 Glacier Deep Archive is the lowest-cost storage class in Amazon S3, designed for long-term data archiving that is accessed once or twice in a year3. While moving data to S3 Glacier Deep Archive can reduce the storage cost, it would also increase the retrieval cost and latency, as it takes up to 12 hours to restore the data from S3 Glacier Deep Archive3. Moreover, Athena does not support querying data that is in S3 Glacier or S3 Glacier Deep Archive storage classes4. Therefore, using this option would not meet the requirements of running on-demand SQL queries on the dataset.
Option C is not the best solution, as adding an Amazon ElastiCache cluster between the BI application and Athena would not cost optimize the company's use of Amazon Athena, but rather increase the cost and complexity. Amazon ElastiCache is a service that offers fully managed in-memory data stores, such as Redis and Memcached, that can improve the performance and scalability of web applications by caching frequently accessed data. While using ElastiCache can reduce the latency and load on the BI application, it would not reduce the amount of data scanned by Athena, which is the main factor that determines the cost of running Athena. Moreover, using ElastiCache would introduce additional infrastructure costs and operational overhead, as you would have to provision, manage, and scale the ElastiCache cluster, and integrate it with the BI application and Athena.
Option D is not the best solution, as changing the format of the files that are in the dataset to Apache Parquet would not cost optimize the company's use of Amazon Athena without adding any additional infrastructure costs, but rather increase the complexity. Apache Parquet is a columnar storage format that can improve the performance of analytical queries by reducing the amount of data that needs to be scanned and providing efficient compression and encoding schemes. However,changing the format of the files that are in the dataset to Apache Parquet would require additional processing and transformation steps, such as using AWS Glue or Amazon EMR to convert the files from their original format to Parquet, and storing the converted files in a separate location in Amazon S3. This would increase the complexity and the operational overhead of the data pipeline, and also incur additional costs for using AWS Glue or Amazon EMR. References:
Query result reuse
Amazon S3 Lifecycle
S3 Glacier Deep Archive
Storage classes supported by Athena
[What is Amazon ElastiCache?]
[Amazon Athena pricing]
[Columnar Storage Formats]
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide


NEW QUESTION # 32
A data engineer is building a data pipeline on AWS by using AWS Glue extract, transform, and load (ETL) jobs. The data engineer needs to process data from Amazon RDS and MongoDB, perform transformations, and load the transformed data into Amazon Redshift for analytics. The data updates must occur every hour.
Which combination of tasks will meet these requirements with the LEAST operational overhead? (Choose two.)

  • A. Use AWS Glue connections to establish connectivity between the data sources and Amazon Redshift.
  • B. Use AWS Lambda functions to schedule and run the ETL jobs even/ hour.
  • C. Use the Redshift Data API to load transformed data into Amazon Redshift.
  • D. Configure AWS Glue triggers to run the ETL jobs even/ hour.
  • E. Use AWS Glue DataBrewto clean and prepare the data for analytics.

Answer: A,D

Explanation:
The correct answer is to configure AWS Glue triggers to run the ETL jobs every hour and use AWS Glue connections to establish connectivity between the data sources and Amazon Redshift. AWS Glue triggers are a way to schedule and orchestrate ETL jobs with the least operational overhead. AWS Glue connections are a way to securely connect to data sources and targets using JDBC or MongoDB drivers. AWS Glue DataBrew is a visual data preparation tool that does not support MongoDB as a data source. AWS Lambda functions are a serverless option to schedule and run ETL jobs, but they have a limit of 15 minutes for execution time, which may not be enough for complex transformations. The Redshift Data API is a way to run SQL commands on Amazon Redshift clusters without needing a persistent connection, but it does not support loading data from AWS Glue ETL jobs. References:
AWS Glue triggers
AWS Glue connections
AWS Glue DataBrew
[AWS Lambda functions]
[Redshift Data API]


NEW QUESTION # 33
A company wants to implement real-time analytics capabilities. The company wants to use Amazon Kinesis Data Streams and Amazon Redshift to ingest and process streaming data at the rate of several gigabytes per second. The company wants to derive near real-time insights by using existing business intelligence (BI) and analytics tools.
Which solution will meet these requirements with the LEAST operational overhead?

  • A. Access the data from Kinesis Data Streams by using SQL queries. Create materialized views directly on top of the stream. Refresh the materialized views regularly to query the most recent stream data.
  • B. Connect Kinesis Data Streams to Amazon Kinesis Data Firehose. Use Kinesis Data Firehose to stage the data in Amazon S3. Use the COPY command to load the data from Amazon S3 to a table in Amazon Redshift.
  • C. Use Kinesis Data Streams to stage data in Amazon S3. Use the COPY command to load data from Amazon S3 directly into Amazon Redshift to make the data immediately available for real-time analysis.
  • D. Create an external schema in Amazon Redshift to map the data from Kinesis Data Streams to an Amazon Redshift object. Create a materialized view to read data from the stream. Set the materialized view to auto refresh.

Answer: D

Explanation:
This solution meets the requirements of implementing real-time analytics capabilities with the least operational overhead. By creating an external schema in Amazon Redshift, you can access the data from Kinesis Data Streams using SQL queries without having to load the data into the cluster. By creating a materialized view on top of the stream, you can store the results of the query in the cluster and make them available for analysis. By setting the materialized view to auto refresh, you can ensure that the view is updated with the latest data from the stream at regular intervals. This way, you can derive near real-time insights by using existing BI and analytics tools. References:
Amazon Redshift streaming ingestion
Creating an external schema for Amazon Kinesis Data Streams
Creating a materialized view for Amazon Kinesis Data Streams


NEW QUESTION # 34
A financial services company stores financial data in Amazon Redshift. A data engineer wants to run real-time queries on the financial data to support a web-based trading application. The data engineer wants to run the queries from within the trading application.
Which solution will meet these requirements with the LEAST operational overhead?

  • A. Use the Amazon Redshift Data API.
  • B. Store frequently accessed data in Amazon S3. Use Amazon S3 Select to run the queries.
  • C. Establish WebSocket connections to Amazon Redshift.
  • D. Set up Java Database Connectivity (JDBC) connections to Amazon Redshift.

Answer: A

Explanation:
The Amazon Redshift Data API is a built-in feature that allows you to run SQL queries on Amazon Redshift data with web services-based applications, such as AWS Lambda, Amazon SageMaker notebooks, and AWS Cloud9. The Data API does not require a persistent connection to your database, and it provides a secure HTTP endpoint and integration with AWS SDKs. You can use the endpoint to run SQL statements without managing connections. The Data API also supports both Amazon Redshift provisioned clusters and Redshift Serverless workgroups. The Data API is the best solution for running real-time queries on the financial data from within the trading application, as it has the least operational overhead compared to the other options.
Option A is not the best solution, as establishing WebSocket connections to Amazon Redshift would require more configuration and maintenance than using the Data API. WebSocket connections are also not supported by Amazon Redshift clusters or serverless workgroups.
Option C is not the best solution, as setting up JDBC connections to Amazon Redshift would also require more configuration and maintenance than using the Data API. JDBC connections are also not supported by Redshift Serverless workgroups.
Option D is not the best solution, as storing frequently accessed data in Amazon S3 and using Amazon S3 Select to run the queries would introduce additional latency and complexity than using the Data API. Amazon S3 Select is also not optimized for real-time queries, as it scans the entire object before returning the results.
References:
Using the Amazon Redshift Data API
Calling the Data API
Amazon Redshift Data API Reference
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide


NEW QUESTION # 35
A company is migrating its database servers from Amazon EC2 instances that run Microsoft SQL Server to Amazon RDS for Microsoft SQL Server DB instances. The company's analytics team must export large data elements every day until the migration is complete. The data elements are the result of SQL joinsacross multiple tables. The data must be in Apache Parquet format. The analytics team must store the data in Amazon S3.
Which solution will meet these requirements in the MOST operationally efficient way?

  • A. Use a SQL query to create a view in the EC2 instance-based SQL Server databases that contains the required data elements. Create and run an AWS Glue crawler to read the view. Create an AWS Glue job that retrieves the data and transfers the data in Parquet format to an S3 bucket. Schedule the AWS Glue job to run every day.
  • B. Create an AWS Lambda function that queries the EC2 instance-based databases by using Java Database Connectivity (JDBC). Configure the Lambda function to retrieve the required data, transform the data into Parquet format, and transfer the data into an S3 bucket. Use Amazon EventBridge to schedule the Lambda function to run every day.
  • C. Create a view in the EC2 instance-based SQL Server databases that contains the required data elements.
    Create an AWS Glue job that selects the data directly from the view and transfers the data in Parquet format to an S3 bucket. Schedule the AWS Glue job to run every day.
  • D. Schedule SQL Server Agent to run a daily SQL query that selects the desired data elements from the EC2 instance-based SQL Server databases. Configure the query to direct the output .csv objects to an S3 bucket. Create an S3 event that invokes an AWS Lambda function to transform the output format from
    .csv to Parquet.

Answer: C

Explanation:
Option A is the most operationally efficient way to meet the requirements because it minimizes the number of steps and services involved in the data export process. AWS Glue is a fully managed service that can extract, transform, and load (ETL) data from various sources to various destinations, including Amazon S3. AWS Glue can also convert data to different formats, such as Parquet, which is a columnar storage format that is optimized for analytics. By creating a view in the SQL Server databases that contains the required data elements, the AWS Glue job can select the data directly from the view without having to perform any joins or transformations on the source data. The AWS Glue job can then transfer the data in Parquet format to an S3 bucket and run on a daily schedule.
Option B is not operationally efficient because it involves multiple steps and services to export the data. SQL Server Agent is a tool that can run scheduled tasks on SQL Server databases, such as executing SQL queries.
However, SQL Server Agent cannot directly export data to S3, so the query output must be saved as .csv objects on the EC2 instance. Then, an S3 event must be configured to trigger an AWS Lambda function that can transform the .csv objects to Parquet format and upload them to S3. This option adds complexity and latency to the data export process and requires additional resources and configuration.
Option C is not operationally efficient because it introduces an unnecessary step of running an AWS Glue crawler to read the view. An AWS Glue crawler is a service that can scan data sources and create metadata tables in the AWS Glue Data Catalog. The Data Catalog is a central repository that stores information about the data sources, such as schema, format, and location. However, in this scenario, the schema and format of the data elements are already known and fixed, so there is no need to run a crawler to discover them. The AWS Glue job can directly select the data from the view without using the Data Catalog. Running a crawler adds extra time and cost to the data export process.
Option D is not operationally efficient because it requires custom code and configuration to query the databases and transform the data. An AWS Lambda function is a service that can run code in response to events or triggers, such as Amazon EventBridge. Amazon EventBridge is a service that can connect applications and services with event sources, such as schedules, and route them to targets, such as Lambda functions. However, in this scenario, using a Lambda function to query the databases and transform the data is not the best option because it requires writing and maintaining code that uses JDBC to connect to the SQL Server databases, retrieve the required data, convert the data to Parquet format, and transfer the data to S3.
This option also has limitations on the execution time, memory, and concurrency of the Lambda function, which may affect the performance and reliability of the data export process.
References:
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
AWS Glue Documentation
Working with Views in AWS Glue
Converting to Columnar Formats


NEW QUESTION # 36
A company has five offices in different AWS Regions. Each office has its own human resources (HR) department that uses a unique IAM role. The company stores employee records in a data lake that is based on Amazon S3 storage.
A data engineering team needs to limit access to the records. Each HR department should be able to access records for only employees who are within the HR department's Region.
Which combination of steps should the data engineering team take to meet this requirement with the LEAST operational overhead? (Choose two.)

  • A. Enable fine-grained access control in AWS Lake Formation. Add a data filter for each Region.
  • B. Register the S3 path as an AWS Lake Formation location.
  • C. Use data filters for each Region to register the S3 paths as data locations.
  • D. Modify the IAM roles of the HR departments to add a data filter for each department's Region.
  • E. Create a separate S3 bucket for each Region. Configure an IAM policy to allow S3 access. Restrict access based on Region.

Answer: A,B

Explanation:
AWS Lake Formation is a service that helps you build, secure, and manage data lakes on Amazon S3. You can use AWS Lake Formation to register the S3 path as a data lake location, and enable fine-grained access control to limit access to the records based on the HR department's Region. You can use data filters to specify which S3 prefixes or partitions each HR department can access, and grant permissions to the IAM roles of the HR departments accordingly. This solution will meet the requirement with the least operational overhead, as it simplifies the data lake management and security, and leverages the existing IAM roles of the HR departments12.
The other options are not optimal for the following reasons:
A: Use data filters for each Region to register the S3 paths as data locations. This option is not possible, as data filters are not used to register S3 paths as data locations, but to grant permissions to access specific S3 prefixes or partitions within a data location. Moreover, this option does not specify how to limit access to the records based on the HR department's Region.
C: Modify the IAM roles of the HR departments to add a data filter for each department's Region. This option is not possible, as data filters are not added to IAM roles, but to permissions granted by AWS Lake Formation. Moreover, this option does not specify how to register the S3 path as a data lake location, or how to enable fine-grained access control in AWS Lake Formation.
E: Create a separate S3 bucket for each Region. Configure an IAM policy to allow S3 access. Restrict access based on Region. This option is not recommended, as it would require more operational overhead to create and manage multiple S3 buckets, and to configure and maintain IAM policies for each HR department. Moreover, this option does not leverage the benefits of AWS Lake Formation, such as data cataloging, data transformation, and data governance.
References:
1: AWS Lake Formation
2: AWS Lake Formation Permissions
3: AWS Identity and Access Management
4: Amazon S3


NEW QUESTION # 37
A data engineer must orchestrate a data pipeline that consists of one AWS Lambda function and one AWS Glue job. The solution must integrate with AWS services.
Which solution will meet these requirements with the LEAST management overhead?

  • A. Use an Apache Airflow workflow that is deployed on an Amazon EC2 instance. Define a directed acyclic graph (DAG) in which the first task is to call the Lambda function and the second task is to call the AWS Glue job.
  • B. Use an Apache Airflow workflow that is deployed on Amazon Elastic Kubernetes Service (Amazon EKS). Define a directed acyclic graph (DAG) in which the first task is to call the Lambda function and the second task is to call the AWS Glue job.
  • C. Use an AWS Step Functions workflow that includes a state machine. Configure the state machine to run the Lambda function and then the AWS Glue job.
  • D. Use an AWS Glue workflow to run the Lambda function and then the AWS Glue job.

Answer: C

Explanation:
AWS Step Functions is a service that allows you to coordinate multiple AWS services into serverless workflows. You can use Step Functions to create state machines that define the sequence and logic of the tasks in your workflow. Step Functions supports various types of tasks, such as Lambda functions, AWS Glue jobs, Amazon EMR clusters, Amazon ECS tasks, etc. You can use Step Functions to monitor and troubleshoot your workflows, as well as to handle errors and retries.
Using an AWS Step Functions workflow that includes a state machine to run the Lambda function and then the AWS Glue job will meet the requirements with the least management overhead, as it leverages the serverless and managed capabilities of Step Functions. You do not need to write any code to orchestrate the tasks in your workflow, as you can use the Step Functions console or the AWS Serverless Application Model (AWS SAM) to define and deploy your state machine. You also do not need to provision or manage any servers or clusters, as Step Functions scales automatically based on the demand.
The other options are not as efficient as using an AWS Step Functions workflow. Using an Apache Airflow workflow that is deployed on an Amazon EC2 instance or on Amazon Elastic Kubernetes Service (Amazon EKS) will require more management overhead, as you will need to provision, configure, and maintain the EC2 instance or the EKS cluster, as well as the Airflow components. You will also need to write and maintain the Airflow DAGs to orchestrate the tasks in your workflow. Using an AWS Glue workflow to run the Lambda function and then the AWS Glue job will not work, as AWS Glue workflows only support AWS Glue jobs and crawlers as tasks, not Lambda functions. References:
AWS Step Functions
AWS Glue
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 6: Data Integration and Transformation, Section 6.3: AWS Step Functions


NEW QUESTION # 38
A data engineer needs Amazon Athena queries to finish faster. The data engineer notices that all the files the Athena queries use are currently stored in uncompressed .csv format. The data engineer also notices that users perform most queries by selecting a specific column.
Which solution will MOST speed up the Athena query performance?

  • A. Change the data format from .csvto JSON format. Apply Snappy compression.
  • B. Compress the .csv files by using gzjg compression.
  • C. Change the data format from .csvto Apache Parquet. Apply Snappy compression.
  • D. Compress the .csv files by using Snappy compression.

Answer: C

Explanation:
Amazon Athena is a serverless interactive query service that allows you to analyze data in Amazon S3 using standard SQL. Athena supports various data formats, such as CSV, JSON, ORC, Avro, and Parquet. However, not all data formats are equally efficient for querying. Some data formats, such as CSV and JSON, are row-oriented, meaning that they store data as a sequence of records, each with the same fields. Row-oriented formats are suitable for loading and exporting data, but they are not optimal for analytical queries that often access only a subset of columns. Row-oriented formats also do not support compression or encoding techniques that can reduce the data size and improve the query performance.
On the other hand, some data formats, such as ORC and Parquet, are column-oriented, meaning that they store data as a collection of columns, each with a specific data type. Column-oriented formats are ideal for analytical queries that often filter, aggregate, or join data by columns. Column-oriented formats also support compression and encoding techniques that can reduce the data size and improve the query performance. For example, Parquet supports dictionary encoding, which replaces repeated values with numeric codes, and run-length encoding, which replaces consecutive identical values with a single value and a count. Parquet also supports various compression algorithms, such as Snappy, GZIP, and ZSTD, that can further reduce the data size and improve the query performance.
Therefore, changing the data format from CSV to Parquet and applying Snappy compression will most speed up the Athena query performance. Parquet is a column-oriented format that allows Athena to scan only the relevant columns and skip the rest, reducing the amount of data read from S3. Snappy is a compression algorithm that reduces the data size without compromising the query speed, as it is splittable and does not require decompression before reading. This solution will also reduce the cost of Athena queries, as Athena charges based on the amount of data scanned from S3.
The other options are not as effective as changing the data format to Parquet and applying Snappy compression. Changing the data format from CSV to JSON and applying Snappy compression will not improve the query performance significantly, as JSON is also a row-oriented format that does not support columnar access or encoding techniques. Compressing the CSV files by using Snappy compression will reduce the data size, but it will not improve the query performance significantly, as CSV is still a row-oriented format that does not support columnar access or encoding techniques. Compressing the CSV files by using gzjg compression will reduce the data size, but it willdegrade the query performance, as gzjg is not a splittable compression algorithm and requires decompression before reading. References:
Amazon Athena
Choosing the Right Data Format
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 5: Data Analysis and Visualization, Section 5.1: Amazon Athena


NEW QUESTION # 39
A company extracts approximately 1 TB of data every day from data sources such as SAP HANA, Microsoft SQL Server, MongoDB, Apache Kafka, and Amazon DynamoDB. Some of the data sources have undefined data schemas or data schemas that change.
A data engineer must implement a solution that can detect the schema for these data sources. The solution must extract, transform, and load the data to an Amazon S3 bucket. The company has a service level agreement (SLA) to load the data into the S3 bucket within 15 minutes of data creation.
Which solution will meet these requirements with the LEAST operational overhead?

  • A. Use Amazon EMR to detect the schema and to extract, transform, and load the data into the S3 bucket.
    Create a pipeline in Apache Spark.
  • B. Create a stored procedure in Amazon Redshift to detect the schema and to extract, transform, and load the data into a Redshift Spectrum table. Access the table from Amazon S3.
  • C. Use AWS Glue to detect the schema and to extract, transform, and load the data into the S3 bucket.
    Create a pipeline in Apache Spark.
  • D. Create a PvSpark proqram in AWS Lambda to extract, transform, and load the data into the S3 bucket.

Answer: C

Explanation:
AWS Glue is a fully managed service that provides a serverless data integration platform. It can automatically discover and categorize data from various sources, including SAP HANA, Microsoft SQL Server, MongoDB, Apache Kafka, and Amazon DynamoDB. It can also infer the schema of the data and store it in the AWS Glue Data Catalog, which is a central metadata repository. AWS Glue can then use the schema information to generate and run Apache Spark code to extract, transform, and load the data into an Amazon S3 bucket. AWS Glue can also monitor and optimize the performance and cost of the data pipeline, and handle any schema changes that may occur in the source data. AWS Glue can meet the SLA of loading the data into the S3 bucket within 15 minutes of data creation, as it can trigger the data pipeline based on events, schedules, or on-demand. AWS Glue has the least operational overhead among the options, as it does not require provisioning, configuring, or managing any servers or clusters. It also handles scaling, patching, and security automatically. References:
AWS Glue
[AWS Glue Data Catalog]
[AWS Glue Developer Guide]
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide


NEW QUESTION # 40
A company uses Amazon Athena for one-time queries against data that is in Amazon S3. The company has several use cases. The company must implement permission controls to separate query processes and access to query history among users, teams, and applications that are in the same AWS account.
Which solution will meet these requirements?

  • A. Create an S3 bucket for each use case. Create an S3 bucket policy that grants permissions to appropriate individual IAM users. Apply the S3 bucket policy to the S3 bucket.
  • B. Create an Athena workgroup for each use case. Apply tags to the workgroup. Create an 1AM policy that uses the tags to apply appropriate permissions to the workgroup.
  • C. Create an JAM role for each use case. Assign appropriate permissions to the role for each use case.
    Associate the role with Athena.
  • D. Create an AWS Glue Data Catalog resource policy that grants permissions to appropriate individual IAM users for each use case. Apply the resource policy to the specific tables that Athena uses.

Answer: B

Explanation:
Athena workgroups are a way to isolate query execution and query history among users, teams, and applications that share the same AWS account. By creating a workgroup for each use case, the company can control the access and actions on the workgroup resource using resource-level IAM permissions or identity-based IAM policies. The company can also use tags to organize and identify the workgroups, and use them as conditions in the IAM policies to grant or deny permissions to the workgroup. This solution meets the requirements of separating query processes and access to query history among users, teams, and applications that are in the same AWS account. References:
Athena Workgroups
IAM policies for accessing workgroups
Workgroup example policies


NEW QUESTION # 41
A company stores data from an application in an Amazon DynamoDB table that operates in provisioned capacity mode. The workloads of the application have predictable throughput load on a regular schedule.
Every Monday, there is an immediate increase in activity early in the morning. The application has very low usage during weekends.
The company must ensure that the application performs consistently during peak usage times.
Which solution will meet these requirements in the MOST cost-effective way?

  • A. Use AWS Application Auto Scaling to schedule higher provisioned capacity for peak usage times.
    Schedule lower capacity during off-peak times.
  • B. Increase the provisioned capacity to the maximum capacity that is currently present during peak load times.
  • C. Divide the table into two tables. Provision each table with half of the provisioned capacity of the original table. Spread queries evenly across both tables.
  • D. Change the capacity mode from provisioned to on-demand. Configure the table to scale up and scale down based on the load on the table.

Answer: A

Explanation:
Amazon DynamoDB is a fully managed NoSQL database service that provides fast and predictable performance with seamless scalability. DynamoDB offers two capacity modes for throughput capacity:
provisioned and on-demand. In provisioned capacity mode, you specify the number of read and write capacity units per second that you expect your application to require. DynamoDB reserves the resources to meet your throughput needs with consistent performance. In on-demand capacity mode, you pay per request and DynamoDB scales the resources up and down automatically based on the actual workload. On-demand capacity mode is suitable for unpredictable workloads that can vary significantly over time1.
The solution that meets the requirements in the most cost-effective way is to use AWS Application Auto Scaling to schedule higher provisioned capacity for peak usage times and lower capacity during off-peak times. This solution has the following advantages:
It allows you to optimize the cost and performance of your DynamoDB table by adjusting the provisioned capacity according to your predictable workload patterns. You can use scheduled scaling to specify the date and time for the scaling actions, and the new minimum and maximum capacity limits. For example, you can schedule higher capacity for every Monday morning and lower capacity for weekends2.
It enables you to take advantage of the lower cost per unit of provisioned capacity mode compared to on-demand capacity mode. Provisioned capacity mode charges a flat hourly rate for the capacity you reserve, regardless of how much you use. On-demand capacity mode charges for each read and write request you consume, with nominimum capacity required. For predictable workloads, provisioned capacity mode can be more cost-effective than on-demand capacity mode1.
It ensures that your application performs consistently during peak usage times by having enough capacity to handle the increased load. You can also use auto scaling to automatically adjust the provisioned capacity based on the actual utilization of your table, and set a target utilization percentage for your table or global secondary index. This way, you can avoid under-provisioning or over-provisioning your table2.
Option A is incorrect because it suggests increasing the provisioned capacity to the maximum capacity that is currently present during peak load times. This solution has the following disadvantages:
It wastes money by paying for unused capacity during off-peak times. If you provision the same high capacity for all times, regardless of the actual workload, you are over-provisioning your table and paying for resources that you don't need1.
It does not account for possible changes in the workload patterns over time. If your peak load times increase or decrease in the future, you may need to manually adjust the provisioned capacity to match the new demand. This adds operational overhead and complexity to your application2.
Option B is incorrect because it suggests dividing the table into two tables and provisioning each table with half of the provisioned capacity of the original table. This solution has the following disadvantages:
It complicates the data model and the application logic by splitting the data into two separate tables. You need to ensure that the queries are evenly distributed across both tables, and that the data is consistent and synchronized between them. This adds extra development and maintenance effort to your application3.
It does not solve the problem of adjusting the provisioned capacity according to the workload patterns.
You still need to manually or automatically scale the capacity of each table based on the actual utilization and demand. This may result in under-provisioning or over-provisioning your tables2.
Option D is incorrect because it suggests changing the capacity mode from provisioned to on-demand. This solution has the following disadvantages:
It may incur higher costs than provisioned capacity mode for predictable workloads. On-demand capacity mode charges for each read and write request you consume, with no minimum capacity required. For predictable workloads, provisioned capacity mode can be more cost-effective than on-demand capacity mode, as you can reserve the capacity you need at a lower rate1.
It may not provide consistent performance during peak usage times, as on-demand capacity mode may take some time to scale up the resources to meet the sudden increase in demand. On-demand capacity mode uses adaptive capacity to handle bursts of traffic, but it may not be able to handle very large spikes or sustained high throughput. In such cases, you may experience throttling or increased latency.
References:
1: Choosing the right DynamoDB capacity mode - Amazon DynamoDB
2: Managing throughput capacity automatically with DynamoDB auto scaling - Amazon DynamoDB
3: Best practices for designing and using partition keys effectively - Amazon DynamoDB
[4]: On-demand mode guidelines - Amazon DynamoDB
[5]: How to optimize Amazon DynamoDB costs - AWS Database Blog
[6]: DynamoDB adaptive capacity: How it works and how it helps - AWS Database Blog
[7]: Amazon DynamoDB pricing - Amazon Web Services (AWS)


NEW QUESTION # 42
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