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NEW QUESTION 145
You are designing an Azure Databricks cluster that runs user-defined local processes. You need to recommend a cluster configuration that meets the following requirements:
* Minimize query latency.
* Maximize the number of users that can run queues on the cluster at the same time for Reduce overall costs without compromising other requirements Which cluster type should you recommend?
- A. Standard with Auto termination
- B. High Concurrency with Autoscaling
- C. High Concurrency with Auto Termination
- D. Standard with Autoscaling
Answer: B
Explanation:
A High Concurrency cluster is a managed cloud resource. The key benefits of High Concurrency clusters are that they provide fine-grained sharing for maximum resource utilization and minimum query latencies.
Databricks chooses the appropriate number of workers required to run your job. This is referred to as autoscaling. Autoscaling makes it easier to achieve high cluster utilization, because you don't need to provision the cluster to match a workload.
Reference:
https://docs.microsoft.com/en-us/azure/databricks/clusters/configure
NEW QUESTION 146
A company purchases IoT devices to monitor manufacturing machinery. The company uses an IoT appliance to communicate with the IoT devices.
The company must be able to monitor the devices in real-time.
You need to design the solution.
What should you recommend?
- A. Azure Analysis Services using Azure Portal
- B. Azure Analysis Services using Azure PowerShell
- C. Azure Data Factory instance using Azure Portal
- D. Azure Stream Analytics cloud job using Azure PowerShell
Answer: D
Explanation:
Explanation
Stream Analytics is a cost-effective event processing engine that helps uncover real-time insights from devices, sensors, infrastructure, applications and data quickly and easily.
Monitor and manage Stream Analytics resources with Azure PowerShell cmdlets and powershell scripting that execute basic Stream Analytics tasks.
Reference:
https://cloudblogs.microsoft.com/sqlserver/2014/10/29/microsoft-adds-iot-streaming-analytics-data-production-
NEW QUESTION 147
You are implementing Azure Stream Analytics windowing functions.
Which windowing function should you use for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION 148
You are designing a highly available Azure Data Lake Storage solution that will include geo-zone-redundant storage (GZRS).
You need to monitor for replication delays that can affect the recovery point objective (RPO).
What should you include in the monitoring solution?
- A. availability
- B. Last Sync Time
- C. 5xx: Server Error errors
- D. Average Success E2E Latency
Answer: B
Explanation:
Because geo-replication is asynchronous, it is possible that data written to the primary region has not yet been written to the secondary region at the time an outage occurs. The Last Sync Time property indicates the last time that data from the primary region was written successfully to the secondary region. All writes made to the primary region before the last sync time are available to be read from the secondary location. Writes made to the primary region after the last sync time property may or may not be available for reads yet.
Reference:
https://docs.microsoft.com/en-us/azure/storage/common/last-sync-time-get
NEW QUESTION 149
You have an Azure Stream Analytics job that receives clickstream data from an Azure event hub.
You need to define a query in the Stream Analytics job. The query must meet the following requirements:
* Count the number of clicks within each 10-second window based on the country of a visitor.
* Ensure that each click is NOT counted more than once.
How should you define the Query?
- A. SELECT Country, Count(*) AS Count
FROM ClickStream TIMESTAMP BY CreatedAt
GROUP BY Country, TumblingWindow(second, 10) - B. SELECT Country, Avg(*) AS Average
FROM ClickStream TIMESTAMP BY CreatedAt
GROUP BY Country, HoppingWindow(second, 10, 2) - C. SELECT Country, Count(*) AS Count
FROM ClickStream TIMESTAMP BY CreatedAt
GROUP BY Country, SessionWindow(second, 5, 10) - D. SELECT Country, Avg(*) AS Average
FROM ClickStream TIMESTAMP BY CreatedAt
GROUP BY Country, SlidingWindow(second, 10)
Answer: A
Explanation:
Explanation
Tumbling window functions are used to segment a data stream into distinct time segments and perform a function against them, such as the example below. The key differentiators of a Tumbling window are that they repeat, do not overlap, and an event cannot belong to more than one tumbling window.
Example:
Reference:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-window-functions
NEW QUESTION 150
You have an enterprise data warehouse in Azure Synapse Analytics that contains a table named FactOnlineSales. The table contains data from the start of 2009 to the end of 2012.
You need to improve the performance of queries against FactOnlineSales by using table partitions. The solution must meet the following requirements:
Create four partitions based on the order date.
Ensure that each partition contains all the orders places during a given calendar year.
How should you complete the T-SQL command? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/sql/t-sql/statements/create-partition-function-transact-sql?view=sql-server-ver15
NEW QUESTION 151
You need to design the partitions for the product sales transactions. The solution must meet the sales transaction dataset requirements.
What should you include in the solution? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-overview-what-is
NEW QUESTION 152
You are designing a sales transactions table in an Azure Synapse Analytics dedicated SQL pool. The table will contains approximately 60 million rows per month and will be partitioned by month. The table will use a clustered column store index and round-robin distribution.
Approximately how many rows will there be for each combination of distribution and partition?
- A. 1 million
- B. 5 million
- C. 60 million
- D. 20 million
Answer: C
Explanation:
Explanation
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-tables-partition
NEW QUESTION 153
You are designing a real-time dashboard solution that will visualize streaming data from remote sensors that connect to the internet. The streaming data must be aggregated to show the average value of each 10-second interval. The data will be discarded after being displayed in the dashboard.
The solution will use Azure Stream Analytics and must meet the following requirements:
Minimize latency from an Azure Event hub to the dashboard.
Minimize the required storage.
Minimize development effort.
What should you include in the solution? To answer, select the appropriate options in the answer are a.
NOTE: Each correct selection is worth one point
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-power-bi-dashboard
NEW QUESTION 154
You have a SQL pool in Azure Synapse.
You plan to load data from Azure Blob storage to a staging table. Approximately 1 million rows of data will be loaded daily. The table will be truncated before each daily load.
You need to create the staging table. The solution must minimize how long it takes to load the data to the staging table.
How should you configure the table? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-tables-partition
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-tables-distribute
NEW QUESTION 155
You are designing an Azure Synapse Analytics dedicated SQL pool.
You need to ensure that you can audit access to Personally Identifiable information (PII).
What should you include in the solution?
- A. row-level security (RLS)
- B. column-level security
- C. sensitivity classifications
- D. dynamic data masking
Answer: B
NEW QUESTION 156
You are creating an Azure Data Factory data flow that will ingest data from a CSV file, cast columns to specified types of data, and insert the data into a table in an Azure Synapse Analytic dedicated SQL pool. The CSV file contains three columns named username, comment, and date.
The data flow already contains the following:
* A source transformation.
* A Derived Column transformation to set the appropriate types of data.
* A sink transformation to land the data in the pool.
You need to ensure that the data flow meets the following requirements:
* All valid rows must be written to the destination table.
* Truncation errors in the comment column must be avoided proactively.
* Any rows containing comment values that will cause truncation errors upon insert must be written to a file in blob storage.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
- A. To the data flow, add a sink transformation to write the rows to a file in blob storage.
- B. To the data flow, add a Conditional Split transformation to separate the rows that will cause truncation errors.
- C. To the data flow, add a filter transformation to filter out rows that will cause truncation errors.
- D. Add a select transformation to select only the rows that will cause truncation errors.
Answer: A,B
Explanation:
Explanation
B: Example:
1. This conditional split transformation defines the maximum length of "title" to be five. Any row that is less than or equal to five will go into the GoodRows stream. Any row that is larger than five will go into the BadRows stream.
2. This conditional split transformation defines the maximum length of "title" to be five. Any row that is less than or equal to five will go into the GoodRows stream. Any row that is larger than five will go into the BadRows stream.
A:
3. Now we need to log the rows that failed. Add a sink transformation to the BadRows stream for logging.
Here, we'll "auto-map" all of the fields so that we have logging of the complete transaction record. This is a text-delimited CSV file output to a single file in Blob Storage. We'll call the log file "badrows.csv".
4. The completed data flow is shown below. We are now able to split off error rows to avoid the SQL truncation errors and put those entries into a log file. Meanwhile, successful rows can continue to write to our target database.
Reference:
https://docs.microsoft.com/en-us/azure/data-factory/how-to-data-flow-error-rows
NEW QUESTION 157
You have an Apache Spark DataFrame named temperatures. A sample of the data is shown in the following table.
You need to produce the following table by using a Spark SQL query.
How should you complete the query? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Text Description automatically generated
Box 1: PIVOT
PIVOT rotates a table-valued expression by turning the unique values from one column in the expression into multiple columns in the output. And PIVOT runs aggregations where they're required on any remaining column values that are wanted in the final output.
Reference:
https://learnsql.com/cookbook/how-to-convert-an-integer-to-a-decimal-in-sql-server/
https://docs.microsoft.com/en-us/sql/t-sql/queries/from-using-pivot-and-unpivot
NEW QUESTION 158
You have a table in an Azure Synapse Analytics dedicated SQL pool. The table was created by using the following Transact-SQL statement.
You need to alter the table to meet the following requirements:
* Ensure that users can identify the current manager of employees.
* Support creating an employee reporting hierarchy for your entire company.
* Provide fast lookup of the managers' attributes such as name and job title.
Which column should you add to the table?
- A. [ManagerEmployeeKey] [int] NULL
- B. [ManagerEmployeeID] [int] NULL
- C. [ManagerName] [varchar](200) NULL
- D. [ManagerEmployeeID] [smallint] NULL
Answer: B
Explanation:
Explanation
Use the same definition as the EmployeeID column.
Reference:
https://docs.microsoft.com/en-us/analysis-services/tabular-models/hierarchies-ssas-tabular
NEW QUESTION 159
You are responsible for providing access to an Azure Data Lake Storage Gen2 account.
Your user account has contributor access to the storage account, and you have the application ID and access key.
You plan to use PolyBase to load data into an enterprise data warehouse in Azure Synapse Analytics.
You need to configure PolyBase to connect the data warehouse to storage account.
Which three components should you create in sequence? To answer, move the appropriate components from the list of components to the answer area and arrange them in the correct order.
Answer:
Explanation:
1 - a database scoped credential
2 - an external data source
3 - an external file format
NEW QUESTION 160
You are designing an Azure Synapse Analytics dedicated SQL pool.
You need to ensure that you can audit access to Personally Identifiable information (PII).
What should you include in the solution?
- A. column-level security
- B. sensitivity classifications
- C. row-level security (RLS)
- D. dynamic data masking
Answer: B
Explanation:
Data Discovery & Classification is built into Azure SQL Database, Azure SQL Managed Instance, and Azure Synapse Analytics. It provides basic capabilities for discovering, classifying, labeling, and reporting the sensitive data in your databases.
Your most sensitive data might include business, financial, healthcare, or personal information. Discovering and classifying this data can play a pivotal role in your organization's information-protection approach. It can serve as infrastructure for:
Helping to meet standards for data privacy and requirements for regulatory compliance.
Various security scenarios, such as monitoring (auditing) access to sensitive data.
Controlling access to and hardening the security of databases that contain highly sensitive data.
Reference:
https://docs.microsoft.com/en-us/azure/azure-sql/database/data-discovery-and-classification-overview
NEW QUESTION 161
You have an Azure Data Lake Storage Gen2 account that contains a JSON file for customers. The file contains two attributes named FirstName and LastName.
You need to copy the data from the JSON file to an Azure Synapse Analytics table by using Azure Databricks. A new column must be created that concatenates the FirstName and LastName values.
You create the following components:
A destination table in Azure Synapse
An Azure Blob storage container
A service principal
Which five actions should you perform in sequence next in is Databricks notebook? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/azure-databricks/databricks-extract-load-sql-data-warehouse
NEW QUESTION 162
You are designing a fact table named FactPurchase in an Azure Synapse Analytics dedicated SQL pool. The table contains purchases from suppliers for a retail store. FactPurchase will contain the following columns.
FactPurchase will have 1 million rows of data added daily and will contain three years of data.
Transact-SQL queries similar to the following query will be executed daily.
SELECT
SupplierKey, StockItemKey, COUNT(*)
FROM FactPurchase
WHERE DateKey >= 20210101
AND DateKey <= 20210131
GROUP By SupplierKey, StockItemKey
Which table distribution will minimize query times?
- A. replicated
- B. hash-distributed on DateKey
- C. round-robin
- D. hash-distributed on PurchaseKey
Answer: D
Explanation:
Explanation
Hash-distributed tables improve query performance on large fact tables, and are the focus of this article.
Round-robin tables are useful for improving loading speed.
Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-tables-distribu
NEW QUESTION 163
You have an Azure Storage account that generates 200,000 new files daily. The file names have a format of {YYYY}/{MM}/{DD}/{HH}/{CustomerID}.csv.
You need to design an Azure Data Factory solution that will load new data from the storage account to an Azure Data Lake once hourly. The solution must minimize load times and costs.
How should you configure the solution? To answer, select the appropriate options in the answer are a.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/stream-analytics-query/tumbling-window-azure-stream-analytics
NEW QUESTION 164
You have an Azure subscription that contains a logical Microsoft SQL server named Server1. Server1 hosts an Azure Synapse Analytics SQL dedicated pool named Pool1.
You need to recommend a Transparent Data Encryption (TDE) solution for Server1. The solution must meet the following requirements:
Track the usage of encryption keys.
Maintain the access of client apps to Pool1 in the event of an Azure datacenter outage that affects the availability of the encryption keys.
What should you include in the recommendation? To answer, select the appropriate options in the answer are a.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/security/workspaces-encryption
https://docs.microsoft.com/en-us/azure/key-vault/general/logging
NEW QUESTION 165
You have an Azure Synapse Analytics workspace named WS1.
You have an Azure Data Lake Storage Gen2 container that contains JSON-formatted files in the following format.
You need to use the serverless SQL pool in WS1 to read the files.
How should you complete the Transact-SQL statement? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Graphical user interface, text, application, email Description automatically generated
Box 1: openrowset
The easiest way to see to the content of your CSV file is to provide file URL to OPENROWSET function, specify csv FORMAT.
Example:
SELECT *
FROM OPENROWSET(
BULK 'csv/population/population.csv',
DATA_SOURCE = 'SqlOnDemandDemo',
FORMAT = 'CSV', PARSER_VERSION = '2.0',
FIELDTERMINATOR =',',
ROWTERMINATOR = '\n'
Box 2: openjson
You can access your JSON files from the Azure File Storage share by using the mapped drive, as shown in the following example:
SELECT book.* FROM
OPENROWSET(BULK N't:\books\books.json', SINGLE_CLOB) AS json
CROSS APPLY OPENJSON(BulkColumn)
WITH( id nvarchar(100), name nvarchar(100), price float,
pages_i int, author nvarchar(100)) AS book
Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/query-single-csv-file
https://docs.microsoft.com/en-us/sql/relational-databases/json/import-json-documents-into-sql-server
NEW QUESTION 166
You need to implement an Azure Synapse Analytics database object for storing the sales transactions data.
The solution must meet the sales transaction dataset requirements.
What solution must meet the sales transaction dataset requirements.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION 167
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