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Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Modeling | ~10% | - Design scalable Delta Lake schemas and clustering - Apply dimensional modeling techniques |
| Cost and Performance Optimization | ~13% | - Leverage system tables and observability tools - Optimize queries, clusters, and storage |
| Security and Governance | ~10% | - Implement row-level security, column masking, and compliance - Manage Unity Catalog permissions and ACLs |
| Developing Code for Data Processing using Python and SQL | ~22% | - Implement scalable Python/SQL code and project structures - Build pipelines with Lakeflow Spark Declarative Pipelines and Auto Loader - Manage dependencies, libraries, and UDFs |
| Streaming Workloads and Change Data Capture | ~11% | - Apply AUTO CDC APIs and exactly-once semantics - Implement reliable streaming pipelines |
| Data Sharing and Federation | ~8% | - Configure Delta Sharing and Lakehouse Federation |
| Monitoring, Logging, and Troubleshooting | ~8% | - Diagnose common pipeline and job failures - Use Spark UI, Query Profiler, and system tables |
| CI/CD, Testing, and Deployment | ~6% | - Implement testing and deployment pipelines - Deploy with Declarative Automation Bundles, CLI, and REST API |
| Data Transformation, Cleansing, and Quality | ~12% | - Enforce data quality and quarantine bad data - Apply advanced Spark transformations |
Databricks Certified Data Engineer Professional Sample Questions:
When scheduling Structured Streaming jobs for production, which configuration automatically recovers from query failures and keeps costs low?
- A. Cluster: Existing All-Purpose Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: 1 - B. Cluster: New Job Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: Unlimited - C. Cluster: New Job Cluster;
Retries: None;
Maximum Concurrent Runs: 1 - D. Cluster: Existing All-Purpose Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: 1 - E. Cluster: Existing All-Purpose Cluster;
Retries: None;
Maximum Concurrent Runs: 1
Correct Answer: D 🗳️
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A production cluster has 3 executor nodes and uses the same virtual machine type for the driver and executor.
When evaluating the Ganglia Metrics for this cluster, which indicator would signal a bottleneck caused by code executing on the driver?
- A. Network I/O never spikes
- B. Total Disk Space remains constant
- C. Bytes Received never exceeds 80 million bytes per second
- D. Overall cluster CPU utilization is around 25%
- E. The five Minute Load Average remains consistent/flat
Correct Answer: D 🗳️
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A table named user_ltv is being used to create a view that will be used by data analysts on various teams. Users in the workspace are configured into groups, which are used for setting up data access using ACLs.
The user_ltv table has the following schema:
email STRING, age INT, ltv INT
The following view definition is executed:
An analyst who is not a member of the auditing group executes the following query:
SELECT * FROM user_ltv_no_minors
Which statement describes the results returned by this query?
- A. All age values less than 18 will be returned as null values all other columns will be returned with the values in user_ltv.
- B. All records from all columns will be displayed with the values in user_ltv.
- C. All values for the age column will be returned as null values, all other columns will be returned with the values in user_ltv.
- D. All columns will be displayed normally for those records that have an age greater than 18; records not meeting this condition will be omitted.
- E. All columns will be displayed normally for those records that have an age greater than 17; records not meeting this condition will be omitted.
Correct Answer: D 🗳️
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A production workload incrementally applies updates from an external Change Data Capture feed to a Delta Lake table as an always-on Structured Stream job. When data was initially migrated for this table, OPTIMIZE was executed and most data files were resized to 1 GB. Auto Optimize and Auto Compaction were both turned on for the streaming production job. Recent review of data files shows that most data files are under 64 MB, although each partition in the table contains at least 1 GB of data and the total table size is over 10 TB.
Which of the following likely explains these smaller file sizes?
- A. Databricks has autotuned to a smaller target file size based on the overall size of data in the table
- B. Databricks has autotuned to a smaller target file size to reduce duration of MERGE operations
- C. Z-order indices calculated on the table are preventing file compaction C Bloom filler indices calculated on the table are preventing file compaction
- D. Databricks has autotuned to a smaller target file size based on the amount of data in each partition
Correct Answer: B 🗳️
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A nightly job ingests data into a Delta Lake table using the following code:
The next step in the pipeline requires a function that returns an object that can be used to manipulate new records that have not yet been processed to the next table in the pipeline.
Which code snippet completes this function definition?
def new_records():
- A. return spark.readStream.load("bronze")
- B. return spark.read.option("readChangeFeed", "true").table ("bronze")
- C.
- D.
- E. return spark.readStream.table("bronze")
Correct Answer: C 🗳️
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