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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Delta Lake and Data Management | - Delta Lake transactions and ACID properties - Time travel and versioning - Schema evolution and enforcement |
| Topic 2: Data Modeling and Transformation | - Spark SQL transformations - Dimensional modeling concepts - Performance optimization techniques |
| Topic 3: Data Ingestion and Processing | - ETL pipeline design patterns - Structured Streaming fundamentals - Batch and streaming ingestion with Auto Loader |
| Topic 4: Databricks Lakehouse Platform Architecture | - Medallion architecture (Bronze, Silver, Gold) - Workspace and cluster architecture - Data governance concepts (Unity Catalog basics) |
| Topic 5: Production Pipelines and Orchestration | - Job scheduling and monitoring - Databricks Workflows - Error handling and recovery strategies |
Databricks Certified Data Engineer Professional Sample Questions:
1. Which statement regarding stream-static joins and static Delta tables is correct?
A) Each microbatch of a stream-static join will use the most recent version of the static Delta table as of the job's initialization.
B) Stream-static joins cannot use static Delta tables because of consistency issues.
C) The checkpoint directory will be used to track updates to the static Delta table.
D) The checkpoint directory will be used to track state information for the unique keys present in the join.
E) Each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch.
2. The data engineer is using Spark's MEMORY_ONLY storage level. Which indicators should the data engineer look for in the spark UI's Storage tab to signal that a cached table is not performing optimally?
A) On Heap Memory Usage is within 75% of off Heap Memory usage
B) The number of Cached Partitions> the number of Spark Partitions
C) Size on Disk is> 0
D) Size on Disk is < Size in Memory
E) The RDD Block Name included the '' annotation signaling failure to cache
3. A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on Task A.
If task A fails during a scheduled run, which statement describes the results of this run?
A) Tasks B and C will be skipped; some logic expressed in task A may have been committed before task failure.
B) Tasks B and C will be skipped; task A will not commit any changes because of stage failure.
C) Tasks B and C will attempt to run as configured; any changes made in task A will be rolled back due to task failure.
D) Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until all tasks have successfully been completed.
E) Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task A failed, all commits will be rolled back automatically.
4. A platform engineer needs to report the resource consumption, categorized by SKU tier, across all workspaces. The engineer decides to use the system.billing.usage system table to create a query. Which SQL query will accurately return the daily usage by product?
A)
B)
C)
D) 
5. A data engineer is designing an append-only pipeline that needs to handle both batch and streaming data in Delta Lake. The team wants to ensure that the streaming component can efficiently track which data has already been processed. Which configuration should be set to enable this?
A) overwriteSchema
B) mergeSchema
C) checkpointLocation
D) partitionBy
Solutions:
| Question # 1 Answer: E | Question # 2 Answer: C | Question # 3 Answer: A | Question # 4 Answer: A | Question # 5 Answer: C |
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