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Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Data Transformation, Cleansing, and Quality | - Data Quality
|
| Topic 2: Data Governance | - Metadata and Discoverability
|
| Topic 3: Debugging and Deploying | - Debugging and Troubleshooting
|
| Topic 4: Monitoring and Alerting | - Alerting
|
| Topic 5: Developing Code for Data Processing using Python and SQL | - Building and Testing ETL Pipelines
|
| Topic 6: Ensuring Data Security and Compliance | - Data Security
|
| Topic 7: Data Sharing and Federation | - Lakehouse Federation
|
| Topic 8: Cost & Performance Optimisation | - Delta Optimization
|
| Topic 9: Data Modelling | - Dimensional Modelling
|
| Topic 10: Data Ingestion & Acquisition | - Design and implement data ingestion pipelines
|
Databricks Certified Data Engineer Professional Sample Questions:
Question 1
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. Size on Disk is> 0
B. Size on Disk is < Size in Memory
C. The number of Cached Partitions> the number of Spark Partitions
D. On Heap Memory Usage is within 75% of off Heap Memory usage
E. The RDD Block Name included the '' annotation signaling failure to cache
Question 2
A data engineer is testing a collection of mathematical functions, one of which calculates the area under a curve as described by another function.
assert(myIntegrate(lambda x: x*x, 0, 3) [0] == 9)
Which kind of the test does the above line exemplify?
A. Integration
B. Manual
C. End-to-end
D. functional
E. Unit
Question 3
A Delta Lake table was created with the below query:
Realizing that the original query had a typographical error, the below code was executed:
ALTER TABLE prod.sales_by_stor RENAME TO prod.sales_by_store
Which result will occur after running the second command?
A. The table reference in the metastore is updated and no data is changed.
B. A new Delta transaction log Is created for the renamed table.
C. The table name change is recorded in the Delta transaction log.
D. All related files and metadata are dropped and recreated in a single ACID transaction.
E. The table reference in the metastore is updated and all data files are moved.
Question 4
A company stores account transactions in a Delta Lake table. The company needs to apply frequent account-level correlations (e.g., UPDATE statements) but wants to avoid rewriting entire Parquet files for each change to reduce file churn and improve write performance. Which Delta Lake feature should they enable?
A. Enable change data feed on the Delta table
B. Enable automatic file compaction on writes
C. Partition the Delta table by account_id
D. Enable deletion vectors on the Delta table
Question 5
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 to reduce duration of MERGE operations
B. Databricks has autotuned to a smaller target file size based on the overall size of data in the table
C. Databricks has autotuned to a smaller target file size based on the amount of data in each partition
D. Z-order indices calculated on the table are preventing file compaction C Bloom filler indices calculated on the table are preventing file compaction
Solutions:
| Question 1 Answer: A | Question 2 Answer: E | Question 3 Answer: A | Question 4 Answer: D | Question 5 Answer: A |
