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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Modeling and Storage | 20% | - Storage Optimization - Data Modeling - File Formats |
| Topic 2: Data Processing | 28% | - Spark SQL - Structured Streaming - ETL Pipelines - Data Transformation |
| Topic 3: Monitoring and Troubleshooting | 16% | - Troubleshooting - Performance Optimization - Monitoring |
| Topic 4: Databricks Lakehouse Platform | 24% | - Data Management - Unity Catalog - Delta Lake - Lakehouse Architecture |
| Topic 5: Data Quality and Governance | 12% | - Data Quality - Data Lineage - Governance |
Databricks Certified Data Engineer Professional Sample Questions:
Why are Pandas UDFs often preferred over traditional PySpark UDFs in performance-critical applications involving large datasets?
- A. They eliminate the JVM-Python boundary by bypassing serialization entirely, thereby avoiding data conversion overhead.
- B. They allow row-level execution of functions in Python with native Spark optimization, removing the need for columnar execution.
- C. They leverage Apache Arrow to enable vectorized operations between the JVM and Python runtimes, reducing serialization costs and improving computational efficiency.
- D. They minimize memory usage by streaming each row individually through a lightweight Python wrapper, avoiding batch processing overhead.
Correct Answer: C 🗳️
Explanation: Only visible for TorrentExam members. You can sign-up / login (it's free).
A data engineer needs to install the PyYAML Python package within an air-gapped Databricks environment. The workspace has no direct internet access to PyPI. The engineer has downloaded the .whl file locally and wants it available automatically on all new clusters. Which approach should the data engineer use?
- A. Upload the PyYAML .whl file to a Unity Catalog Volume, ensure it's allow-listed, and create a cluster-scoped init script that installs it from that path.
- B. Upload the PyYAML .whl file to the user home directory and create a cluster-scoped init script to install it.
- C. Add the .whl file to Databricks Git Repos and assume automatic installation.
- D. Set up a private PyPI repository and install via pip index URL.
Correct Answer: A 🗳️
Explanation: Only visible for TorrentExam members. You can sign-up / login (it's free).
A new data engineer notices that a critical field was omitted from an application that writes its Kafka source to Delta Lake. This happened even though the critical field was in the Kafka source.
That field was further missing from data written to dependent, long-term storage. The retention threshold on the Kafka service is seven days. The pipeline has been in production for three months.
Which describes how Delta Lake can help to avoid data loss of this nature in the future?
- A. The Delta log and Structured Streaming checkpoints record the full history of the Kafka producer.
- B. Delta Lake automatically checks that all fields present in the source data are included in the ingestion layer.
- C. Delta Lake schema evolution can retroactively calculate the correct value for newly added fields, as long as the data was in the original source.
- D. Data can never be permanently dropped or deleted from Delta Lake, so data loss is not possible under any circumstance.
- E. Ingestine all raw data and metadata from Kafka to a bronze Delta table creates a permanent, replayable history of the data state.
Correct Answer: E 🗳️
Explanation: Only visible for TorrentExam members. You can sign-up / login (it's free).
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. End-to-end
- B. Manual
- C. Integration
- D. Unit
- E. functional
Correct Answer: D 🗳️
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A data engineer has created a transactions Delta table on Databricks that should be used by the analytics team. The analytics team wants to use the table with another tool that requires Apache Iceberg format. What should the data engineer do?
- A. Create an Iceberg copy of the transactions Delta table which can be used by the analytics team.
- B. Enable uniform on the transactions table to 'iceberg' so that the table can be read as an Iceberg table.
- C. Convert the transactions Delta table to Iceberg and enable uniform so that the table can be read as a Delta table.
- D. Require the analytics team to use a tool that supports Delta table.
Correct Answer: C 🗳️
Explanation: Only visible for TorrentExam members. You can sign-up / login (it's free).
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