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Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Streaming Workloads and Change Data Capture | ~11% | - Implement reliable streaming pipelines - Apply AUTO CDC APIs and exactly-once semantics |
| Topic 2: Data Sharing and Federation | ~8% | - Configure Delta Sharing and Lakehouse Federation |
| Topic 3: Developing Code for Data Processing using Python and SQL | ~22% | - Implement scalable Python/SQL code and project structures - Manage dependencies, libraries, and UDFs - Build pipelines with Lakeflow Spark Declarative Pipelines and Auto Loader |
| Topic 4: Cost and Performance Optimization | ~13% | - Optimize queries, clusters, and storage - Leverage system tables and observability tools |
| Topic 5: Security and Governance | ~10% | - Manage Unity Catalog permissions and ACLs - Implement row-level security, column masking, and compliance |
| Topic 6: Monitoring, Logging, and Troubleshooting | ~8% | - Use Spark UI, Query Profiler, and system tables - Diagnose common pipeline and job failures |
| Topic 7: CI/CD, Testing, and Deployment | ~6% | - Deploy with Declarative Automation Bundles, CLI, and REST API - Implement testing and deployment pipelines |
| Topic 8: Data Transformation, Cleansing, and Quality | ~12% | - Enforce data quality and quarantine bad data - Apply advanced Spark transformations |
| Topic 9: Data Modeling | ~10% | - Apply dimensional modeling techniques - Design scalable Delta Lake schemas and clustering |
Databricks Certified Data Engineer Professional Sample Questions:
The data governance team is reviewing code used for deleting records for compliance with GDPR. They note the following logic is used to delete records from the Delta Lake table named users.
Assuming that user_id is a unique identifying key and that delete_requests contains all users that have requested deletion, which statement describes whether successfully executing the above logic guarantees that the records to be deleted are no longer accessible and why?
- A. Yes; Delta Lake ACID guarantees provide assurance that the delete command succeeded fully and permanently purged these records.
- B. No; files containing deleted records may still be accessible with time travel until a vacuum command is used to remove invalidated data files.
- C. No; the Delta cache may return records from previous versions of the table until the cluster is restarted.
- D. No; the Delta Lake delete command only provides ACID guarantees when combined with the merge into command.
- E. Yes; the Delta cache immediately updates to reflect the latest data files recorded to disk.
Correct Answer: B 🗳️
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Which of the following is true of Delta Lake and the Lakehouse?
- A. Views in the Lakehouse maintain a valid cache of the most recent versions of source tables at all times.
- B. Because Parquet compresses data row by row. strings will only be compressed when a character is repeated multiple times.
- C. Delta Lake automatically collects statistics on the first 32 columns of each table which are leveraged in data skipping based on query filters.
- D. Z-order can only be applied to numeric values stored in Delta Lake tables
- E. Primary and foreign key constraints can be leveraged to ensure duplicate values are never entered into a dimension table.
Correct Answer: C 🗳️
Explanation: Only visible for Dumpexams members. You can sign-up / login (it's free).
A data engineering team has a time-consuming data ingestion job with three data sources. Each notebook takes about one hour to load new data. One day, the job fails because a notebook update introduced a new required configuration parameter. The team must quickly fix the issue and load the latest data from the failing source. Which action should the team take?
- A. Repair the run with the new parameter, and update the task by adding the missing task parameter.
- B. Repair the run with the new parameter.
- C. Update the task by adding the missing task parameter, and manually run the job.
- D. Share the analysis with the failing notebook owner so that they can fix it quickly.
Correct Answer: A 🗳️
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Given the following PySpark code snippet in a Databricks notebook:
filtered_df = spark.read.format("delta").load("/mnt/data/large_table")
\
.filter("event_date > '2024-01-01'")
filtered_df.count()
The data engineer notices from the Query Profiler that the scan operator for filtered_df is reading almost all files, despite the filter being applied.
What is the probable reason for poor data skipping?
- A. The filter condition involves a data type excluded from data skipping support.
- B. The event_date column is outside the table's partitioning and Z-ordering scheme.
- C. The filter is executed only after the full data scan, preventing data skipping.
- D. The Delta table lacks optimization that enables dynamic file pruning.
Correct Answer: B 🗳️
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The business reporting tem requires that data for their dashboards be updated every hour. The total processing time for the pipeline that extracts transforms and load the data for their pipeline runs in 10 minutes.
Assuming normal operating conditions, which configuration will meet their service-level agreement requirements with the lowest cost?
- A. Schedule a job to execute the pipeline once an hour on a new job cluster.
- B. Schedule a Structured Streaming job with a trigger interval of 60 minutes.
- C. Schedule a job to execute the pipeline once an hour on a dedicated interactive cluster.
- D. Configure a job that executes every time new data lands in a given directory.
Correct Answer: A 🗳️
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