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Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Developing Code for Data Processing using Python and SQL | ~22% | - Manage dependencies, libraries, and UDFs - Implement scalable Python/SQL code and project structures - Build pipelines with Lakeflow Spark Declarative Pipelines and Auto Loader |
| Topic 2: Monitoring, Logging, and Troubleshooting | ~8% | - Diagnose common pipeline and job failures - Use Spark UI, Query Profiler, and system tables |
| Topic 3: Data Modeling | ~10% | - Design scalable Delta Lake schemas and clustering - Apply dimensional modeling techniques |
| Topic 4: Data Transformation, Cleansing, and Quality | ~12% | - Apply advanced Spark transformations - Enforce data quality and quarantine bad data |
| Topic 5: Security and Governance | ~10% | - Manage Unity Catalog permissions and ACLs - Implement row-level security, column masking, and compliance |
| Topic 6: Cost and Performance Optimization | ~13% | - Optimize queries, clusters, and storage - Leverage system tables and observability tools |
| Topic 7: Streaming Workloads and Change Data Capture | ~11% | - Apply AUTO CDC APIs and exactly-once semantics - Implement reliable streaming pipelines |
| Topic 8: CI/CD, Testing, and Deployment | ~6% | - Deploy with Declarative Automation Bundles, CLI, and REST API - Implement testing and deployment pipelines |
| Topic 9: Data Sharing and Federation | ~8% | - Configure Delta Sharing and Lakehouse Federation |
Databricks Certified Data Engineer Professional Sample Questions:
1. Which of the following technologies can be used to identify key areas of text when parsing Spark Driver log4j output?
A) Julia
B) Scala Datasets
C) pyspsark.ml.feature
D) C++
E) Regex
2. The Databricks CLI is use to trigger a run of an existing job by passing the job_id parameter. The response that the job run request has been submitted successfully includes a filed run_id.
Which statement describes what the number alongside this field represents?
A) The job_id is returned in this field.
B) The globally unique ID of the newly triggered run.
C) The job_id and number of times the job has been are concatenated and returned.
D) The total number of jobs that have been run in the workspace.
E) The number of times the job definition has been run in the workspace.
3. A data engineer is working in an interactive notebook with many transformations before outputting the result from display(df.collect() ). The notebook includes wide transformations and a cross join.
The data engineer is getting the following error: "The spark driver has stopped unexpectedly and is restarting. Your notebook will be automatically reattached." Which action should the data engineer take?
A) Check into the Spark UI to see how many jobs are assigned to each stage as they are employing fewer executors.
B) Run the notebook on a single node cluster to keep driver from falling.
C) Look at the compute metrics UI to see if the executors have higher than 90% memory utilization.
D) Rewrite their code to avoid putting memory pressure on the driver node.
4. A data engineer deploys a multi-task Databricks job that orchestrates three notebooks. One task intermittently fails with Exit Code 1 but succeeds on retry. The engineer needs to collect detailed logs for the failing attempts, including stdout/stderr and cluster lifecycle context, and share them with the platform team. What steps the data engineer needs to follow using built-in tools?
A) Export the notebook run results to HTML; this bundle includes complete stdout, stderr, and cluster event history across all tasks.
B) Download worker logs directly from the Spark UI and ignore driver logs, as worker logs contain stdout/stderr for all tasks and cluster events.
C) Use the notebook interactive debugger to re-run the entire multi-task job, and capture step- through traces for the failing task.
D) From the job run details page, export the job's logs or configure log delivery; then retrieve the compute driver logs and event logs from the compute details page to correlate stdout/stderr with cluster events.
5. Two of the most common data locations on Databricks are the DBFS root storage and external object storage mounted with dbutils.fs.mount().
Which of the following statements is correct?
A) The DBFS root stores files in ephemeral block volumes attached to the driver, while mounted directories will always persist saved data to external storage between sessions.
B) The DBFS root is the most secure location to store data, because mounted storage volumes must have full public read and write permissions.
C) By default, both the DBFS root and mounted data sources are only accessible to workspace administrators.
D) Neither the DBFS root nor mounted storage can be accessed when using %sh in a Databricks notebook.
E) DBFS is a file system protocol that allows users to interact with files stored in object storage using syntax and guarantees similar to Unix file systems.
Solutions:
| Question # 1 Answer: E | Question # 2 Answer: B | Question # 3 Answer: D | Question # 4 Answer: D | Question # 5 Answer: E |
