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Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Sharing and Federation | ~8% | - Configure Delta Sharing and Lakehouse Federation |
| Topic 2: Streaming Workloads and Change Data Capture | ~11% | - Implement reliable streaming pipelines - Apply AUTO CDC APIs and exactly-once semantics |
| Topic 3: Developing Code for Data Processing using Python and SQL | ~22% | - Implement scalable Python/SQL code and project structures - Build pipelines with Lakeflow Spark Declarative Pipelines and Auto Loader - Manage dependencies, libraries, and UDFs |
| Topic 4: Cost and Performance Optimization | ~13% | - Leverage system tables and observability tools - Optimize queries, clusters, and storage |
| Topic 5: Data Modeling | ~10% | - Design scalable Delta Lake schemas and clustering - Apply dimensional modeling techniques |
| Topic 6: Security and Governance | ~10% | - Manage Unity Catalog permissions and ACLs - Implement row-level security, column masking, and compliance |
| Topic 7: Monitoring, Logging, and Troubleshooting | ~8% | - Use Spark UI, Query Profiler, and system tables - Diagnose common pipeline and job failures |
| Topic 8: Data Transformation, Cleansing, and Quality | ~12% | - Apply advanced Spark transformations - Enforce data quality and quarantine bad data |
| Topic 9: CI/CD, Testing, and Deployment | ~6% | - Implement testing and deployment pipelines - Deploy with Declarative Automation Bundles, CLI, and REST API |
Databricks Certified Data Engineer Professional Sample Questions:
1. A data engineer is building a streaming data pipeline to ingest JSON files from cloud storage into a Delta Lake table. The pipeline must process files incrementally, handle schema evolution automatically, ensure exactly-once processing, and minimize manual infrastructure management.
How should the data engineer fulfill these requirements?
A) Use Auto Loader in batch mode with a daily job to overwrite the Delta table.
B) Use Lakeflow Spark Declarative Pipelines with a static DataFrame read, merge schema with spark.conf.set ("spark.databricks.delta.schema.autoMerge.enabled", "true")
C) Use Lakeflow Spart Declarative Pipelines with Auto Loader and enabling schema inference with
"cloudFiles.schemaEvolutionMode"= "addNewColumns"
D) Use traditional Spark Structured Streaming with Auto Loader, manually configuring checkpoints location and enabling schema inference with "mergeSchema"= "true"
2. A data engineer is developing a Lakeflow Declarative Pipeline (LDP) using a Databricks notebook directly connected to their pipeline. After adding new table definitions and transformation logic in their notebook, they want to check for any syntax errors in the pipeline code without actually processing data or running the pipeline. How should the data engineer perform this syntax check?
A) Disconnect the notebook from the pipeline and reconnect it to a compute cluster to access code validation features.
B) Open the web terminal from the notebook and run a shell command to validate the pipeline code.
C) Switch to a workspace file instead of a notebook to access validation and diagnostics tools.
D) Use the "Validate" option in the notebook to check for syntax errors.
3. A data engineer needs to productionize a new Spark application written by teammate. This application has numerous external dependencies, including libraries, and requires custom environment variables and Spark configuration parameters to be set. Which two methods will help the data engineer accomplish the task? (Choose two.)
A) Use secrets in init scripts to store configuration data
B) Create init scripts on DBFS.
C) Use compute policies to set system properties, environment variables, and Spark configuration parameters.
D) Add libraries to compute policies
E) Install libraries on DBFS
4. A data engineer is creating a daily reporting job. There are two reporting notebooks--one for weekdays and one for weekends. An "if/else condition" task is configured as
{{job.start_time.is_weekday}} == true to route the job to either the weekday or weekend notebook tasks. The same job would be used across multiple time zones. Which action should a senior data engineer take upon reviewing the job to merge or reject the pull request?
A) Reject, as the {{job.start_time.is_weekday}} is not a valid value reference.
B) Merge, as the job configuration looks good.
C) Reject, as they should use {{job.trigger_time.is_weekday}} instead.
D) Reject, as the {{job.start_time.is_weekday}} is for the UTC timezone.
5. The following code has been migrated to a Databricks notebook from a legacy workload:
The code executes successfully and provides the logically correct results, however, it takes over
20 minutes to extract and load around 1 GB of data.
Which statement is a possible explanation for this behavior?
A) Python will always execute slower than Scala on Databricks. The run.py script should be refactored to Scala.
B) %sh does not distribute file moving operations; the final line of code should be updated to use %fs instead.
C) Instead of cloning, the code should use %sh pip install so that the Python code can get executed in parallel across all nodes in a cluster.
D) %sh executes shell code on the driver node. The code does not take advantage of the worker nodes or Databricks optimized Spark.
E) %sh triggers a cluster restart to collect and install Git. Most of the latency is related to cluster startup time.
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: D | Question # 3 Answer: B,C | Question # 4 Answer: D | Question # 5 Answer: D |
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