Databricks Certified-Data-Engineer-Professional : Databricks Certified Data Engineer Professional

  • Exam Code: Certified-Data-Engineer-Professional
  • Exam Name: Databricks Certified Data Engineer Professional
  • Updated: Aug 26, 2026
  • Q & A: 250 Questions and Answers

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Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:

SectionWeightObjectives
Data Modeling~10%- Apply dimensional modeling techniques
- Design scalable Delta Lake schemas and clustering
CI/CD, Testing, and Deployment~6%- Implement testing and deployment pipelines
- Deploy with Declarative Automation Bundles, CLI, and REST API
Security and Governance~10%- Implement row-level security, column masking, and compliance
- Manage Unity Catalog permissions and ACLs
Monitoring, Logging, and Troubleshooting~8%- Use Spark UI, Query Profiler, and system tables
- Diagnose common pipeline and job failures
Streaming Workloads and Change Data Capture~11%- Apply AUTO CDC APIs and exactly-once semantics
- Implement reliable streaming pipelines
Data Transformation, Cleansing, and Quality~12%- Apply advanced Spark transformations
- Enforce data quality and quarantine bad data
Data Sharing and Federation~8%- Configure Delta Sharing and Lakehouse Federation
Cost and Performance Optimization~13%- Optimize queries, clusters, and storage
- Leverage system tables and observability tools
Developing Code for Data Processing using Python and SQL~22%- Build pipelines with Lakeflow Spark Declarative Pipelines and Auto Loader
- Implement scalable Python/SQL code and project structures
- Manage dependencies, libraries, and UDFs

Databricks Certified Data Engineer Professional Sample Questions:

1. A task orchestrator has been configured to run two hourly tasks. First, an outside system writes Parquet data to a directory mounted at /mnt/raw_orders/. After this data is written, a Databricks job containing the following code is executed:

Assume that the fields customer_id and order_id serve as a composite key to uniquely identify each order, and that the time field indicates when the record was queued in the source system.
If the upstream system is known to occasionally enqueue duplicate entries for a single order hours apart, which statement is correct?

A) All records will be held in the state store for 2 hours before being deduplicated and committed to the orders table.
B) The orders table will contain only the most recent 2 hours of records and no duplicates will be present.
C) Duplicate records arriving more than 2 hours apart will be dropped, but duplicates that arrive in the same batch may both be written to the orders table.
D) The orders table will not contain duplicates, but records arriving more than 2 hours late will be ignored and missing from the table.
E) Duplicate records enqueued more than 2 hours apart may be retained and the orders table may contain duplicate records with the same customer_id and order_id.


2. Review the following error traceback:

Which statement describes the error being raised?

A) There is a type error because a column object cannot be multiplied.
B) There is a syntax error because the heartrate column is not correctly identified as a column.
C) There is a type error because a DataFrame object cannot be multiplied.
D) The code executed was PvSoark but was executed in a Scala notebook.
E) There is no column in the table named heartrateheartrateheartrate


3. A workspace admin has created a new catalog called finance_data and wants to delegate permission management to a finance team lead without giving them full admin rights. Which privilege should be granted to the finance team lead?

A) MANAGE privilege on the finance_data catalog.
B) ALL PRIVILEGES on the finance_data catalog.
C) GRANT OPTION privilege on the finance_data catalog.
D) Make the finance team lead a metastore admin.


4. A job runs four independent tasks (X, Y, Z, W) in parallel to process regional sales data. The Data Engineering team recently updated its cluster policy to ban cost-prohibitive instance types. Task Y now fails due to the newly enforced cluster policy restricting the use of a specific instance type.
A data engineer needs to resolve the failure quickly without disrupting the other tasks. How should the data engineer resolve the failure of tasks?

A) Use "Repair run", override the cluster configuration for Task Y to use a permitted instance type, and let Databricks re-run only Task Y.
B) Delete the failed run, disable the cluster policy, and re-execute all tasks.
C) Manually create a new cluster for Task Y, update the job configuration, and trigger a full re-run.
D) Edit the global cluster policy to allow the restricted instance type, then re-run the entire job.


5. A data engineering team is implementing an append-only data pipeline using Delta Lake, and wants to ensure that data is never modified or deleted once written. Which Delta Lake feature should the data engineer enable to prevent modifications to existing data?

A) Delta Time Travel
B) Delta VACUUM
C) Delta APPEND_ONLY
D) Delta OPTIMIZE


Solutions:

Question # 1
Answer: E
Question # 2
Answer: E
Question # 3
Answer: A
Question # 4
Answer: A
Question # 5
Answer: C

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