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- Exam Code: Databricks-Certified-Data-Engineer-Professional
- Exam Name: Databricks Certified Data Engineer Professional Exam
- Updated: Oct 05, 2026
- Q & A: 250 Questions and Answers
- PDF Price: $59.99
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- Exam Code: Databricks-Certified-Data-Engineer-Professional
- Exam Name: Databricks Certified Data Engineer Professional Exam
- Updated: Oct 05, 2026
- Q & A: 250 Questions and Answers
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- Exam Code: Databricks-Certified-Data-Engineer-Professional
- Exam Name: Databricks Certified Data Engineer Professional Exam
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- Q & A: 250 Questions and Answers
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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Ingestion & Acquisition | 7% | - Use Auto Loader and structured streaming - Handle incremental and batch data loads - Ingest data from diverse sources |
| Topic 2: Data Sharing and Federation | 5% | - Use Delta Sharing for secure data sharing - Manage cross-platform data access - Implement Lakehouse Federation |
| Topic 3: Monitoring and Alerting | 10% | - Monitor pipeline performance and health - Track data lineage and metrics - Set up alerts and notifications |
| Topic 4: Data Governance | 7% | - Manage data assets and metadata - Use Unity Catalog for governance - Enforce data policies and standards |
| Topic 5: Data Modelling | 6% | - Design Medallion Architecture - Implement dimensional and relational models - Optimize table design and partitioning |
| Topic 6: Data Transformation, Cleansing, and Quality | 10% | - Apply data cleansing and validation rules - Implement schema evolution and management - Enforce data quality standards |
| Topic 7: Developing Code for Data Processing using Python and SQL | 22% | - Implement complex data processing logic - Use Databricks-specific libraries and APIs - Write efficient and maintainable code |
| Topic 8: Cost & Performance Optimisation | 13% | - Optimize compute and storage resources - Improve query and pipeline performance - Apply cost management best practices |
| Topic 9: Debugging and Deploying | 10% | - Implement CI/CD and DevOps practices - Deploy using Asset Bundles, CLI, and APIs - Troubleshoot and debug pipelines |
| Topic 10: Ensuring Data Security and Compliance | 10% | - Implement access control and permissions - Ensure data privacy and compliance - Secure data at rest and in transit |
Databricks Certified Data Engineer Professional Sample Questions:
Question #1
The data science team has created and logged a production model using MLflow. The model accepts a list of column names and returns a new column of type DOUBLE.
The following code correctly imports the production model, loads the customers table containing the customer_id key column into a DataFrame, and defines the feature columns needed for the model.
Which code block will output a DataFrame with the schema "customer_id LONG, predictions DOUBLE"?
- A. df.apply(model, columns).select("customer_id, predictions")
- B. df.map(lambda x:model(x[columns])).select("customer_id, predictions")
- C. df.select("customer_id", model(*columns).alias("predictions"))
- D. df.select("customer_id", pandas_udf(model, columns).alias("predictions"))
- E. model.predict(df, columns)
Correct Answer: C 🗳️
Explanation: Only visible for ValidTorrent members. You can sign-up / login (it's free).
Question #2
Which of the following is true of Delta Lake and the Lakehouse?
- A. Because Parquet compresses data row by row. strings will only be compressed when a character is repeated multiple times.
- B. Delta Lake automatically collects statistics on the first 32 columns of each table which are leveraged in data skipping based on query filters.
- C. Primary and foreign key constraints can be leveraged to ensure duplicate values are never entered into a dimension table.
- D. Views in the Lakehouse maintain a valid cache of the most recent versions of source tables at all times.
- E. Z-order can only be applied to numeric values stored in Delta Lake tables
Correct Answer: B 🗳️
Explanation: Only visible for ValidTorrent members. You can sign-up / login (it's free).
Question #3
Although the Databricks Utilities Secrets module provides tools to store sensitive credentials and avoid accidentally displaying them in plain text users should still be careful with which credentials are stored here and which users have access to using these secrets.
Which statement describes a limitation of Databricks Secrets?
- A. Account administrators can see all secrets in plain text by logging on to the Databricks Accounts console.
- B. Because the SHA256 hash is used to obfuscate stored secrets, reversing this hash will display the value in plain text.
- C. The Databricks REST API can be used to list secrets in plain text if the personal access token has proper credentials.
- D. Secrets are stored in an administrators-only table within the Hive Metastore; database administrators have permission to query this table by default.
- E. Iterating through a stored secret and printing each character will display secret contents in plain text.
Correct Answer: C 🗳️
Explanation: Only visible for ValidTorrent members. You can sign-up / login (it's free).
Question #4
A junior data engineer has configured a workload that posts the following JSON to the Databricks REST API endpoint 2.0/jobs/create.
Assuming that all configurations and referenced resources are available, which statement describes the result of executing this workload three times?
- A. One new job named "Ingest new data" will be defined in the workspace, but it will not be executed.
- B. The logic defined in the referenced notebook will be executed three times on new clusters with the configurations of the provided cluster ID.
- C. Three new jobs named "Ingest new data" will be defined in the workspace, and they will each run once daily.
- D. Three new jobs named "Ingest new data" will be defined in the workspace, but no jobs will be executed.
- E. The logic defined in the referenced notebook will be executed three times on the referenced existing all purpose cluster.
Correct Answer: D 🗳️
Explanation: Only visible for ValidTorrent members. You can sign-up / login (it's free).
Question #5
A data engineer has configured their Databricks Asset Bundle with multiple targets in databricks.yml and deployed it to the production workspace. Now, to validate the deployment, they need to invoke a job named my_project_job specifically within the prod target context.
Assuming the job is already deployed, they need to trigger its execution while ensuring the target- specific configuration is respected. Which command will trigger the job execution?
- A. databricks bundle run my_project_job -t prod
- B. databricks job run my_project_job --env prod
- C. databricks run my_project_job -t prod
- D. databricks execute my_project_job -e prod
Correct Answer: A 🗳️
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