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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Ensuring Data Security and Compliance | 10% | - Secure data at rest and in transit - Ensure data privacy and compliance - Implement access control and permissions |
| Topic 2: 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 3: Data Transformation, Cleansing, and Quality | 10% | - Enforce data quality standards - Apply data cleansing and validation rules - Implement schema evolution and management |
| Topic 4: Monitoring and Alerting | 10% | - Set up alerts and notifications - Monitor pipeline performance and health - Track data lineage and metrics |
| Topic 5: Cost & Performance Optimisation | 13% | - Optimize compute and storage resources - Improve query and pipeline performance - Apply cost management best practices |
| Topic 6: Data Ingestion & Acquisition | 7% | - Ingest data from diverse sources - Use Auto Loader and structured streaming - Handle incremental and batch data loads |
| Topic 7: Debugging and Deploying | 10% | - Troubleshoot and debug pipelines - Deploy using Asset Bundles, CLI, and APIs - Implement CI/CD and DevOps practices |
| Topic 8: Data Modelling | 6% | - Optimize table design and partitioning - Implement dimensional and relational models - Design Medallion Architecture |
| Topic 9: Data Governance | 7% | - Use Unity Catalog for governance - Manage data assets and metadata - Enforce data policies and standards |
| Topic 10: Data Sharing and Federation | 5% | - Manage cross-platform data access - Use Delta Sharing for secure data sharing - Implement Lakehouse Federation |
Databricks Certified Data Engineer Professional Sample Questions:
What is a method of installing a Python package scoped at the notebook level to all nodes in the currently active cluster?
- A. Run source env/bin/activate in a notebook setup script
- B. Install libraries from PyPi using the cluster UI
- C. Use &Pip install in a notebook cell
- D. Use &sh install in a notebook cell
Correct Answer: B 🗳️
Explanation: Only visible for VCE4Plus members. You can sign-up / login (it's free).
A company wants to implement Lakehouse Federation across multiple data sources but is concerned about data consistency and ensuring that all teams access the same authoritative version of their data. Which statement is applicable for Lakehouse Federations to maintain data consistency?
- A. Federation provides read-only access that reflects the current state of source systems.
- B. A separate data synchronization service must be deployed.
- C. Federation implements change data capture (CDC) from all sources.
- D. Federation creates local copies that must be manually refreshed.
Correct Answer: A 🗳️
Explanation: Only visible for VCE4Plus members. You can sign-up / login (it's free).
An upstream system is emitting change data capture (CDC) logs that are being written to a cloud object storage directory. Each record in the log indicates the change type (insert, update, or delete) and the values for each field after the change. The source table has a primary key identified by the field pk_id.
For analytical purposes, only the most recent value for each record needs to be recorded in the target Delta Lake table in the Lakehouse. The Databricks job to ingest these records occurs once per hour, but each individual record may have changed multiple times over the course of an hour.
Which solution meets these requirements?
- A. Use MERGE INTO to insert, update, or delete the most recent entry for each pk_id into a table, then propagate all changes throughout the system.
- B. Iterate through an ordered set of changes to the table, applying each in turn to create the current state of the table, (insert, update, delete), timestamp of change, and the values.
- C. Deduplicate records in each batch by pk_id and overwrite the target table.
- D. Use Delta Lake's change data feed to automatically process CDC data from an external system, propagating all changes to all dependent tables in the Lakehouse.
Correct Answer: D 🗳️
A data engineer is using the AUTO CDC API in Lakeflow Spark Declarative Pipeline to propagate deletions from a source table (orders_source) to a target table (orders_target). The source has Change Data Feed (CDF) enabled, but some delete events arrive out of order due to upstream delays. How does the AUTO CDC API internally ensure deletions are applied correctly despite out-of-order events?
- A. It manually sorts incoming events by timestamp before applying changes.
- B. It ignores deletions if they arrive after updates for the same key.
- C. It uses sequence_by to order events and retains tombstones for deleted rows until older sequences are processed.
- D. It runs VACUUM on the target table to purge conflicting records.
Correct Answer: C 🗳️
Explanation: Only visible for VCE4Plus members. You can sign-up / login (it's free).
A view is registered with the following code:
Both users and orders are Delta Lake tables.
Which statement describes the results of querying recent_orders?
- A. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.
- B. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.
- C. The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.
- D. All logic will execute when the table is defined and store the result of joining tables to the DBFS; this stored data will be returned when the table is queried.
Correct Answer: B 🗳️
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