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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Ensuring Data Security and Compliance | 10% | - Secure data at rest and in transit - Ensure data privacy and compliance - Implement access control and permissions |
| Developing Code for Data Processing using Python and SQL | 22% | - Write efficient and maintainable code - Use Databricks-specific libraries and APIs - Implement complex data processing logic |
| Monitoring and Alerting | 10% | - Track data lineage and metrics - Set up alerts and notifications - Monitor pipeline performance and health |
| Data Governance | 7% | - Use Unity Catalog for governance - Manage data assets and metadata - Enforce data policies and standards |
| Data Ingestion & Acquisition | 7% | - Ingest data from diverse sources - Use Auto Loader and structured streaming - Handle incremental and batch data loads |
| Data Transformation, Cleansing, and Quality | 10% | - Apply data cleansing and validation rules - Enforce data quality standards - Implement schema evolution and management |
| Data Sharing and Federation | 5% | - Use Delta Sharing for secure data sharing - Manage cross-platform data access - Implement Lakehouse Federation |
| Cost & Performance Optimisation | 13% | - Optimize compute and storage resources - Apply cost management best practices - Improve query and pipeline performance |
| Data Modelling | 6% | - Implement dimensional and relational models - Optimize table design and partitioning - Design Medallion Architecture |
| Debugging and Deploying | 10% | - Implement CI/CD and DevOps practices - Deploy using Asset Bundles, CLI, and APIs - Troubleshoot and debug pipelines |
Databricks Certified Data Engineer Professional Sample Questions:
A junior data engineer is working to implement logic for a Lakehouse table named silver_device_recordings. The source data contains 100 unique fields in a highly nested JSON structure.
The silver_device_recordings table will be used downstream to power several production monitoring dashboards and a production model. At present, 45 of the 100 fields are being used in at least one of these applications.
The data engineer is trying to determine the best approach for dealing with schema declaration given the highly-nested structure of the data and the numerous fields.
Which of the following accurately presents information about Delta Lake and Databricks that may impact their decision-making process?
- A. Schema inference and evolution on .Databricks ensure that inferred types will always accurately match the data types used by downstream systems.
- B. The Tungsten encoding used by Databricks is optimized for storing string data; newly-added native support for querying JSON strings means that string types are always most efficient.
- C. Because Delta Lake uses Parquet for data storage, data types can be easily evolved by just modifying file footer information in place.
- D. Human labor in writing code is the largest cost associated with data engineering workloads; as such, automating table declaration logic should be a priority in all migration workloads.
- E. Because Databricks will infer schema using types that allow all observed data to be processed, setting types manually provides greater assurance of data quality enforcement.
Correct Answer: E 🗳️
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When evaluating the Ganglia Metrics for a given cluster with 3 executor nodes, which indicator would signal proper utilization of the VM's resources?
- A. Network I/O never spikes
- B. Total Disk Space remains constant
- C. Bytes Received never exceeds 80 million bytes per second
- D. The five Minute Load Average remains consistent/flat
- E. CPU Utilization is around 75%
Correct Answer: E 🗳️
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A platform engineer is creating catalogs and schemas for the development team to use.
The engineer has created an initial catalog, catalog_A, and initial schema, schema_A. The engineer has also granted USE CATALOG, USE SCHEMA, and CREATE TABLE to the development team so that the engineer can begin populating the schema with new tables.
Despite being owner of the catalog and schema, the engineer noticed that they do not have access to the underlying tables in Schema_A.
What explains the engineer's lack of access to the underlying tables?
- A. Permissions explicitly given by the table creator are the only way the Platform Engineer could access the underlying tables in their schema.
- B. The owner of the schema does not automatically have permission to tables within the schema, but can grant them to themselves at any point.
- C. Users granted with USE CATALOG can modify the owner's permissions to downstream tables.
- D. The platform engineer needs to execute a REFRESH statement as the table permissions did not automatically update for owners.
Correct Answer: B 🗳️
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What describes a primary technical challenge in ensuring consistent PII masking across all nodes in large-scale, distributed Databricks batch and streaming pipelines?
- A. Masking functions must be standardized and managed through Unity Catalog, with enforcement applied across all relevant datasets to avoid any data inconsistency.
- B. Dynamic data masking is applied only at rest, so it does not affect query performance.
- C. PII masking is only required for direct identifiers.
- D. Native masking in Databricks automatically synchronizes with all downstream external Databricks systems.
Correct Answer: A 🗳️
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A data team's Structured Streaming job is configured to calculate running aggregates for item sales to update a downstream marketing dashboard. The marketing team has introduced a new field to track the number of times this promotion code is used for each item. A junior data engineer suggests updating the existing query as follows: Note that proposed changes are in bold.
Original query:
Proposed query:
Which step must also be completed to put the proposed query into production?
- A. Increase the shuffle partitions to account for additional aggregates
- B. Register the data in the "/item_agg" directory to the Hive metastore
- C. Remove .option (mergeSchema', true') from the streaming write
- D. Run REFRESH TABLE delta, /item_agg'
- E. Specify a new checkpointlocation
Correct Answer: E 🗳️
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