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Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Cost and Performance Optimization | ~13% | - Optimize queries, clusters, and storage - Leverage system tables and observability tools |
| Topic 2: Data Modeling | ~10% | - Design scalable Delta Lake schemas and clustering - Apply dimensional modeling techniques |
| Topic 3: CI/CD, Testing, and Deployment | ~6% | - Deploy with Declarative Automation Bundles, CLI, and REST API - Implement testing and deployment pipelines |
| Topic 4: Monitoring, Logging, and Troubleshooting | ~8% | - Diagnose common pipeline and job failures - Use Spark UI, Query Profiler, and system tables |
| Topic 5: Data Transformation, Cleansing, and Quality | ~12% | - Apply advanced Spark transformations - Enforce data quality and quarantine bad data |
| Topic 6: Streaming Workloads and Change Data Capture | ~11% | - Implement reliable streaming pipelines - Apply AUTO CDC APIs and exactly-once semantics |
| Topic 7: Data Sharing and Federation | ~8% | - Configure Delta Sharing and Lakehouse Federation |
| Topic 8: Developing Code for Data Processing using Python and SQL | ~22% | - Build pipelines with Lakeflow Spark Declarative Pipelines and Auto Loader - Manage dependencies, libraries, and UDFs - Implement scalable Python/SQL code and project structures |
| Topic 9: Security and Governance | ~10% | - Implement row-level security, column masking, and compliance - Manage Unity Catalog permissions and ACLs |
Databricks Certified Data Engineer Professional Sample Questions:
Predictive Optimization is an automated Databricks service enabled by default for Unity Catalog Managed tables. It helps maintain Delta tables by continuously optimizing them to ensure optimal performance and costs. Which two operations does Predictive Optimization run to maintain the Delta tables? (Choose two.)
- A. BUCKETING
- B. ANALYZE
- C. OPTIMIZE
- D. COMPACT
- E. PARTITION BY
Correct Answer: B,C 🗳️
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A small company based in the United States has recently contracted a consulting firm in India to implement several new data engineering pipelines to power artificial intelligence applications. All the company's data is stored in regional cloud storage in the United States.
The workspace administrator at the company is uncertain about where the Databricks workspace used by the contractors should be deployed.
Assuming that all data governance considerations are accounted for, which statement accurately informs this decision?
- A. Databricks leverages user workstations as the driver during interactive development; as such, users should always use a workspace deployed in a region they are physically near.
- B. Databricks notebooks send all executable code from the user's browser to virtual machines over the open internet; whenever possible, choosing a workspace region near the end users is the most secure.
- C. Databricks runs HDFS on cloud volume storage; as such, cloud virtual machines must be deployed in the region where the data is stored.
- D. Cross-region reads and writes can incur significant costs and latency; whenever possible, compute should be deployed in the same region the data is stored.
- E. Databricks workspaces do not rely on any regional infrastructure; as such, the decision should be made based upon what is most convenient for the workspace administrator.
Correct Answer: D 🗳️
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A data engineer wants to join a stream of advertisement impressions (when an ad was shown) with another stream of user clicks on advertisements to correlate when impressions led to monetizable clicks.
In the code below, Impressions is a streaming DataFrame with a watermark ("event_time", "10 minutes")
The data engineer notices the query slowing down significantly.
Which solution would improve the performance?
- A. Joining on event time constraint: clickTime == impressionTime using a leftOuter join
- B. Joining on event time constraint: clickTime + 3 hours < impressionTime - 2 hours
- C. Joining on event time constraint: clickTime >= impressionTime AND clickTime <= impressionTime interval 1 hour
- D. Joining on event time constraint: clickTime >= impressionTime - interval 3 hours and removing watermarks
Correct Answer: C 🗳️
The view updates represents an incremental batch of all newly ingested data to be inserted or updated in the customers table.
The following logic is used to process these records.
Which statement describes this implementation?
- A. The customers table is implemented as a Type 0 table; all writes are append only with no changes to existing values.
- B. The customers table is implemented as a Type 3 table; old values are maintained as a new column alongside the current value.
- C. The customers table is implemented as a Type 1 table; old values are overwritten by new values and no history is maintained.
- D. The customers table is implemented as a Type 2 table; old values are overwritten and new customers are appended.
- E. The customers table is implemented as a Type 2 table; old values are maintained but marked as no longer current and new values are inserted.
Correct Answer: E 🗳️
A data engineer is implementing Unity Catalog governance for a multi-team environment. Data scientists need interactive clusters for basic data exploration tasks, while automated ETL jobs require dedicated processing. How should the data engineer configure cluster isolation policies to enforce least privilege and ensure Unity Catalog compliance?
- A. Create compute policies with STANDARD access mode for interactive workloads and DEDICATED access mode for automated jobs.
- B. Use only DEDICATED access mode for both interactive workloads and automated jobs to maximize security isolation.
- C. Configure all clusters with NO ISOLATION_SHARED access mode since Unity Catalog works with any cluster configuration.
- D. Allow all users to create any cluster type and rely on manual configuration to enable Unity Catalog access modes.
Correct Answer: A 🗳️
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