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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Ensuring Data Security and Compliance | 10% | - Ensure data privacy and compliance - Secure data at rest and in transit - Implement access control and permissions |
| Data Transformation, Cleansing, and Quality | 10% | - Implement schema evolution and management - Apply data cleansing and validation rules - Enforce data quality standards |
| Developing Code for Data Processing using Python and SQL | 22% | - Use Databricks-specific libraries and APIs - Implement complex data processing logic - Write efficient and maintainable code |
| Data Ingestion & Acquisition | 7% | - Ingest data from diverse sources - Use Auto Loader and structured streaming - Handle incremental and batch data loads |
| Data Sharing and Federation | 5% | - Use Delta Sharing for secure data sharing - Implement Lakehouse Federation - Manage cross-platform data access |
| Cost & Performance Optimisation | 13% | - Apply cost management best practices - Improve query and pipeline performance - Optimize compute and storage resources |
| Data Modelling | 6% | - Optimize table design and partitioning - Design Medallion Architecture - Implement dimensional and relational models |
| Data Governance | 7% | - Use Unity Catalog for governance - Manage data assets and metadata - Enforce data policies and standards |
| Monitoring and Alerting | 10% | - Set up alerts and notifications - Monitor pipeline performance and health - Track data lineage and metrics |
| Debugging and Deploying | 10% | - Troubleshoot and debug pipelines - Implement CI/CD and DevOps practices - Deploy using Asset Bundles, CLI, and APIs |
Databricks Certified Data Engineer Professional Sample Questions:
A data architect has designed a system in which two Structured Streaming jobs will concurrently write to a single bronze Delta table. Each job is subscribing to a different topic from an Apache Kafka source, but they will write data with the same schema. To keep the directory structure simple, a data engineer has decided to nest a checkpoint directory to be shared by both streams.
The proposed directory structure is displayed below:
Which statement describes whether this checkpoint directory structure is valid for the given scenario and why?
- A. Yes; both of the streams can share a single checkpoint directory.
- B. No; Delta Lake manages streaming checkpoints in the transaction log.
- C. Yes; Delta Lake supports infinite concurrent writers.
- D. No; each of the streams needs to have its own checkpoint directory.
- E. No; only one stream can write to a Delta Lake table.
Correct Answer: D 🗳️
Explanation: Only visible for Dumpexams members. You can sign-up / login (it's free).
The data engineer is using Spark's MEMORY_ONLY storage level. Which indicators should the data engineer look for in the spark UI's Storage tab to signal that a cached table is not performing optimally?
- A. The RDD Block Name included the '' annotation signaling failure to cache
- B. The number of Cached Partitions> the number of Spark Partitions
- C. On Heap Memory Usage is within 75% of off Heap Memory usage
- D. Size on Disk is < Size in Memory
- E. Size on Disk is> 0
Correct Answer: E 🗳️
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An analytics team wants to run a short-term experiment in Databricks SQL on the customer transactions Delta table (about 20 billion records) created by the data engineering team. Which strategy should the data engineering team use to ensure minimal downtime and no impact on the ongoing ETL processes?
- A. Shallow clone the table for the analytics team.
- B. Give the analytics team direct access to the production table.
- C. Deep clone the table for the analytics team.
- D. Create a new table for the analytics team using a CTAS statement.
Correct Answer: A 🗳️
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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.
MERGE INTO customers
USING (
SELECT updates.customer_id as merge_ey, updates .*
FROM updates
UNION ALL
SELECT NULL as merge_key, updates .*
FROM updates JOIN customers
ON updates.customer_id = customers.customer_id
WHERE customers.current = true AND updates.address <> customers.address ) staged_updates ON customers.customer_id = mergekey WHEN MATCHED AND customers. current = true AND customers.address <> staged_updates.address THEN UPDATE SET current = false, end_date = staged_updates.effective_date WHEN NOT MATCHED THEN INSERT (customer_id, address, current, effective_date, end_date) VALUES (staged_updates.customer_id, staged_updates.address, true, staged_updates.effective_date, null) Which statement describes this implementation?
- A. The customers table is implemented as a Type 1 table; old values are overwritten by new values and no history is maintained.
- B. 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.
- C. The customers table is implemented as a Type 2 table; old values are overwritten and new customers are appended.
- D. The customers table is implemented as a Type 0 table; all writes are append only with no changes to existing values.
Correct Answer: B 🗳️
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Which statement describes a key benefit of an end-to-end test?
- A. It pinpoint errors in the building blocks of your application.
- B. It provides testing coverage for all code paths and branches.
- C. It makes it easier to automate your test suite
- D. It closely simulates real world usage of your application.
Correct Answer: D 🗳️
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