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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Security and Governance | ~10% | - Implement row-level security, column masking, and compliance - Manage Unity Catalog permissions and ACLs |
| Topic 2: Streaming Workloads and Change Data Capture | ~11% | - Implement reliable streaming pipelines - Apply AUTO CDC APIs and exactly-once semantics |
| Topic 3: Data Transformation, Cleansing, and Quality | ~12% | - Enforce data quality and quarantine bad data - Apply advanced Spark transformations |
| Topic 4: Monitoring, Logging, and Troubleshooting | ~8% | - Diagnose common pipeline and job failures - Use Spark UI, Query Profiler, and system tables |
| Topic 5: Developing Code for Data Processing using Python and SQL | ~22% | - Build pipelines with Lakeflow Spark Declarative Pipelines and Auto Loader - Implement scalable Python/SQL code and project structures - Manage dependencies, libraries, and UDFs |
| Topic 6: Cost and Performance Optimization | ~13% | - Leverage system tables and observability tools - Optimize queries, clusters, and storage |
| Topic 7: Data Sharing and Federation | ~8% | - Configure Delta Sharing and Lakehouse Federation |
| Topic 8: CI/CD, Testing, and Deployment | ~6% | - Implement testing and deployment pipelines - Deploy with Declarative Automation Bundles, CLI, and REST API |
| Topic 9: Data Modeling | ~10% | - Design scalable Delta Lake schemas and clustering - Apply dimensional modeling techniques |
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NEW QUESTION # 196
A data engineering workspace was automatically enabled for Unity Catalog, creating a workspace catalog. New team members report they can create tables in the default schema but cannot access table in other schemas within the same workspace catalog. Why are the new team members unable to access tables in other schemas?
Answer: B
Explanation:
When a workspace catalog is automatically created, new users are granted USE CATALOG and limited privileges on the default schema only. Access to other schemas requires explicit grants, so users cannot see or query tables in those schemas without additional permissions.
NEW QUESTION # 197
A healthcare analytics team is implementing a dimensional model in Delta Lake for patient care analysis. They have a date dimension table and are evaluating design options to ensure it supports a wide range of time-based analyses. Which design approach for the date dimension will support efficient time-based querying and aggregation?
Answer: B
Explanation:
Pre-calculating commonly used time attributes in the date dimension enables efficient filtering, grouping, and aggregation without requiring repeated computations at query time. This design supports a wide range of time-based analyses and improves query performance and usability in dimensional models.
NEW QUESTION # 198
A junior data engineer seeks to leverage Delta Lake's Change Data Feed functionality to create a Type 1 table representing all of the values that have ever been valid for all rows in a bronze table created with the property delta.enableChangeDataFeed = true. They plan to execute the following code as a daily job:
Which statement describes the execution and results of running the above query multiple times?
Answer: C
Explanation:
Reading table's changes, captured by CDF, using spark.read means that you are reading them as a static source. So, each time you run the query, all table's changes (starting from the specified startingVersion) will be read.
NEW QUESTION # 199
Assuming that the Databricks CLI has been installed and configured correctly, which Databricks CLI command can be used to upload a custom Python Wheel to object storage mounted with the DBFS for use with a production job?
Answer: E
Explanation:
https://docs.databricks.com/en/archive/dev-tools/cli/dbfs-cli.html
NEW QUESTION # 200
A data engineer is designing an append-only pipeline that needs to handle both batch and streaming data in Delta Lake. The team wants to ensure that the streaming component can efficiently track which data has already been processed. Which configuration should be set to enable this?
Answer: C
Explanation:
When working with Delta Lake streaming ingestion, checkpointing is critical for maintaining fault tolerance and ensuring exactly-once data processing semantics.
The checkpointLocation parameter defines the directory where Spark Structured Streaming stores progress information, offsets, and metadata. This allows the engine to resume processing from the last committed offset without reprocessing previously ingested data.
Without checkpointing, each stream restart would reprocess all data, leading to duplicates.
Parameters like partitionBy or schema options (mergeSchema / overwriteSchema) affect table structure, not data lineage tracking. Therefore, the correct and required configuration for efficient streaming state management is checkpointLocation.
NEW QUESTION # 201
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