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| Section | Objectives |
|---|---|
| Topic 1: Data Processing and Transformations | - Delta Lake fundamentals (tables, transactions, optimization) - User-defined functions (UDFs) - Apache Spark SQL operations (joins, aggregations, filtering) - PySpark DataFrame transformations |
| Topic 2: Data Governance and Quality | - Unity Catalog basics - Data access control and governance - Data quality concepts and management |
| Topic 3: Data Ingestion and ELT Development | - ETL patterns and transformations - Handling structured and semi-structured data - Data ingestion using Spark SQL and PySpark |
| Topic 4: Databricks Lakehouse Platform Fundamentals | - Clusters, notebooks, and basic Databricks environment usage - Workspace, architecture, and core platform concepts |
| Topic 5: Productionizing Data Pipelines | - Pipeline deployment and operationalization - Scheduling and monitoring jobs - Databricks Workflows / Jobs orchestration |
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NEW QUESTION # 160
A new data engineering team has been assigned to work on a project. The team will need access to database customers in order to see what tables already exist. The team has its own group team.
Which of the following commands can be used to grant the necessary permission on the entire database to the new team?
Answer: C
Explanation:
Explanation
The GRANT statement is used to grant privileges on a database, table, or view to a user or role. The ALL PRIVILEGES option grants all possible privileges on the specified object, such as CREATE, SELECT, MODIFY, and USAGE. The syntax of the GRANT statement is:
GRANT privilege_type ON object TO user_or_role;
Therefore, to grant full permissions on the database customers to the new data engineering team, the command should be:
GRANT ALL PRIVILEGES ON DATABASE customers TO team;
NEW QUESTION # 161
A data engineer is using the OPTIMIZE command on a Delta table. What happens when OPTIMIZE is run twice on the same table with the same data?
Answer: D
Explanation:
The OPTIMIZE command in Delta Lake is designed to compact small files into larger files to improve query performance and reduce file system overhead. Importantly, OPTIMIZE is considered an idempotent operation when run on unchanged data. This means that once files have been compacted optimally, running OPTIMIZE again does not produce additional changes unless new small files have been added to the table.
Databricks ensures that file compaction is efficient and avoids unnecessary rewrites, which helps control compute costs and maintain stable performance. While features like ZORDER can be applied during OPTIMIZE to improve data skipping, re-running the same command without new data will not further reorganize files. Options A, B, and C incorrectly imply additional restructuring or changes on repeated execution. According to Delta Lake best practices, OPTIMIZE should be run periodically after data ingestion events rather than repeatedly on unchanged datasets. This behavior reinforces predictable performance and efficient resource utilization in Databricks environments.
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NEW QUESTION # 162
Which of the following Structured Streaming queries is performing a hop from a Silver table to a Gold table?
Answer: E
Explanation:
The best practice is to use "Complete" as output mode instead of "append" when working with aggregated tables. Since gold layer is work final aggregated tables, the only option with output mode as complete is option E.
NEW QUESTION # 163
A Lakeflow Jobs workflow fails at the last task because of an error in a notebook task. This workflow is scheduled to run daily. The data engineer fixes the notebook and wants to rerun the pipeline. This workflow is very compute-intensive and expensive to run. Which action should the data engineer take in order to minimize downtime and compute cost?
Answer: A
Explanation:
Repair run allows rerunning only the failed task and any dependent tasks instead of executing the entire workflow again, minimizing compute cost and downtime while preserving the original run context.
NEW QUESTION # 164
A data engineering team needs to integrate two data sources into Databricks:
- Clickstream Events: Ingesting 5,000 events per second from an Apache
Kafka topic.
- Customer Master Data: Syncing only changed records every 4 hours from a Snowflake database.
The solution must meet two key requirements:
- Process the streaming clickstream data with a latency of under 30
seconds.
- Prevent the reprocessing of customer master data that has not
changed.
Which data ingestion approach meets these requirements?
Answer: A
Explanation:
Structured Streaming provides continuous, low-latency ingestion from Kafka. A Lakeflow Connect managed connector in incremental mode tracks source changes and ingests only changed Snowflake records after the initial load, preventing unnecessary reprocessing.
NEW QUESTION # 165
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