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| Section | Objectives |
|---|---|
| Topic 1: Data Governance and Quality | - Unity Catalog basics - Data access control and governance - Data quality concepts and management |
| Topic 2: Data Ingestion and ELT Development | - ETL patterns and transformations - Data ingestion using Spark SQL and PySpark - Handling structured and semi-structured data |
| Topic 3: Productionizing Data Pipelines | - Databricks Workflows / Jobs orchestration - Scheduling and monitoring jobs - Pipeline deployment and operationalization |
| Topic 4: Databricks Lakehouse Platform Fundamentals | - Workspace, architecture, and core platform concepts - Clusters, notebooks, and basic Databricks environment usage |
| Topic 5: Data Processing and Transformations | - Apache Spark SQL operations (joins, aggregations, filtering) - PySpark DataFrame transformations - Delta Lake fundamentals (tables, transactions, optimization) - User-defined functions (UDFs) |
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NEW QUESTION # 138
A data engineer working in Databricks needs to control BI query latency and cost for a sales analytics domain. Silver tables already contain validated, normalized sales and product data. The team wants dashboard-friendly query performance and to align datasets with business concepts (e.g., weekly_sales and account_performance) without overloading analysts with complex joins.
Which design approach should a data engineer choose for the Gold layer in a medallion architecture?
Answer: D
Explanation:
The Gold layer is designed for business-level consumption, so creating denormalized, domain- aligned tables and precomputed aggregates reduces query complexity and latency while optimizing cost for BI workloads and dashboards.
NEW QUESTION # 139
In which of the following scenarios should a data engineer use the MERGE INTO command instead of the INSERT INTO command?
Answer: E
Explanation:
Explanation
With merge , you can avoid inserting the duplicate records. The dataset containing the new logs needs to be deduplicated within itself. By the SQL semantics of merge, it matches and deduplicates the new data with the existing data in the table, but if there is duplicate data within the new dataset, it is inserted.https://docs.databricks.com/en/delta/merge.html#:~:text=With%20merge%20%2C%20you%20can%20a
NEW QUESTION # 140
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 # 141
What are the transformations typically included in building the Bronze layer ?
Answer: B
Explanation:
In the Medallion Architecture promoted by Databricks, the Bronze layer represents the raw ingestion layer where data is stored in its original form with minimal transformation. The primary goal is to ensure data fidelity and traceability , not to apply business logic or heavy transformations. Typical operations in the Bronze layer include schema inference (or enforcement), ingestion of raw data, and the addition of metadata columns such as ingestion timestamp, load date/time, and process identifiers. These metadata fields help track lineage, enable debugging, and support incremental processing downstream. Extensive data cleansing (A), aggregations (B), and business transformations (C) are intentionally deferred to the Silver and Gold layers , where data is refined and structured for analytics. By keeping Bronze data close to the source and append-only, organizations maintain a reliable audit trail and can reprocess data if needed. Therefore, adding operational metadata like load timestamps and process IDs is the correct characteristic of Bronze layer transformations.
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NEW QUESTION # 142
A data engineer only wants to execute the final block of a Python program if the Python variable day_of_week is equal to 1 and the Python variable review_period is True.
Which of the following control flow statements should the data engineer use to begin this conditionally executed code block?
Answer: E
Explanation:
In Python, the == operator is used to compare the values of two variables, while the = operator is used to assign a value to a variable. Therefore, option A and E are incorrect, as they use the = operator for comparison. Option B and C are also incorrect, as they compare the review_period variable to a string value "True", which is different from the boolean value True. Option D is the correct answer, as it uses the == operator to compare the day_of_week variable to the integer value 1, and the and operator to check if both conditions are true. If both conditions are true, then the final block of the Python program will be executed. Reference: [Python Operators], [Python If ... Else]
NEW QUESTION # 143
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