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NEW QUESTION # 109
A data engineer is running code in a Databricks Repo that is cloned from a central Git repository.
A colleague of the data engineer informs them that changes have been made and synced to the central Git repository. The data engineer now needs to sync their Databricks Repo to get the changes from the central Git repository.
Which of the following Git operations does the data engineer need to run to accomplish this task?
Answer: A
NEW QUESTION # 110
A data engineering team is designing the Gold layer in their Unity Catalog-governed lakehouse for downstream BI and analytics users. The team wants to expose business-ready metrics with fast query performance and consistent definitions, while keeping the transformation logic in Spark notebooks. Which type of Gold layer object meets this requirement?
Answer: B
Explanation:
A materialized view stores precomputed Gold-layer aggregations, providing BI tools with fast queries and centrally defined, consistent business results while allowing the transformation logic to remain in Databricks notebooks.
NEW QUESTION # 111
A Python file is ready for production and the client wants to use the most efficient yet cost- effective type of cluster possible. The workload is quite small, only processing 10GBs of data with only simple joins and no complex aggregations or wide transformations.
Which cluster meets the requirement?
Answer: A
Explanation:
A job cluster is the most cost-effective option for production workloads because it is created only for the duration of the job and then terminated. Enabling spot instances further reduces cost, and the small, simple workload does not require advanced performance optimizations.
NEW QUESTION # 112
A data engineer has a bundle with a job resource keyed as etl_job in databricks.yml. After running 'databricks bundle deploy', the job appears in the workspace but has not executed. Which command triggers execution of the deployed job?
Answer: C
Explanation:
databricks bundle deploy creates or updates the workspace resource but does not execute it.
databricks bundle run etl_job triggers the deployed job using its bundle resource key.
NEW QUESTION # 113
What are the transformations typically included in building the Bronze layer ?
Answer: D
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 # 114
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