A workspace admin has created a new catalog called finance_data and wants to delegate permission management to a finance team lead without giving them full admin rights. Which privilege should be granted to the finance team lead?

• A.ALL PRIVILEGES on the finance_data catalog.
• B.Make the finance team lead a metastore admin.
• C.GRANT OPTION privilege on the finance_data catalog.
• D.MANAGE privilege on the finance_data catalog.

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Two of the most common data locations on Databricks are the DBFS root storage and external object storage mounted with dbutils.fs.mount().
Which of the following statements is correct?

• A.DBFS is a file system protocol that allows users to interact with files stored in object storage using syntax and guarantees similar to Unix file systems.
• B.By default, both the DBFS root and mounted data sources are only accessible to workspace administrators.
• C.The DBFS root is the most secure location to store data, because mounted storage volumes must have full public read and write permissions.
• D.Neither the DBFS root nor mounted storage can be accessed when using %sh in a Databricks notebook.
• E.The DBFS root stores files in ephemeral block volumes attached to the driver, while mounted directories will always persist saved data to external storage between sessions.

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A data engineering team is collaborating on a Databricks project where each team member needs to develop and test code independently before merging changes into the main branch.
They want to avoid accidental overwrites or branch switching issues while ensuring that all work is version- controlled and can be integrated into their CI/CD pipeline.
How should the data engineer achieve collaboration?

• A.Each team member creates their own Databricks Git folder, mapped to the same remote Git repository, and works in their own development branch within their personal folder.
• B.All team members work in the same Databricks Git folder and perform Git operations (pull, push, commit, branch switching) directly in that shared folder.
• C.Team members edit notebooks directly in the workspace's shared folder and periodically copy changes into a Git folder for version control.
• D.Team members use the Databricks CLI to clone the Git repository and perform Git operations from a cluster's web terminal.

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An upstream system is emitting change data capture (CDC) logs that are being written to a cloud object storage directory. Each record in the log indicates the change type (insert, update, or delete) and the values for each field after the change. The source table has a primary key identified by the field pk_id.
For auditing purposes, the data governance team wishes to maintain a full record of all values that have ever been valid in the source system. For analytical purposes, only the most recent value for each record needs to be recorded. The Databricks job to ingest these records occurs once per hour, but each individual record may have changed multiple times over the course of an hour.
Which solution meets these requirements?

• A.Create a separate history table for each pk_id resolve the current state of the table by running a union all filtering the history tables for the most recent state.
• B.Use merge into to insert, update, or delete the most recent entry for each pk_id into a bronze table, then propagate all changes throughout the system.
• C.Iterate through an ordered set of changes to the table, applying each in turn; rely on Delta Lake's versioning ability to create an audit log.
• D.Use Delta Lake's change data feed to automatically process CDC data from an external system, propagating all changes to all dependent tables in the Lakehouse.
• E.Ingest all log information into a bronze table; use merge into to insert, update, or delete the most recent entry for each pk_id into a silver table to recreate the current table state.

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A nightly batch job is configured to ingest all data files from a cloud object storage container where records are stored in a nested directory structure YYYY/MM/DD. The data for each date represents all records that were processed by the source system on that date, noting that some records may be delayed as they await moderator approval. Each entry represents a user review of a product and has the following schema:
user_id STRING, review_id BIGINT, product_id BIGINT, review_timestamp TIMESTAMP, review_text STRING The ingestion job is configured to append all data for the previous date to a target table reviews_raw with an identical schema to the source system. The next step in the pipeline is a batch write to propagate all new records inserted into reviews_raw to a table where data is fully deduplicated, validated, and enriched.
Which solution minimizes the compute costs to propagate this batch of data?
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• A.Perform a batch read on the reviews_raw table and perform an insert-only merge using the natural composite key user_id, review_id, product_id, review_timestamp.
• B.Configure a Structured Streaming read against the reviews_raw table using the trigger once execution mode to process new records as a batch job.
• C.Use Delta Lake version history to get the difference between the latest version of reviews_raw and one version prior, then write these records to the next table.
• D.Filter all records in the reviews_raw table based on the review_timestamp; batch append those records produced in the last 48 hours.
• E.Reprocess all records in reviews_raw and overwrite the next table in the pipeline.

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