A. Incorrect.
OWN is not the best answer because it does not match the Databricks/Spark behavior being tested. The key point is: SELECT grants read access without write authority.

B. Incorrect.
CREATE VOLUME is not the best answer because it does not match the Databricks/Spark behavior being tested. The key point is: SELECT grants read access without write authority.

C. Correct.
SELECT grants read access without write authority.

D. Incorrect.
MODIFY is not the best answer because it does not match the Databricks/Spark behavior being tested. The key point is: SELECT grants read access without write authority.

A. Incorrect.
MSCK REPAIR TABLE is not the best answer because it does not match the Databricks/Spark behavior being tested. The key point is: VACUUM deletes files no longer referenced by the Delta log after retention constraints are met.

B. Correct.
VACUUM deletes files no longer referenced by the Delta log after retention constraints are met.

C. Incorrect.
CACHE TABLE is not the best answer because it does not match the Databricks/Spark behavior being tested. The key point is: VACUUM deletes files no longer referenced by the Delta log after retention constraints are met.

D. Incorrect.
ANALYZE TABLE is not the best answer because it does not match the Databricks/Spark behavior being tested. The key point is: VACUUM deletes files no longer referenced by the Delta log after retention constraints are met.

A. Correct.
Workflow task dependencies define execution order and failure behavior.

B. Incorrect.
A larger SQL warehouse is not the best answer because it does not match the Databricks/Spark behavior being tested. The key point is: Workflow task dependencies define execution order and failure behavior.

C. Incorrect.
A cluster policy with no libraries is not the best answer because it does not match the Databricks/Spark behavior being tested. The key point is: Workflow task dependencies define execution order and failure behavior.

D. Incorrect.
A manual dashboard refresh is not the best answer because it does not match the Databricks/Spark behavior being tested. The key point is: Workflow task dependencies define execution order and failure behavior.

A. Incorrect.
COPY INTO inside an infinite loop is not the best answer because it does not match the Databricks/Spark behavior being tested. The key point is: Auto Loader tracks discovered files and scales incremental ingestion.

B. Incorrect.
Manual dbutils.fs.ls polling is not the best answer because it does not match the Databricks/Spark behavior being tested. The key point is: Auto Loader tracks discovered files and scales incremental ingestion.

C. Incorrect.
A dashboard alert is not the best answer because it does not match the Databricks/Spark behavior being tested. The key point is: Auto Loader tracks discovered files and scales incremental ingestion.

D. Correct.
Auto Loader tracks discovered files and scales incremental ingestion.

A. Incorrect.
DROP FEATURE customer_id is not the best answer because it does not match the Databricks/Spark behavior being tested. The key point is: Z-order clustering co-locates related column values to improve skipping.

B. Incorrect.
Increase notebook autosave frequency is not the best answer because it does not match the Databricks/Spark behavior being tested. The key point is: Z-order clustering co-locates related column values to improve skipping.

C. Correct.
Z-order clustering co-locates related column values to improve skipping.

D. Incorrect.
VACUUM with zero retention is not the best answer because it does not match the Databricks/Spark behavior being tested. The key point is: Z-order clustering co-locates related column values to improve skipping.

A. Incorrect.
SQL endpoint tags is not the best answer because it does not match the Databricks/Spark behavior being tested. The key point is: Expectations define data quality rules in Lakeflow/Delta Live Tables pipelines.

B. Correct.
Expectations define data quality rules in Lakeflow/Delta Live Tables pipelines.

C. Incorrect.
Cluster init scripts is not the best answer because it does not match the Databricks/Spark behavior being tested. The key point is: Expectations define data quality rules in Lakeflow/Delta Live Tables pipelines.

D. Incorrect.
Notebook comments is not the best answer because it does not match the Databricks/Spark behavior being tested. The key point is: Expectations define data quality rules in Lakeflow/Delta Live Tables pipelines.

A. Correct.
Secret scopes avoid exposing credentials in code or table metadata.

B. Incorrect.
Hard-code it in the notebook is not the best answer because it does not match the Databricks/Spark behavior being tested. The key point is: Secret scopes avoid exposing credentials in code or table metadata.

C. Incorrect.
Put it in the table comment is not the best answer because it does not match the Databricks/Spark behavior being tested. The key point is: Secret scopes avoid exposing credentials in code or table metadata.

D. Incorrect.
Save it in DBFS as plain text is not the best answer because it does not match the Databricks/Spark behavior being tested. The key point is: Secret scopes avoid exposing credentials in code or table metadata.

A. Incorrect.
Notebook revision history is not the best answer because it does not match the Databricks/Spark behavior being tested. The key point is: AQE can adjust plans at runtime, including skewed partition handling.

B. Incorrect.
Unity Catalog lineage only is not the best answer because it does not match the Databricks/Spark behavior being tested. The key point is: AQE can adjust plans at runtime, including skewed partition handling.

C. Incorrect.
A SQL visualization is not the best answer because it does not match the Databricks/Spark behavior being tested. The key point is: AQE can adjust plans at runtime, including skewed partition handling.

D. Correct.
AQE can adjust plans at runtime, including skewed partition handling.

A. Incorrect.
Personal access token comments is not the best answer because it does not match the Databricks/Spark behavior being tested. The key point is: Cluster policies constrain user-selectable compute settings.

B. Incorrect.
A table alias is not the best answer because it does not match the Databricks/Spark behavior being tested. The key point is: Cluster policies constrain user-selectable compute settings.

C. Correct.
Cluster policies constrain user-selectable compute settings.

D. Incorrect.
Dashboard filters is not the best answer because it does not match the Databricks/Spark behavior being tested. The key point is: Cluster policies constrain user-selectable compute settings.

A. Incorrect.
The cluster event log only is not the best answer because it does not match the Databricks/Spark behavior being tested. The key point is: Unity Catalog captures lineage across supported queries and jobs.

B. Correct.
Unity Catalog captures lineage across supported queries and jobs.

C. Incorrect.
A local Python requirements file is not the best answer because it does not match the Databricks/Spark behavior being tested. The key point is: Unity Catalog captures lineage across supported queries and jobs.

D. Incorrect.
A Spark accumulator is not the best answer because it does not match the Databricks/Spark behavior being tested. The key point is: Unity Catalog captures lineage across supported queries and jobs.