Online Access Free Databricks-Certified-Data-Engineer-Professional Practice Test

A Delta Lake table was created with the below query:

Realizing that the original query had a typographical error, the below code was executed:
ALTER TABLE prod.sales_by_stor RENAME TO prod.sales_by_store
Which result will occur after running the second command?

• A.The table reference in the metastore is updated and no data is changed.
• B.The table reference in the metastore is updated and all data files are moved.
• C.The table name change is recorded in the Delta transaction log.
• D.A new Delta transaction log Is created for the renamed table.
• E.All related files and metadata are dropped and recreated in a single ACID transaction.

A data engineer is designing a Lakeflow Declarative Pipeline to process streaming order data.
The pipeline uses Auto Loader to ingest data and must enforce data quality by ensuring customer_id and amount are greater than zero. Invalid records should be dropped. Which Lakeflow Declarative Pipelines configurations implement this requirement using Python?

• A.@dlt.table
  @dlt.expect("valid_customer", "customer_id IS NOT NULL")
  @dlt.expect("valid_amount", "amount > 0")
  def silver_orders():
  return dlt.read_stream("bronze_orders")
• B.@dlt.table
  def silver_orders():
  return (
  dlt.read_stream("bronze_orders")
  .expect("valid_customer", "customer_id IS NOT NULL")
  .expect("valid_amount", "amount > 0")
  )
• C.@dlt.table
  @dlt.expect_or_drop("valid_customer", "customer_id IS NOT NULL")
  @dlt.expect_or_drop("valid_amount", "amount > 0")
  def silver_orders():
  return dlt.read_stream("bronze_orders")
• D.@dlt.table
  def silver_orders():
  return (
  dlt.read_stream("bronze_orders")
  .expect_or_drop("valid_customer", "customer_id IS NOT NULL")
  .expect_or_drop("valid_amount", "amount > 0")
  )

A data engineer has created a new cluster using shared access mode with default configurations.
The data engineer needs to allow the development team access to view the driver logs if needed.
What are the minimal cluster permissions that allow the development team to accomplish this?

• A.CAN ATTACH TO
• B.CAN MANAGE
• C.CAN RESTART
• D.CAN VIEW

A data engineer and a platform engineer are working together to automate their system tasks. A script needs to be executed outside of Databricks only if a particular daily Databricks job finishes successfully for the day. Databricks CLI command was used to check the last execution of the job. What are the required command options for that task?

• A.databricks jobs list-runs --job-id JOB_ID --start-time-from TODAY_MIDNIGHT_EPOCH_MS -- completed-only
• B.databricks jobs list-runs --job-id JOB_ID --start-time-to TODAY_MIDNIGHT_EPOCH_MS --active- only
• C.databricks jobs list-runs --job-id JOB_ID --start-time-to TODAY_MIDNIGHT_EPOCH_MS -- completed-only
• D.databricks jobs list-runs --job-id JOB_ID --start-time-from TODAY_MIDNIGHT_EPOCH_MS -- active-only

A Delta table of weather records is partitioned by date and has the below schema:
date DATE, device_id INT, temp FLOAT, latitude FLOAT, longitude FLOAT
To find all the records from within the Arctic Circle, you execute a query with the below filter:
latitude > 66.3
Which statement describes how the Delta engine identifies which files to load?

• A.All records are cached to attached storage and then the filter is applied
• B.The Delta log is scanned for min and max statistics for the latitude column
• C.All records are cached to an operational database and then the filter is applied
• D.The Parquet file footers are scanned for min and max statistics for the latitude column
• E.The Hive metastore is scanned for min and max statistics for the latitude column