A data team ' s Structured Streaming job is configured to calculate running aggregates for item sales to update a downstream marketing dashboard. The marketing team has introduced a new field to track the number of times this promotion code is used for each item. A junior data engineer suggests updating the existing query as follows: Note that proposed changes are in bold.

Which step must also be completed to put the proposed query into production?

• A. Specify a new checkpointlocation
• B. Run REFRESH TABLE delta, /item_agg '
• C. Remove .option (mergeSchema ' , true ' ) from the streaming write
• D. Increase the shuffle partitions to account for additional aggregates

A data engineer is using Auto Loader to read incoming JSON data as it arrives. They have configured Auto Loader to quarantine invalid JSON records but notice that over time, some records are being quarantined even though they are well-formed JSON .
The code snippet is:
df = (spark.readStream
.format( " cloudFiles " )
.option( " cloudFiles.format " , " json " )
.option( " badRecordsPath " , " /tmp/somewhere/badRecordsPath " )
.schema( " a int, b int " )
.load( " /Volumes/catalog/schema/raw_data/ " ))
What is the cause of the missing data?

• A. At some point, the upstream data provider switched everything to multi-line JSON.
• B. The source data is valid JSON but does not conform to the defined schema in some way.
• C. The badRecordsPath location is accumulating many small files.
• D. The engineer forgot to set the option " cloudFiles.quarantineMode " = " rescue " .

A data engineer is using Lakeflow Declarative Pipelines Expectations feature to track the data quality of their incoming sensor data. Periodically, sensors send bad readings that are out of range, and they are currently flagging those rows with a warning and writing them to the silver table along with the good data. They've been given a new requirement - the bad rows need to be quarantined in a separate quarantine table and no longer included in the silver table.
This is the existing code for their silver table:
@dlt.table
@dlt.expect( " valid_sensor_reading " , " reading < 120 " )
def silver_sensor_readings():
return spark.readStream.table( " bronze_sensor_readings " )
What code will satisfy the requirements?

• A. @dlt.table
  @dlt.expect_or_drop( " valid_sensor_reading " , " reading < 120 " )
  def silver_sensor_readings():
  return spark.readStream.table( " bronze_sensor_readings " )
  @dlt.table
  @dlt.expect( " invalid_sensor_reading " , " reading > = 120 " )
  def quarantine_sensor_readings():
  return spark.readStream.table( " bronze_sensor_readings " )
• B. @dlt.table
  @dlt.expect( " valid_sensor_reading " , " reading < 120 " )
  def silver_sensor_readings():
  return spark.readStream.table( " bronze_sensor_readings " )
  @dlt.table
  @dlt.expect( " invalid_sensor_reading " , " reading > = 120 " )
  def quarantine_sensor_readings():
  return spark.readStream.table( " bronze_sensor_readings " )
• C. @dlt.table
  @dlt.expect_or_drop( " valid_sensor_reading " , " reading < 120 " )
  def silver_sensor_readings():
  return spark.readStream.table( " bronze_sensor_readings " )
  @dlt.table
  @dlt.expect_or_drop( " invalid_sensor_reading " , " reading > = 120 " ) def quarantine_sensor_readings():
  return spark.readStream.table( " bronze_sensor_readings " )
• D. @dlt.table
  @dlt.expect_or_drop( " valid_sensor_reading " , " reading < 120 " )
  def silver_sensor_readings():
  return spark.readStream.table( " bronze_sensor_readings " )
  @dlt.table
  @dlt.expect( " invalid_sensor_reading " , " reading < 120 " )
  def quarantine_sensor_readings():
  return spark.readStream.table( " bronze_sensor_readings " )

A data engineer needs to provide access to a group named manufacturing-team. The team needs privileges to create tables in the quality schema.
Which set of SQL commands will grant a group named manufacturing-team to create tables in a schema named production with the parent catalog named manufacturing with the least privileges?

• A. GRANT CREATE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT CREATE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE CATALOG ON CATALOG manufacturing TO manufacturing-team;
• B. GRANT CREATE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE CATALOG ON CATALOG manufacturing TO manufacturing-team;
• C. GRANT CREATE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT CREATE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT CREATE CATALOG ON CATALOG manufacturing TO manufacturing-team;
• D. GRANT USE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE CATALOG ON CATALOG manufacturing TO manufacturing-team;

A transactions table has been liquid clustered on the columns product_id, user_id, and event_date.
Which operation lacks support for cluster on write?

• A. spark.writestream.format( ' delta ' ).mode( ' append ' )
• B. CTAS and RTAS statements
• C. spark.write.format( ' delta ' ).mode( ' append ' )
• D. INSERT INTO operations