A data engineering team is in the process of converting their existing data pipeline to utilize Auto Loader for
incremental processing in the ingestion of JSON files. One data engineer comes across the following code
block in the Auto Loader documentation:
1. (streaming_df = spark.readStream.format("cloudFiles")
2. .option("cloudFiles.format", "json")
3. .option("cloudFiles.schemaLocation", schemaLocation)
4. .load(sourcePath))
Assuming that schemaLocation and sourcePath have been set correctly, which of the following changes does
the data engineer need to make to convert this code block to use Auto Loader to ingest the data?

• A.The data engineer needs to change the format("cloudFiles") line to format("autoLoader")
• B.The data engineer needs to add the .autoLoader line before the .load(sourcePath) line
• C.There is no change required. The data engineer needs to ask their administrator to turn on Auto Loader
• D.There is no change required. Databricks automatically uses Auto Loader for streaming reads
• E.There is no change required. The inclusion of format("cloudFiles") enables the use of Auto Loader

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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("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_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")
• 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")

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

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Assuming that the Databricks CLI has been installed and configured correctly, which Databricks CLI command can be used to upload a custom Python Wheel to object storage mounted with the DBFS for use with a production job?

• A.configure
• B.fs
• C.jobs
• D.libraries
• E.workspace

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A small company based in the United States has recently contracted a consulting firm in India to implement several new data engineering pipelines to power artificial intelligence applications. All the company's data is stored in regional cloud storage in the United States.
The workspace administrator at the company is uncertain about where the Databricks workspace used by the contractors should be deployed.
Assuming that all data governance considerations are accounted for, which statement accurately informs this decision?

• A.Databricks runs HDFS on cloud volume storage; as such, cloud virtual machines must be deployed in the region where the data is stored.
• B.Databricks workspaces do not rely on any regional infrastructure; as such, the decision should be made based upon what is most convenient for the workspace administrator.
• C.Cross-region reads and writes can incur significant costs and latency; whenever possible, compute should be deployed in the same region the data is stored.
• D.Databricks leverages user workstations as the driver during interactive development; as such, users should always use a workspace deployed in a region they are physically near.
• E.Databricks notebooks send all executable code from the user's browser to virtual machines over the open internet; whenever possible, choosing a workspace region near the end users is the most secure.

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