The downstream consumers of a Delta Lake table have been complaining about data quality issues impacting performance in their applications. Specifically, they have complained that invalid latitude and longitude values in the activity_details table have been breaking their ability to use other geolocation processes.
A junior engineer has written the following code to add CHECK constraints to the Delta Lake table:

A senior engineer has confirmed the above logic is correct and the valid ranges for latitude and longitude are provided, but the code fails when executed.
Which statement explains the cause of this failure?

• A. Because another team uses this table to support a frequently running application, two-phase locking is preventing the operation from committing.
• B. The activity details table already contains records; CHECK constraints can only be added prior to inserting values into a table.
• C. The current table schema does not contain the field valid coordinates; schema evolution will need to be enabled before altering the table to add a constraint.
• D. The activity details table already exists; CHECK constraints can only be added during initial table creation.
• E. The activity details table already contains records that violate the constraints; all existing data must pass CHECK constraints in order to add them to an existing table.

A data engineer is attempting to execute the following PySpark code:
df = spark.read.table("sales")
result = df.groupBy("region").agg(sum("revenue"))
However, upon inspecting the execution plan and profiling the Spark job, they observe excessive data shuffling during the aggregation phase.
Which technique should be applied to reduce shuffling during the groupBy aggregation operation?

• A. Use broadcast join.
• B. Caching the DataFrame df.
• C. Repartition by region before aggregation.
• D. Use coalesce() after the aggregation.

The data engineering team has configured a Databricks SQL query and alert to monitor the values in a Delta Lake table. The recent_sensor_recordings table contains an identifying sensor_id alongside the timestamp and temperature for the most recent 5 minutes of recordings.
The below query is used to create the alert:

The query is set to refresh each minute and always completes in less than 10 seconds. The alert is set to trigger when mean (temperature) > 120. Notifications are triggered to be sent at most every 1 minute.
If this alert raises notifications for 3 consecutive minutes and then stops, which statement must be true?

• A. The maximum temperature recording for at least one sensor exceeded 120 on three consecutive executions of the query
• B. The recent_sensor_recordingstable was unresponsive for three consecutive runs of the query
• C. The average temperature recordings for at least one sensor exceeded 120 on three consecutive executions of the query
• D. The total average temperature across all sensors exceeded 120 on three consecutive executions of the query
• E. The source query failed to update properly for three consecutive minutes and then restarted

Which statement describes the default execution mode for Databricks Auto Loader?

• A. Cloud vendor-specific queue storage and notification services are configured to track newly arriving files; the target table is materialized by directly querying all valid files in the source directory.
• B. New files are identified by listing the input directory; the target table is materialized by directory querying all valid files in the source directory.
• C. Webhook trigger Databricks job to run anytime new data arrives in a source directory; new data automatically merged into target tables using rules inferred from the data.
• D. New files are identified by listing the input directory; new files are incrementally and idempotently loaded into the target Delta Lake table.
• E. Cloud vendor-specific queue storage and notification services are configured to track newly arriving files; new files are incrementally and impotently into the target Delta Lake table.

A Data engineer wants to run unit's tests using common Python testing frameworks on python functions defined across several Databricks notebooks currently used in production. How can the data engineer run unit tests against function that work with data in production?

• A. Define and unit test functions using Files in Repos
• B. Run unit tests against non-production data that closely mirrors production
• C. Define units test and functions within the same notebook
• D. Define and import unit test functions from a separate Databricks notebook