A user wants to use DLT expectations to validate that a derived table report contains all records from the source, included in the table validation_copy.
The user attempts and fails to accomplish this by adding an expectation to the report table definition.

Which approach would allow using DLT expectations to validate all expected records are present in this table?

• A. Define a view that performs a left outer join on validation_copy and report, and reference this view in DLT expectations for the report table
• B. Define a function that performs a left outer join on validation_copy and report and report, and check against the result in a DLT expectation for the report table
• C. Define a temporary table that perform a left outer join on validation_copy and report, and define an expectation that no report key values are null
• D. Define a SQL UDF that performs a left outer join on two tables, and check if this returns null values for report key values in a DLT expectation for the report table.

An upstream system is emitting change data capture (CDC) logs that are being written to a cloud object storage directory. Each record in the log indicates the change type (insert, update, or delete) and the values for each field after the change. The source table has a primary key identified by the field pk_id.
For analytical purposes, only the most recent value for each record needs to be recorded in the target Delta Lake table in the Lakehouse. The Databricks job to ingest these records occurs once per hour, but each individual record may have changed multiple times over the course of an hour.
Which solution meets these requirements?

• A. Deduplicate records in each batch by pk_id and overwrite the target table.
• B. Use Delta Lake's change data feed to automatically process CDC data from an external system, propagating all changes to all dependent tables in the Lakehouse.
• C. Use MERGE INTO to insert, update, or delete the most recent entry for each pk_id into a table, then propagate all changes throughout the system.
• D. Iterate through an ordered set of changes to the table, applying each in turn to create the current state of the table, (insert, update, delete), timestamp of change, and the values.

A data engineer is using the AUTO CDC API in Lakeflow Spark Declarative Pipeline to propagate deletions from a source table (orders_source) to a target table (orders_target). The source has Change Data Feed (CDF) enabled, but some delete events arrive out of order due to upstream delays. How does the AUTO CDC API internally ensure deletions are applied correctly despite out-of-order events?

• A. It uses sequence_by to order events and retains tombstones for deleted rows until older sequences are processed.
• B. It runs VACUUM on the target table to purge conflicting records.
• C. It manually sorts incoming events by timestamp before applying changes.
• D. It ignores deletions if they arrive after updates for the same key.

A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Events are recorded once per minute per device.
Streaming DataFrame df has the following schema:
"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"
Code block:

Choose the response that correctly fills in the blank within the code block to complete this task.

• A. window("event_time", "10 minutes").alias("time")
• B. lag("event_time", "10 minutes").alias("time")
• C. window("event_time", "5 minutes").alias("time")
• D. "event_time"
• E. to_interval("event_time", "5 minutes").alias("time")