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

A task orchestrator has been configured to run two hourly tasks. First, an outside system writes Parquet data to a directory mounted at /mnt/raw_orders/. After this data is written, a Databricks job containing the following code is executed:

Assume that the fields customer_id and order_id serve as a composite key to uniquely identify each order, and that the time field indicates when the record was queued in the source system.
If the upstream system is known to occasionally enqueue duplicate entries for a single order hours apart, which statement is correct?

• A. All records will be held in the state store for 2 hours before being deduplicated and committed to the orders table.
• B. Duplicate records enqueued more than 2 hours apart may be retained and the orders table may contain duplicate records with the same customer_id and order_id.
• C. Duplicate records arriving more than 2 hours apart will be dropped, but duplicates that arrive in the same batch may both be written to the orders table.
• D. The orders table will contain only the most recent 2 hours of records and no duplicates will be present.
• E. The orders table will not contain duplicates, but records arriving more than 2 hours late will be ignored and missing from the table.

A view is registered with the following code:

Both users and orders are Delta Lake tables.
Which statement describes the results of querying recent_orders?

• A. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.
• B. Results will be computed and cached when the view is defined; these cached results will incrementally update as new records are inserted into source tables.
• C. All logic will execute when the view is defined and store the result of joining tables to the DBFS; this stored data will be returned when the view is queried.
• D. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.

A team of data engineer are adding tables to a DLT pipeline that contain repetitive expectations for many of the same data quality checks.
One member of the team suggests reusing these data quality rules across all tables defined for this pipeline.
What approach would allow them to do this?

• A. Maintain data quality rules in a separate Databricks notebook that each DLT notebook of file.
• B. Add data quality constraints to tables in this pipeline using an external job with access to pipeline configuration files.
• C. Maintain data quality rules in a Delta table outside of this pipeline's target schema, providing the schema name as a pipeline parameter.
• D. Use global Python variables to make expectations visible across DLT notebooks included in the same pipeline.