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A data architect has heard about lake's built-in versioning and time travel capabilities. For auditing purposes they have a requirement to maintain a full of all valid street addresses as they appear in the customers table.
The architect is interested in implementing a Type 1 table, overwriting existing records with new values and relying on Delta Lake time travel to support long-term auditing. A data engineer on the project feels that a Type 2 table will provide better performance and scalability. Which piece of information is critical to this decision?

• A. Delta Lake time travel cannot be used to query previous versions of these tables because Type 1 changes modify data files in place.
• B. Shallow clones can be combined with Type 1 tables to accelerate historic queries for long-term versioning.
• C. Data corruption can occur if a query fails in a partially completed state because Type 2 tables requires setting multiple fields in a single update.
• D. Delta Lake only supports Type 0 tables; once records are inserted to a Delta Lake table, they cannot be modified.
• E. Delta Lake time travel does not scale well in cost or latency to provide a long-term versioning solution.

A data engineer is performing a join operation to combine values from a static userlookup table with a streaming DataFrame streamingDF.
Which code block attempts to perform an invalid stream-static join?

• A. userLookup.join(streamingDF, ["userid"], how="inner")
• B. streamingDF.join(userLookup, ["user_id"], how="outer")
• C. streamingDF.join(userLookup, ["userid"], how="inner")
• D. userLookup.join(streamingDF, ["user_id"], how="right")
• E. streamingDF.join(userLookup, ["user_id"], how="left")

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

Spill occurs as a result of executing various wide transformations. However, diagnosing spill requires one to proactively look for key indicators.
Where in the Spark UI are two of the primary indicators that a partition is spilling to disk?

• A. Executor's detail screen and Executor's log files
• B. Stage's detail screen and Query's detail screen
• C. Query's detail screen and Job's detail screen
• D. Stage's detail screen and Executor's log files
• E. Driver's and Executor's log files