A data engineer wants to automate job monitoring and recovery in Databricks using the Jobs API.
They need to list all jobs, identify a failed job, and rerun it. Which sequence of API actions should the data engineer perform?

• A. Use the jobs/list endpoint to list jobs, check job run statuses with jobs/runs/list, and rerun a failed job using jobs/run-now.
• B. Use the jobs/list endpoint to list jobs, then use the jobs/create endpoint to create a new job, and run the new job using jobs/run-now.
• C. Use the jobs/get endpoint to retrieve job details, then use jobs/update to rerun failed jobs.
• D. Use the jobs/cancel endpoint to remove failed jobs, then recreate them with jobs/create and run the new ones.

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. 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.
• 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. 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.

A nightly batch job is configured to ingest all data files from a cloud object storage container where records are stored in a nested directory structure YYYY/MM/DD. The data for each date represents all records that were processed by the source system on that date, noting that some records may be delayed as they await moderator approval. Each entry represents a user review of a product and has the following schema:
user_id STRING, review_id BIGINT, product_id BIGINT, review_timestamp TIMESTAMP, review_text STRING The ingestion job is configured to append all data for the previous date to a target table reviews_raw with an identical schema to the source system. The next step in the pipeline is a batch write to propagate all new records inserted into reviews_raw to a table where data is fully deduplicated, validated, and enriched.
Which solution minimizes the compute costs to propagate this batch of data?

• A. Configure a Structured Streaming read against the reviews_raw table using the trigger once execution mode to process new records as a batch job.
• B. Filter all records in the reviews_raw table based on the review_timestamp; batch append those records produced in the last 48 hours.
• C. Perform a batch read on the reviews_raw table and perform an insert-only merge using the natural composite key user_id, review_id, product_id, review_timestamp.
• D. Reprocess all records in reviews_raw and overwrite the next table in the pipeline.
• E. Use Delta Lake version history to get the difference between the latest version of reviews_raw and one version prior, then write these records to the next table.

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 temporary table that perform a left outer join on validation_copy and report, and define an expectation that no report key values are null
• B. 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
• C. 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
• 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.