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A table named user_ltv is being used to create a view that will be used by data analysts on various teams. Users in the workspace are configured into groups, which are used for setting up data access using ACLs.
The user_ltv table has the following schema:
email STRING, age INT, ltv INT
The following view definition is executed:

An analyst who is not a member of the marketing group executes the following query:
SELECT * FROM email_ltv
Which statement describes the results returned by this query?

• A. Three columns will be returned, but one column will be named "redacted" and contain only null values.
• B. Only the email and itv columns will be returned; the email column will contain all null values.
• C. The email, age. and ltv columns will be returned with the values in user ltv.
• D. Only the email and ltv columns will be returned; the email column will contain the string
  "REDACTED" in each row.
• E. The email and ltv columns will be returned with the values in user itv.

The data governance team is reviewing user for deleting records for compliance with GDPR. The following logic has been implemented to propagate deleted requests from the user_lookup table to the user aggregate table.

Assuming that user_id is a unique identifying key and that all users have requested deletion have been removed from the user_lookup table, which statement describes whether successfully executing the above logic guarantees that the records to be deleted from the user_aggregates table are no longer accessible and why?

• A. Yes; Delta Lake ACID guarantees provide assurance that the DELETE command successed fully and permanently purged these records.
• B. Yes; the change data feed uses foreign keys to ensure delete consistency throughout the Lakehouse.
• C. No; the Delta Lake DELETE command only provides ACID guarantees when combined with the MERGE INTO command
• D. No; the change data feed only tracks inserts and updates not deleted records.
• E. No; files containing deleted records may still be accessible with time travel until a BACUM command is used to remove invalidated data files.

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. Reprocess all records in reviews_raw and overwrite the next table in the pipeline.
• 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. 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.
• D. 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.
• E. Configure a Structured Streaming read against the reviews_raw table using the trigger once execution mode to process new records as a batch job.

A data engineer needs to create an application that will collect information about the latest job run including the repair history. How should the data engineer format the request?

• A. Call/api/2.1/jobs/runs/list with the job_id and include_history parameters
• B. Call/api/2.1/jobs/runs/get with the run_id and include_history parameters
• C. Call/api/2.1/jobs/runs/get with the job_id and include_history parameters
• D. Call/api/2.1/jobs/runs/list with the run_id and include_history parameters

All records from an Apache Kafka producer are being ingested into a single Delta Lake table with the following schema:
key BINARY, value BINARY, topic STRING, partition LONG, offset LONG, timestamp LONG There are 5 unique topics being ingested. Only the "registration" topic contains Personal Identifiable Information (PII). The company wishes to restrict access to PII. The company also wishes to only retain records containing PII in this table for 14 days after initial ingestion.
However, for non-PII information, it would like to retain these records indefinitely.
Which of the following solutions meets the requirements?

• A. Data should be partitioned by the registration field, allowing ACLs and delete statements to be set for the PII directory.
• B. Separate object storage containers should be specified based on the partition field, allowing isolation at the storage level.
• C. All data should be deleted biweekly; Delta Lake's time travel functionality should be leveraged to maintain a history of non-PII information.
• D. Because the value field is stored as binary data, this information is not considered PII and no special precautions should be taken.
• E. Data should be partitioned by the topic field, allowing ACLs and delete statements to leverage partition boundaries.