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Databricks Certified Professional Data Engineer Exam Sample Questions (Q65-Q70):

NEW QUESTION # 65
The data governance team is reviewing code used for deleting records for compliance with GDPR. They note the following logic is used to delete records from the Delta Lake table namedusers.

Assuming thatuser_idis a unique identifying key and that contains all users that have requested deletion, which statement describes whether successfully executing the above logic guarantees that the records to be deleted are no longer accessible and why?

A. No; the Delta cache may return records from previous versions of the table until the cluster is restarted.
B. Yes; the Delta cache immediately updates to reflect the latest data files recorded to disk.
C. Yes; Delta Lake ACID guarantees provide assurance that the delete command succeeded fully and permanently purged these records.
D. No; the Delta Lake delete command only provides ACID guarantees when combined with the mergeinto command.
E. No; files containing deleted records may still be accessible with time travel until a vacuum command is used to remove invalidated data files.

Answer: E

Explanation:
Explanation
The code uses the DELETE FROM command to delete records from the users table that match a condition based on a join with another table called delete_requests, which contains all users that have requested deletion.
The DELETE FROM command deletes records from a Delta Lake table by creating a new version of the table that does not contain the deleted records. However, this does not guarantee that the records to be deleted are no longer accessible, because Delta Lake supports time travel, which allows querying previous versions of the table using a timestamp or version number. Therefore, files containing deleted records may still be accessible with time travel until a vacuum command is used to remove invalidated data files from physical storage.
Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Delete from a table" section; Databricks Documentation, under "Remove files no longer referenced by a Delta table" section.

NEW QUESTION # 66
Which of the following is a correct statement on how the data is organized in the storage when when managing a DELTA table?

A. All of the data is broken down into one or many parquet files, log files are broken down into one or many JSON files, and each transaction creates a new data file(s) and log file.
(Correct)
B. All of the data is stored into one parquet file, log files are broken down into one or many json files.
C. All of the data and log are stored in a single parquet file
D. All of the data is broken down into one or many parquet files, log file is removed once the transaction is committed.
E. All of the data is broken down into one or many parquet files, but the log file is stored as a single json file, and every transaction creates a new data file(s) and log file gets appended.

Answer: A

Explanation:
Explanation
Answer is
All of the data is broken down into one or many parquet files, log files are broken down into one or many json files, and each transaction creates a new data file(s) and log file.
here is sample layout of how DELTA table might look,

NEW QUESTION # 67
Which of the following techniques structured streaming uses to ensure recovery of failures during stream processing?

A. Delta time travel
B. Checkpointing and Idempotent sinks
C. Checkpointing and write-ahead logging
D. Checkpointing and Watermarking
E. Write ahead logging and watermarking
F. The stream will failover to available nodes in the cluster

Answer: C

Explanation:
Explanation
The answer is Checkpointing and write-ahead logging.
Structured Streaming uses checkpointing and write-ahead logs to record the offset range of data being processed during each trigger interval.

NEW QUESTION # 68
A data analyst has noticed that their Databricks SQL queries are running too slowly. They claim that this issue
is affecting all of their sequentially run queries. They ask the data engineering team for help. The data
engineering team notices that each of the queries uses the same SQL endpoint, but the SQL endpoint is not
used by any other user.
Which of the following approaches can the data engineering team use to improve the latency of the data
analyst's queries?

A. They can turn on the Auto Stop feature for the SQL endpoint
B. They can turn on the Serverless feature for the SQL endpoint
C. They can increase the cluster size of the SQL endpoint
D. They can turn on the Serverless feature for the SQL endpoint and change the Spot In-stance Policy to
"Reliability Optimized"
E. They can increase the maximum bound of the SQL endpoint's scaling range

Answer: C

NEW QUESTION # 69
A data team is automating a daily multi-task ETL pipeline in Databricks. The pipeline includes a notebook for ingesting raw data, a Python wheel task for data transformation, and a SQL query to update aggregates. They want to trigger the pipeline programmatically and see previous runs in the GUI. They need to ensure tasks are retried on failure and stakeholders are notified by email if any task fails.
Which two approaches will meet these requirements? (Choose 2 answers)

A. Create a multi-task job using the UI, Databricks Asset Bundles (DABs), or the Jobs REST API (/jobs/create) with notebook, Python wheel, and SQL tasks. Configure task-level retries and email notifications in the job definition.
B. Create a single orchestrator notebook that calls each step with dbutils.notebook.run(), defining a job for that notebook and configuring retries and notifications at the notebook level.
C. Use Databricks Asset Bundles (DABs) to deploy the workflow, then trigger individual tasks directly by referencing each task's notebook or script path in the workspace.
D. Use the REST API endpoint /jobs/runs/submit to trigger each task individually as separate job runs and implement retries using custom logic in the orchestrator.
E. Trigger the job programmatically using the Databricks Jobs REST API (/jobs/run-now), the CLI (databricks jobs run-now), or one of the Databricks SDKs.

Answer: A,E

Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
Databricks Jobs supports defining multi-task workflows that include notebooks, SQL statements, and Python wheel tasks. These can be configured with retry policies, dependency chains, and failure notifications. The correct practice, as stated in the documentation, is to use the Jobs REST API (/jobs/create) or Databricks Asset Bundles to define multi-task jobs, and then trigger them programmatically using /jobs/run-now, CLI, or SDK. This allows the team to maintain full job history, handle retries automatically, and receive alerts via configured email notifications. Using /jobs/runs/submit creates one-off ad hoc runs without maintaining dependency visibility. Therefore, options B and C together satisfy the operational, automation, and governance requirements.

NEW QUESTION # 70
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