Databricks-Certified-Professional-Data-Engineer Exam - Databricks-Certified-Professional-Data-Engineer German

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Databricks Certified Professional Data Engineer Exam Databricks-Certified-Professional-Data-Engineer Prüfungsfragen mit Lösungen (Q124-Q129):

124. Frage
You have configured AUTO LOADER to process incoming IOT data from cloud object storage every 15 mins, recently a change was made to the notebook code to update the processing logic but the team later realized that the notebook was failing for the last 24 hours, what steps team needs to take to reprocess the data that was not loaded after the notebook was corrected?

A. Move the files that were not processed to another location and manually copy the files into the ingestion path to reprocess them
B. Autoloader automatically re-processes data that was not loaded
C. Enable back_fill = TRUE to reprocess the data
D. Manually re-load the data
E. Delete the checkpoint folder and run the autoloader again

Antwort: B

Begründung:
Explanation
The answer is,
Autoloader automatically re-processes data that was not loaded using the checkpoint.

125. Frage
Where in the Spark UI can one diagnose a performance problem induced by not leveraging predicate push-down?

A. In the Executor's log file, by grippingfor "predicate push-down"
B. In the Storage Detail screen, by noting which RDDs are not stored on disk
C. In the Delta Lake transaction log. by noting the column statistics
D. In the Query Detail screen, by interpreting the Physical Plan
E. In the Stage's Detail screen, in the Completed Stages table, by noting the size of data read from the Input column

Antwort: D

Begründung:
Explanation
This is the correct answer because it is where in the Spark UI one can diagnose a performance problem induced by not leveraging predicate push-down. Predicate push-down is an optimization technique that allows filtering data at the source before loading it into memory or processing it further. This can improve performance and reduce I/O costs by avoiding reading unnecessary data. To leverage predicate push-down, one should use supported data sources and formats, such as Delta Lake, Parquet, or JDBC, and use filter expressions that can be pushed down to the source. To diagnose a performance problem induced by not leveraging predicate push-down, one can use the Spark UI to access the Query Detail screen, which showsinformation about a SQL query executed on a Spark cluster. The Query Detail screen includes the Physical Plan, which is the actual plan executed by Spark to perform the query. The Physical Plan shows the physical operators used by Spark, such as Scan, Filter, Project, or Aggregate, and their input and output statistics, such as rows and bytes. By interpreting the Physical Plan, one can see if the filter expressions are pushed down to the source or not, and how much data is read or processed by each operator. Verified References: [Databricks Certified Data Engineer Professional], under "Spark Core" section; Databricks Documentation, under "Predicate pushdown" section; Databricks Documentation, under "Query detail page" section.

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

Antwort: B

Begründung:
The code creates a view called email_ltv that selects the email and ltv columns from a table called user_ltv, which has the following schema: email STRING, age INT, ltv INT. The code also uses the CASE WHEN expression to replace the email values with the string "REDACTED" if the user is not a member of the marketing group. The user who executes the query is not a member of the marketing group, so they will only see the email and ltv columns, and the email column will contain the string "REDACTED" in each row. Verified Reference: [Databricks Certified Data Engineer Professional], under "Lakehouse" section; Databricks Documentation, under "CASE expression" section.

127. Frage
A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on Task A.
If task A fails during a scheduled run, which statement describes the results of this run?

A. Tasks B and C will be skipped; task A will not commit any changes because of stage failure.
B. Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task A failed, all commits will be rolled back automatically.
C. Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until all tasks have successfully been completed.
D. Tasks B and C will be skipped; some logic expressed in task A may have been committed before task failure.
E. Tasks B and C will attempt to run as configured; any changes made in task A will be rolled back due to task failure.

Antwort: D

Begründung:
When a Databricks job runs multiple tasks with dependencies, the tasks are executed in a dependency graph.
If a task fails, the downstream tasks that depend on it are skipped and marked as Upstream failed. However, the failed task may have already committed some changes to the Lakehouse before the failure occurred, and those changes are not rolled back automatically. Therefore, the job run may result in a partial update of the Lakehouse. To avoid this, you can use the transactional writes feature of Delta Lake to ensure that the changes are only committed when the entire job run succeeds. Alternatively, you can use the Run if condition to configure tasks to run even when some or all of their dependencies have failed, allowing your job to recover from failures and continue running. References:
* transactional writes: https://docs.databricks.com/delta/delta-intro.html#transactional-writes
* Run if: https://docs.databricks.com/en/workflows/jobs/conditional-tasks.html

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

Antwort: E

Begründung:
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.

129. Frage
......

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