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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:

Databricks Certified Data Engineer Professional Sample Questions:

1. A junior data engineer has manually configured a series of jobs using the Databricks Jobs UI.
Upon reviewing their work, the engineer realizes that they are listed as the "Owner" for each job.
They attempt to transfer "Owner" privileges to the "DevOps" group, but cannot successfully accomplish this task.
Which statement explains what is preventing this privilege transfer?

A) Databricks jobs must have exactly one owner; "Owner" privileges cannot be assigned to a group.
B) Only workspace administrators can grant "Owner" privileges to a group.
C) A user can only transfer job ownership to a group if they are also a member of that group.
D) Other than the default "admins" group, only individual users can be granted privileges on jobs.
E) The creator of a Databricks job will always have "Owner" privileges; this configuration cannot be changed.

2. When monitoring a complex workload, being able to see the query plan is critical to understanding what the workload is doing. Where can the visualization of the query plan be found?

A) In the Query Profiler, under the Stages tab
B) In the Query Profiler, under Query Source
C) In the Spark UI, under the SQL/DataFrame tab
D) In the Spart UI, under the Jobs tab

3. A Delta Lake table was created with the below query:

Realizing that the original query had a typographical error, the below code was executed:
ALTER TABLE prod.sales_by_stor RENAME TO prod.sales_by_store
Which result will occur after running the second command?

A) A new Delta transaction log Is created for the renamed table.
B) The table reference in the metastore is updated and no data is changed.
C) The table reference in the metastore is updated and all data files are moved.
D) All related files and metadata are dropped and recreated in a single ACID transaction.
E) The table name change is recorded in the Delta transaction log.

4. The view updates represents an incremental batch of all newly ingested data to be inserted or updated in the customers table.
The following logic is used to process these records.
MERGE INTO customers
USING (
SELECT updates.customer_id as merge_ey, updates .*
FROM updates
UNION ALL
SELECT NULL as merge_key, updates .*
FROM updates JOIN customers
ON updates.customer_id = customers.customer_id
WHERE customers.current = true AND updates.address <> customers.address ) staged_updates ON customers.customer_id = mergekey WHEN MATCHED AND customers. current = true AND customers.address <> staged_updates.address THEN UPDATE SET current = false, end_date = staged_updates.effective_date WHEN NOT MATCHED THEN INSERT (customer_id, address, current, effective_date, end_date) VALUES (staged_updates.customer_id, staged_updates.address, true, staged_updates.effective_date, null) Which statement describes this implementation?

A) The customers table is implemented as a Type 2 table; old values are maintained but marked as no longer current and new values are inserted.
B) The customers table is implemented as a Type 0 table; all writes are append only with no changes to existing values.
C) The customers table is implemented as a Type 1 table; old values are overwritten by new values and no history is maintained.
D) The customers table is implemented as a Type 2 table; old values are overwritten and new customers are appended.

5. A faulty IoT sensor in a factory reports a temperature of -500, causing the LDP pipeline to fail the expectation, which only allows values between -100 and 200 degrees Celsius. The data engineer would like to further analyze the faulty data to better understand the reason behind this. How should the data engineer resolve the faulty data while ensuring data quality standards are maintained?

A) Change the expectation action from fail to warn so that invalid records are included in the output and the pipeline does not fail.
B) Remove all expectations form the pipeline to prevent any future failures, regardless of data quality.
C) Ignore the error and simply re-run the pipeline, as Databricks will automatically skip the problematic record on the next run.
D) Fix the pipeline code and implement a quarantine logic to isolate the faulty data before re-running the pipeline.

Solutions: