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

Section	Weight	Objectives
Data Modeling and Storage	20%	- File Formats - Storage Optimization - Data Modeling
Monitoring and Troubleshooting	16%	- Monitoring - Performance Optimization - Troubleshooting
Data Processing	28%	- Data Transformation - Spark SQL - ETL Pipelines - Structured Streaming
Databricks Lakehouse Platform	24%	- Lakehouse Architecture - Data Management - Unity Catalog - Delta Lake
Data Quality and Governance	12%	- Governance - Data Lineage - Data Quality

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q10-Q15):

NEW QUESTION # 10
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 named users.

Assuming that user_id is a unique identifying key and that delete_requests 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 merge into 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:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from 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.

NEW QUESTION # 11
A data engineer is troubleshooting a slow-running Delta Lake query on Databricks SQL involves complex joins and large datasets. They need to identify whether the root cause is related to poor data skipping, inefficient join strategies, or excessive data shuffling. Which approach should identify the specific bottlenecks using native Databricks tools?

A. Analyze the Top Operators panel in the Query Profile to identify high-cost operations like BroadcastNestedLoopJoin
B. Check the query's execution time in the Jobs UI and correlate it with cluster resource utilization metrics.
C. Use the LIMIT clause to run a subset of the query and compare execution times with the full dataset.
D. Enable the EXPLAIN command to review the parsed logical plan and manually estimate shuffle sizes.

Answer: A

Explanation:
The Query Profile's Top Operators panel surfaces the most expensive operators in the query execution, making it possible to directly identify bottlenecks such as inefficient join strategies, poor data skipping, or excessive shuffling. This native visualization highlights where time and resources are spent, enabling precise root-cause analysis for slow-running queries.

NEW QUESTION # 12
A data engineer is using Auto Loader to read incoming JSON data as it arrives. They have configured Auto Loader to quarantine invalid JSON records but notice that over time, some records are being quarantined even though they are well-formed JSON.
The code snippet is:
df = (spark.readStream
.format("cloudFiles")
.option("cloudFiles.format", "json")
.option("badRecordsPath", "/tmp/somewhere/badRecordsPath")
.schema("a int, b int")
.load("/Volumes/catalog/schema/raw_data/"))
What is the cause of the missing data?

A. At some point, the upstream data provider switched everything to multi-line JSON.
B. The engineer forgot to set the option "cloudFiles.quarantineMode" = "rescue".
C. The source data is valid JSON but does not conform to the defined schema in some way.
D. The badRecordsPath location is accumulating many small files.

Answer: C

Explanation:
Auto Loader quarantines records that cannot be parsed according to the specified schema, even if the JSON itself is well formed. If incoming records contain additional fields, missing fields, or incompatible data types compared to the declared schema, they are treated as invalid and routed to the bad records path.

NEW QUESTION # 13
A user new to Databricks is trying to troubleshoot long execution times for some pipeline logic they are working on. Presently, the user is executing code cell-by-cell, using display() calls to confirm code is producing the logically correct results as new transformations are added to an operation. To get a measure of average time to execute, the user is running each cell multiple times interactively.
Which of the following adjustments will get a more accurate measure of how code is likely to perform in production?

A. Production code development should only be done using an IDE; executing code against a local build of open source Spark and Delta Lake will provide the most accurate benchmarks for how code will perform in production.
B. The Jobs Ul should be leveraged to occasionally run the notebook as a job and track execution time during incremental code development because Photon can only be enabled on clusters launched for scheduled jobs.
C. The only way to meaningfully troubleshoot code execution times in development notebooks Is to use production-sized data and production-sized clusters with Run All execution.
D. Scala is the only language that can be accurately tested using interactive notebooks; because the best performance is achieved by using Scala code compiled to JARs. all PySpark and Spark SQL logic should be refactored.
E. Calling display () forces a job to trigger, while many transformations will only add to the logical query plan; because of caching, repeated execution of the same logic does not provide meaningful results.

Answer: C

NEW QUESTION # 14
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 job_id and include_history parameters
C. Call/api/2.1/jobs/runs/get with the run_id and include_history parameters
D. Call/api/2.1/jobs/runs/list with the run_id and include_history parameters

Answer: A

Explanation:
To retrieve information about the latest job runs along with their repair history, you use the jobs/runs/list endpoint with the job_id and include_history=true. This endpoint returns a list of runs for a specific job, including details about retries and repair attempts, which is not available via runs/get that retrieves a single run by run_id.

NEW QUESTION # 15
......

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