Databricks-Certified-Data-Engineer-Professional Certified Questions & Databricks-Certified-Data-Engineer-Professional Question Explanations

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

Section	Weight	Objectives
Topic 1: Monitoring and Troubleshooting	16%	- Troubleshooting - Monitoring - Performance Optimization
Topic 2: Data Processing	28%	- Spark SQL - Structured Streaming - ETL Pipelines - Data Transformation
Topic 3: Data Modeling and Storage	20%	- Data Modeling - Storage Optimization - File Formats
Topic 4: Data Quality and Governance	12%	- Governance - Data Lineage - Data Quality
Topic 5: Databricks Lakehouse Platform	24%	- Data Management - Delta Lake - Lakehouse Architecture - Unity Catalog

>> Databricks-Certified-Data-Engineer-Professional Certified Questions <<

Pass Guaranteed 2026 Databricks-Certified-Data-Engineer-Professional: Databricks Certified Data Engineer Professional Exam High Hit-Rate Certified Questions

Two Databricks Databricks-Certified-Data-Engineer-Professional practice tests of Actual4Labs (desktop and web-based) create an actual test scenario and give you a Databricks-Certified-Data-Engineer-Professional real exam feeling. These Databricks-Certified-Data-Engineer-Professional Practice Tests also help you gauge your Databricks Certification Exams preparation and identify areas where improvements are necessary.

Databricks Certified Data Engineer Professional Exam Sample Questions (Q137-Q142):

NEW QUESTION # 137
A data ingestion task requires a one-TB JSON dataset to be written out to Parquet with a target part-file size of 512 MB. Because Parquet is being used instead of Delta Lake, built-in file-sizing features such as Auto-Optimize & Auto-Compaction cannot be used.
Which strategy will yield the best performance without shuffling data?

A. Set spark.sql.shuffle.partitions to 512, ingest the data, execute the narrow transformations, and then write to parquet.
B. Set spark.sql.shuffle.partitions to 2,048 partitions (1TB*1024*1024/512), ingest the data, execute the narrow transformations, optimize the data by sorting it (which automatically repartitions the data), and then write to parquet.
C. Ingest the data, execute the narrow transformations, repartition to 2,048 partitions (1TB*
1024*1024/512), and then write to parquet.
D. Set spark.sql.files.maxPartitionBytes to 512 MB, ingest the data, execute the narrow transformations, and then write to parquet.
E. Set spark.sql.adaptive.advisoryPartitionSizeInBytes to 512 MB bytes, ingest the data, execute the narrow transformations, coalesce to 2,048 partitions (1TB*1024*1024/512), and then write to parquet.

Answer: B

Explanation:
The key to efficiently converting a large JSON dataset to Parquet files of a specific size without shuffling data lies in controlling the size of the output files directly. Setting spark.sql.files.maxPartitionBytes to 512 MB configures Spark to process data in chunks of 512 MB. This setting directly influences the size of the part-files in the output, aligning with the target file size.
Narrow transformations (which do not involve shuffling data across partitions) can then be applied to this data.
Writing the data out to Parquet will result in files that are approximately the size specified by spark.sql.files.maxPartitionBytes, in this case, 512 MB. The other options involve unnecessary shuffles or repartitions (B, C, D) or an incorrect setting for this specific requirement (E).

NEW QUESTION # 138
Each configuration below is identical to the extent that each cluster has 400 GB total of RAM, 160 total cores and only one Executor per VM.
Given a job with at least one wide transformation, which of the following cluster configurations will result in maximum performance?

A. Total VMs: 2
200 GB per Executor
80 Cores / Executor
B. Total VMs: 1
400 GB per Executor
160 Cores / Executor
C. Total VMs: 4
100 GB per Executor
40 Cores/Executor
D. Total VMs: 8
50 GB per Executor
20 Cores / Executor

Answer: B

Explanation:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from
https://docs.databricks.com/en/clusters/cluster-config-best-practices.html

NEW QUESTION # 139
A table is registered with the following code:
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Both users and orders are Delta Lake tables. Which statement describes the results of querying recent_orders?

A. All logic will execute when the table is defined and store the result of joining tables to the DBFS; this stored data will be returned when the table is queried.
B. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.
C. Results will be computed and cached when the table is defined; these cached results will incrementally update as new records are inserted into source tables.
D. The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.
E. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.

Answer: A

Explanation:
Table is created and data of join will be stored on DBFS and it will be returned on query time.

NEW QUESTION # 140
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. Use the LIMIT clause to run a subset of the query and compare execution times with the full dataset.
C. Check the query's execution time in the Jobs UI and correlate it with cluster resource utilization metrics.
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 # 141
What statement is true regarding the retention of job run history?

A. It is retained for 90 days or until the run-id is re-used through custom run configuration
B. t is retained for 60 days, during which you can export notebook run results to HTML
C. It is retained for 30 days, during which time you can deliver job run logs to DBFS or S3
D. It is retained until you export or delete job run logs
E. It is retained for 60 days, after which logs are archived

Answer: B

Explanation:
https://docs.databricks.com/en/workflows/jobs/monitor-job-runs.html

NEW QUESTION # 142
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Databricks-Certified-Data-Engineer-Professional Question Explanations: https://www.actual4labs.com/Databricks/Databricks-Certified-Data-Engineer-Professional-actual-exam-dumps.html