Practice Databricks-Certified-Data-Engineer-Professional Test Engine - Certification Databricks-Certified-Data-Engineer-Professional Exam Infor

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

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

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q26-Q31):

NEW QUESTION # 26
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. Ingest the data, execute the narrow transformations, repartition to 2,048 partitions (1TB*
1024*1024/512), 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. Set spark.sql.shuffle.partitions to 512, ingest the data, execute the narrow transformations, and then write to parquet.
D. 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.
E. Set spark.sql.files.maxPartitionBytes to 512 MB, ingest the data, execute the narrow transformations, 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 # 27
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?
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from

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

Answer: C

Explanation:
A job cannot have more than one owner. A job cannot have a group as an owner.

NEW QUESTION # 28
A data engineer is testing a collection of mathematical functions, one of which calculates the area under a curve as described by another function.
assert(myIntegrate(lambda x: x*x, 0, 3) [0] == 9)
Which kind of the test does the above line exemplify?

A. Integration
B. Manual
C. Unit
D. End-to-end
E. functional

Answer: C

Explanation:
A unit test is designed to verify the correctness of a small, isolated piece of code, typically a single function. Testing a mathematical function that calculates the area under a curve is an example of a unit test because it is testing a specific, individual function to ensure it operates as expected.

NEW QUESTION # 29
Which approach demonstrates a modular and testable way to use DataFrame transform for ETL code in PySpark?

Answer: D

Explanation:
Using DataFrame.transform with a pure transformation function promotes modular, reusable, and easily testable ETL logic. Each transformation is encapsulated as a standalone function, can be independently unit tested, and composed cleanly in a pipeline without coupling to orchestration or class state.

NEW QUESTION # 30
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 an extremely long-running job for which completion must be guaranteed, which cluster configuration will be able to guarantee completion of the job in light of one or more VM failures?

A. - Total VMs: 8
- 50 GB per Executor
- 20 Cores / Executor
B. - Total VMs: 16
- 25 GB per Executor
- 10 Cores / Executor
C. - Total VMs: 4
- 100 GB per Executor
- 40 Cores / Executor
D. - Total VMs: 1
- 400 GB per Executor
- 160 Cores/Executor
E. - Total VMs: 2
- 200 GB per Executor
- 80 Cores / Executor

Answer: B

NEW QUESTION # 31
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