Top Databricks Databricks-Certified-Professional-Data-Engineer Questions & Certification Databricks-Certified-Professional-Data-Engineer Questions

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Databricks Databricks-Certified-Professional-Data-Engineer Exam Overview:

Certification Vendor:	Databricks
Exam Name:	Databricks Certified Professional Data Engineer Exam
Exam Number:	Databricks-Certified-Professional-Data-Engineer
Certificate Validity Period:	2 years
Passing Score:	70%
Available Languages:	English
Real Exam Qty:	60
Related Certifications:	Databricks Certified Data Analyst Associate Databricks Certified Associate Developer
Exam Duration:	90 minutes
Exam Format:	Multiple Select, Multiple Choice
Exam Price:	$200 USD
Sample Questions:	Databricks Databricks-Certified-Professional-Data-Engineer Sample Questions
Exam Way:	Online proctored exam (Pearson VUE)
Pre Condition:	Recommended: 6+ months of hands-on experience with Databricks and data engineering concepts; familiarity with Python or Scala and SQL is strongly recommended
Official Syllabus URL:	https://www.databricks.com/learn/certification/professional-data-engineer

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

Databricks Databricks-Certified-Professional-Data-Engineer Questions Tips For Better Preparation

The Databricks Certified Professional Data Engineer Exam (Databricks-Certified-Professional-Data-Engineer) certification exam is one of the top-rated career advancement certifications in the market. This Databricks Certified Professional Data Engineer Exam (Databricks-Certified-Professional-Data-Engineer) exam dumps have been inspiring beginners and experienced professionals since its beginning. There are several personal and professional benefits that you can gain after passing the Databricks Databricks-Certified-Professional-Data-Engineer Exam. The validation of expertise, more career opportunities, salary enhancement, instant promotion, and membership of Databricks certified professional community.

To take the Databricks Databricks-Certified-Professional-Data-Engineer exam, candidates must first complete the Databricks Certified Associate Data Analyst and Databricks Certified Associate Data Engineer exams. These exams provide a foundation of knowledge and skills that are necessary to pass the professional-level exam. Candidates must also have experience working with Databricks and be familiar with its various features and capabilities.

Databricks Certified Professional Data Engineer certification exam is a rigorous and challenging exam that requires a deep understanding of data engineering concepts and the Databricks platform. Candidates must have a strong foundation in computer science and data engineering, as well as practical experience using the Databricks platform. Databricks-Certified-Professional-Data-Engineer Exam consists of multiple-choice questions and hands-on exercises that test a candidate's ability to design, build, and maintain data pipelines using the Databricks platform.

Databricks is a cloud-based platform that provides data engineering, machine learning, and analytics services. The Databricks Certified Professional Data Engineer (DCPDE) exam is designed to test the skills and knowledge of data engineers who are responsible for building data pipelines, managing data infrastructure, and optimizing data processing workflows on the Databricks platform.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q175-Q180):

NEW QUESTION # 175
What steps need to be taken to set up a DELTA LIVE PIPELINE as a job using the workspace UI?

A. Use Pipeline creation UI, select a new pipeline and job cluster
B. DELTA LIVE TABLES do not support job cluster
C. Select Workflows UI and Delta live tables tab, under task type select Delta live tables pipeline and select the notebook
D. Select Workflows UI and Delta live tables tab, under task type select Delta live tables pipeline and select the pipeline JSON file

Answer: C

Explanation:
Explanation
The answer is,
Select Workflows UI and Delta live tables tab, under task type select Delta live tables pipeline and select the notebook.
Create a pipeline
To create a new pipeline using the Delta Live Tables notebook:
1.Click Workflows in the sidebar, click the Delta Live Tables tab, and click Create Pipeline.
2.Give the pipeline a name and click to select a notebook.
3.Optionally enter a storage location for output data from the pipeline. The system uses a de-fault location if you leave Storage Location empty.
4.Select Triggered for Pipeline Mode.
5.Click Create.
The system displays the Pipeline Details page after you click Create. You can also access your pipeline by clicking the pipeline name in the Delta Live Tables tab.

NEW QUESTION # 176
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.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

NEW QUESTION # 177
A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non- overlapping five-minute interval. Incremental state information should be maintained for 10 minutes for late- arriving data.
Streaming DataFrame df has the following schema:
" device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT "
Code block:

Choose the response that correctly fills in the blank within the code block to complete this task.

A. delayWrite( " event_time " , " 10 minutes " )
B. slidingWindow( " event_time " , " 10 minutes " )
C. awaitArrival( " event_time " , " 10 minutes " )
D. withWatermark( " event_time " , " 10 minutes " )
E. await( " event_time + '10 minutes ' " )

Answer: D

Explanation:
The correct answer is A. withWatermark("event_time", "10 minutes"). This is because the question asks for incremental state information to be maintained for 10 minutes for late-arriving data. The withWatermark method is used to define the watermark for late data. The watermark is a timestamp column and a threshold that tells the system how long to wait for late data. In this case, the watermark is set to 10 minutes. The other options are incorrect because they are not valid methods or syntax for watermarking in Structured Streaming. References:
* Watermarking : https://docs.databricks.com/spark/latest/structured-streaming/watermarks.html
* Windowed aggregations : https://docs.databricks.com/spark/latest/structured-streaming/window-operations.html

NEW QUESTION # 178
A data engineer wants to enforce the principle of least privilege when configuring ACLs for Databricks jobs in a collaborative workspace.
Which approach should the data engineer use?

A. Assign users only the minimum permission level (e.g., CAN RUN or CAN VIEW) required for their role on each job.
B. Use only folder-level permissions and avoid setting permissions on individual jobs.
C. Grant CAN RUN permission to everyone and CAN MANAGE to a single admin group.
D. Grant all users CAN MANAGE permission on all jobs to avoid access issues.

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
Databricks follows the principle of least privilege (PoLP) to ensure users and groups have only the permissions required for their specific role.
For Databricks Jobs, three primary permission levels exist:
CAN VIEW - Allows viewing job configurations and runs.
CAN RUN - Allows triggering job runs.
CAN MANAGE - Allows full control, including editing and deletion.
To enforce PoLP, each user should be assigned only the specific permission required for their task. This approach prevents unauthorized edits and limits the risk of accidental deletions or job misconfigurations.
Granting broad permissions such as "ALL" or "CAN MANAGE to everyone" violates Databricks security best practices. Thus, the correct answer is D.

NEW QUESTION # 179
You are asked to write a python function that can read data from a delta table and return the Data-Frame, which of the following is correct?

A. Python function can return a DataFrame
B. Python function cannot return a DataFrame
C. Python function will result in out of memory error due to data volume
D. Write SQL UDF that can return tabular data
E. Write SQL UDF to return a DataFrame

Answer: C

Explanation:
Explanation
The answer is Python function can return a DataFrame
The function would something like this,
1.get_source_dataframe(tablename):
2. df = spark.read.table(tablename)
3.return df
df = get_source_dataframe('test_table')
since there is no action spark returns a Dataframe and assigns to df python variable

NEW QUESTION # 180
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