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Databricks-Certified-Professional-Data-Engineer Questions and Answers

Question # 1

When evaluating the Ganglia Metrics for a given cluster with 3 executor nodes, which indicator would signal proper utilization of the VM ' s resources?

A.

The five Minute Load Average remains consistent/flat

B.

Bytes Received never exceeds 80 million bytes per second

C.

Network I/O never spikes

D.

Total Disk Space remains constant

E.

CPU Utilization is around 75%

Question # 2

A data team is automating a daily multi-task ETL pipeline in Databricks. The pipeline includes a notebook for ingesting raw data, a Python wheel task for data transformation, and a SQL query to update aggregates. They want to trigger the pipeline programmatically and see previous runs in the GUI. They need to ensure tasks are retried on failure and stakeholders are notified by email if any task fails.

Which two approaches will meet these requirements? (Choose 2 answers)

A.

Use the REST API endpoint /jobs/runs/submit to trigger each task individually as separate job runs and implement retries using custom logic in the orchestrator.

B.

Create a multi-task job using the UI, Databricks Asset Bundles (DABs), or the Jobs REST API (/jobs/create) with notebook, Python wheel, and SQL tasks. Configure task-level retries and email notifications in the job definition.

C.

Trigger the job programmatically using the Databricks Jobs REST API (/jobs/run-now), the CLI (databricks jobs run-now), or one of the Databricks SDKs.

D.

Create a single orchestrator notebook that calls each step with dbutils.notebook.run(), defining a job for that notebook and configuring retries and notifications at the notebook level.

E.

Use Databricks Asset Bundles (DABs) to deploy the workflow, then trigger individual tasks directly by referencing each task’s notebook or script path in the workspace.

Question # 3

The data science team has requested assistance in accelerating queries on free form text from user reviews. The data is currently stored in Parquet with the below schema:

item_id INT, user_id INT, review_id INT, rating FLOAT, review STRING

The review column contains the full text of the review left by the user. Specifically, the data science team is looking to identify if any of 30 key words exist in this field.

A junior data engineer suggests converting this data to Delta Lake will improve query performance.

Which response to the junior data engineer s suggestion is correct?

A.

Delta Lake statistics are not optimized for free text fields with high cardinality.

B.

Text data cannot be stored with Delta Lake.

C.

ZORDER ON review will need to be run to see performance gains.

D.

The Delta log creates a term matrix for free text fields to support selective filtering.

E.

Delta Lake statistics are only collected on the first 4 columns in a table.

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Databricks Databricks-Certified-Professional-Data-Engineer Exam Dumps FAQs

The Databricks-Certified-Professional-Data-Engineer (DPE) exam is a certification designed to assess an individual's ability to perform advanced data engineering tasks using the Databricks platform.
The Databricks-Certified-Professional-Data-Engineer exam consists of multiple-choice and multiple-select questions. The exam is designed to test your knowledge and skills in data engineering on the Databricks platform.
The passing score for the Databricks-Certified-Professional-Data-Engineer exam is 70%.
The Databricks-Certified-Professional-Data-Engineer certification is valid for two years. After this period, you'll need to recertify by taking the current version of the exam to maintain your certified status.

The Databricks-Certified-Professional-Data-Engineer and Databricks-Certified-Data-Engineer-Associate certifications are designed for different levels of expertise and focus on different aspects of data engineering on the Databricks platform.

  • Databricks-Certified-Professional-Data-Engineer: The Databricks-Certified-Professional-Data-Engineer Certification Exam is aimed at professionals with at least one year of hands-on experience in data engineering using Databricks. It covers advanced topics such as building and optimizing ETL pipelines, data modeling, security and governance, monitoring and logging, and testing and deployment.
  • Databricks-Certified-Data-Engineer-Associate: The Databricks-Certified-Data-Engineer-Associate Certification Exam is designed for individuals who are new to data engineering or have a basic understanding of Databricks. It focuses on foundational concepts and skills, such as working with Delta tables, understanding DLT pipelines, and basic knowledge of Databricks tools.
Some effective tips include: understanding the exam objectives, practicing with Databricks-Certified-Professional-Data-Engineer sample questions, reviewing the Databricks-Certified-Professional-Data-Engineer study guides, taking timed practice exams, and staying updated with the latest exam changes.
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