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

Databricks Certified Data Engineer Professional Sample Questions:

Question 1

A data engineer needs to install the PyYAML Python package within an air-gapped Databricks environment. The workspace has no direct internet access to PyPI. The engineer has downloaded the .whl file locally and wants it available automatically on all new clusters. Which approach should the data engineer use?

A. Add the .whl file to Databricks Git Repos and assume automatic installation.
B. Upload the PyYAML .whl file to the user home directory and create a cluster-scoped init script to install it.
C. Set up a private PyPI repository and install via pip index URL.
D. Upload the PyYAML .whl file to a Unity Catalog Volume, ensure it's allow-listed, and create a cluster-scoped init script that installs it from that path.

Question 2

A transactions table has been liquid clustered on the columns product_id, user_id, and event_date. Which operation lacks support for cluster on write?

A. INSERT INTO operations
B. spark.writestream.format('delta').mode('append')
C. CTAS and RTAS statements
D. spark.write.format('delta').mode('append')

Question 3

A senior data engineer is planning large-scale data workflows. The current task is to identify the considerations that form a foundation for creating scalable data models that are essential for effective management of large datasets. The data engineering team has identified the core capabilities as part of a scalable data model to build a modern data platform and provided their reasoning for considering Delta Lake for review. The senior data engineer is responsible for identifying the recommendations that are not valid. Which key features can be ignored while evaluating Delta Lake?

A. Delta Lake's capability to process data in both batch and streaming modes seamlessly, providing flexibility in data ingestion and processing.
B. Delta Lake optimizes metadata handling, efficiently managing billions of files and facilitating scalability to petabyte-scale datasets.
C. Delta Lake works with various data formats (e.g., Parquet, JSON, CSV) and integrates well with Spark and Databricks tools.
D. Delta Lake provides limited support for monitoring and troubleshooting data pipelines, so relevant partner tools have to be identified and set up for enhanced operational efficiency.

Question 4

A data engineer wants to ingest a large collection of image files (JPEG and PNG) from cloud object storage into a Unity Catalog-managed table for analysis and visualization. Which two configurations and practices are recommended to incrementally ingest these images into the table? (Choose two.)

A. Use Auto Loader and set cloudFiles.format to "IMAGE".
B. Use Auto Loader and set cloudFiles.format to "BINARYFILE".
C. Use Auto Loader and set cloudFiles.format to "TEXT".
D. Move files to a volume and read with SQL editor.
E. Use the pathGlobFilter option to select only image files (e.g., "*.jpg,*.png").

Question 5

Which distribution does Databricks support for installing custom Python code packages?

A. nom
B. Wheels
C. sbt
D. CRAN
E. jars
F. CRAM

Solutions: