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

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

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q235-Q240):

NEW QUESTION # 235
A streaming video analytics team ingests billions of events daily into a Unity Catalog-managed Delta table video_events. Analysts run ad-hoc point-lookup queries on columns like user_id, campaign_id, and region. The team manually runs OPTIMIZE video_events ZORDER BY (user_id, campaign_id, region), but still sees poor performance on recent data and dislikes the operational overhead. The team wants a hands-off way to keep hot columns co-located as query patterns evolve. Which Delta capability should the team leverage on video_events?

A. Utilize Liquid Clustering (CLUSTER BY AUTO) and Predictive Optimization.
B. Enable Delta caching.
C. Schedule OPTIMIZE/ZORDER to run after each job to improve recent file performance.
D. Enable auto-compaction (optimizeWrite and autoCompact).

Answer: A

Explanation:
According to Databricks Delta Lake optimization documentation, Liquid Clustering is a next- generation file organization capability that automatically manages file co-location without requiring explicit partitioning or manual Z-ORDERing. When combined with Predictive Optimization, Databricks automatically maintains clustering across frequently filtered or queried columns, adapting dynamically as query workloads evolve.
This approach eliminates the need for manual maintenance (such as periodic OPTIMIZE or Z- ORDER commands) while improving query performance on large tables--particularly for high- ingest streaming workloads.
Delta caching (B) only improves performance for cached queries and does not address file layout issues, and (D) handles file size optimization but not clustering. Thus, C is the most efficient, modern, and low-maintenance solution recommended by Databricks.

NEW QUESTION # 236
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 "BINARYFILE".
B. Use Auto Loader and set cloudFiles.format to "TEXT".
C. Use Auto Loader and set cloudFiles.format to "IMAGE".
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").

Answer: A,E

Explanation:
Databricks Auto Loader supports ingestion of binary file formats using the cloudFiles.format option. For ingesting JPEG or PNG image files, the correct setting is "BINARYFILE", which loads the raw binary content and file metadata into a DataFrame. Additionally, when processing files from object storage, it is best practice to apply pathGlobFilter to limit ingestion to specific file types and reduce unnecessary scanning of non-image files. Options like "IMAGE" or "TEXT" are invalid, and using volumes with SQL editors does not provide incremental ingestion. Therefore, combining Auto Loader with cloudFiles.format="BINARYFILE" and pathGlobFilter ensures scalable, incremental ingestion of image data into Unity Catalog tables.

NEW QUESTION # 237
The Databricks CLI is use to trigger a run of an existing job by passing the job_id parameter. The response that the job run request has been submitted successfully includes a filed run_id.
Which statement describes what the number alongside this field represents?

A. The job_id is returned in this field.
B. The number of times the job definition has been run in the workspace.
C. The total number of jobs that have been run in the workspace.
D. The job_id and number of times the job has been are concatenated and returned.
E. The globally unique ID of the newly triggered run.

Answer: E

Explanation:
When triggering a job run using the Databricks CLI, the run_id field in the response represents a globally unique identifier for that particular run of the job. This run_id is distinct from the job_id.
While the job_id identifies the job definition and is constant across all runs of that job, the run_id is unique to each execution and is used to track and query the status of that specific job run within the Databricks environment. This distinction allows users to manage and reference individual executions of a job directly.

NEW QUESTION # 238
A data engineer needs to capture pipeline settings from an existing in the workspace, and use them to create and version a JSON file to create a new pipeline. Which command should the data engineer enter in a web terminal configured with the Databricks CLI?

A. Use the get command to capture the settings for the existing pipeline; remove the pipeline_id and rename the pipeline; use this in a create command
B. Use the alone command to create a copy of an existing pipeline; use the get JSON command to get the pipeline definition; save this to git
C. Use list pipelines to get the specs for all pipelines; get the pipeline spec from the return results parse and use this to create a pipeline
D. Stop the existing pipeline; use the returned settings in a reset command

Answer: A

Explanation:
The Databricks CLI provides a way to automate interactions with Databricks services. When dealing with pipelines, you can use the databricks pipelines get --pipeline-id command to capture the settings of an existing pipeline in JSON format. This JSON can then be modified by removing the pipeline_id to prevent conflicts and renaming the pipeline to create a new pipeline. The modified JSON file can then be used with the databricks pipelines create command to create a new pipeline with those settings.

NEW QUESTION # 239
A data engineer is implementing a job to download multiple PDF files from a third-party provided REST API endpoint by specifying different report types. The REST API is time-consuming and encounters intermittent errors, so the engineer wants to track each download activity to know when it fails and to retry partially, while providing scalable throughput. The engineer needs to download ten report types, and the list can be changed over time. How should the data engineer achieve this?

A. Define a list variable within a Notebook to loop through the report types to download them, and print the download results. Execute it as a Notebook tasks.
B. Define ten Notebook tasks to clearly track which report download failed.
C. Use a Delta Lake table to track each report download status as 10 rows, and use it as a source table to execute the download function as a Pandas UDF.
D. Use a foreach task with a list of report types as its inputs.

Answer: D

Explanation:
A foreach task allows the job to dynamically iterate over a configurable list of report types, execute downloads in parallel, and track the success or failure of each item independently. This enables scalable throughput, partial retries for failed downloads, and easy updates when the list of report types changes, without hardcoding tasks or introducing unnecessary complexity.

NEW QUESTION # 240
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