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

Section	Weight	Objectives
Debugging and Deploying	10%	- CI/CD and DevOps practices - Deployment using bundles, CLI, and APIs - Troubleshooting pipelines and errors
Data Transformation, Cleansing, and Quality	10%	- Data validation and quality checks - Standardization and normalization - Handling missing or inconsistent data
Monitoring and Alerting	10%	- Pipeline observability and logging - Setting up alerts and notifications - Performance and health monitoring
Data Modelling	6%	- Schema design and management - Medallion Architecture implementation - Delta Lake table design
Data Governance	7%	- Unity Catalog management - Data lineage and metadata tracking - Policy enforcement
Cost & Performance Optimisation	13%	- Query optimization and caching - Storage optimization (partitioning, Z-order, indexing) - Cluster configuration and scaling
Developing Code for Data Processing using Python and SQL	22%	- Integration with Databricks APIs and tools - Batch and incremental processing logic - Data transformation and aggregation
Data Sharing and Federation	5%	- Cross-workspace and cross-cloud access - Unity Catalog data sharing
Ensuring Data Security and Compliance	10%	- Compliance standards implementation - Access control and permissions - Data encryption and masking
Data Ingestion & Acquisition	7%	- Connecting to diverse data sources - Auto Loader and streaming ingestion - Schema inference and evolution

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q200-Q205):

NEW QUESTION # 200
A data engineer is designing a data pipeline in Databricks that needs to process records from a Kafka stream where late-arriving data is common. Which approach should the data engineer use?

A. Implement a custom solution using Databricks Jobs to periodically reprocess all historical data.
B. Use batch processing and overwrite the entire output table each time to ensure late data is incorporated correctly.
C. Use a watermark to specify the allowed lateness to accommodate records that arrive after their expected window, ensuring correct aggregation and state management.
D. Use an Auto CDC pipeline with batch tables to simplify late data handling.

Answer: C

Explanation:
Databricks and Apache Spark document watermarks as the standard mechanism for handling late-arriving data in Structured Streaming. A watermark defines how long the engine should continue waiting for out-of- order event-time data and helps manage state for aggregations, joins, and deduplication. ( Databricks Documentation ) This directly matches Kafka streaming scenarios where lateness is common. The other options are not the standard streaming solution for event-time late data: Auto CDC is for change data capture use cases, and repeated full historical reprocessing is inefficient and unnecessary when watermarking is the built-in feature designed for this problem. ( Databricks Documentation )
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NEW QUESTION # 201
Which statement describes Delta Lake Auto Compaction?

A. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 1 GB.
B. Before a Jobs cluster terminates, optimize is executed on all tables modified during the most recent job.
C. Data is queued in a messaging bus instead of committing data directly to memory; all data is committed from the messaging bus in one batch once the job is complete.
D. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 128 MB.
E. Optimized writes use logical partitions instead of directory partitions; because partition boundaries are only represented in metadata, fewer small files are written.

Answer: D

Explanation:
This is the correct answer because it describes the behavior of Delta Lake Auto Compaction, which is a feature that automatically optimizes the layout of Delta Lake tables by coalescing small files into larger ones.
Auto Compaction runs as an asynchronous job after a write to a table has succeeded and checks if files within a partition can be further compacted. If yes, it runs an optimize job with a default target file size of 128 MB.
Auto Compaction only compacts files that have not been compacted previously. Verified References:
[Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Auto Compaction for Delta Lake on Databricks" section.
"Auto compaction occurs after a write to a table has succeeded and runs synchronously on the cluster that has performed the write. Auto compaction only compacts files that haven't been compacted previously."
https://learn.microsoft.com/en-us/azure/databricks/delta/tune-file-size

NEW QUESTION # 202
Two data engineers are working on the same Databricks notebook in separate branches. Both have edited the same section of code. When one tries to merge the other's branch into their own using the Databricks Git folders UI, a merge conflict occurs on that notebook file. The UI highlights the conflict and presents options for resolution.
How should the data engineers resolve this merge conflict using Databricks Git folders?

A. Use the Git folders UI to manually edit the notebook file, selecting the desired lines from both versions and removing the conflict markers, then mark the conflict as resolved.
B. Abort the merge, discard all local changes, and try the merge operation again without reviewing the conflicting code.
C. Delete the conflicted notebook file via the Databricks workspace UI, commit the deletion, and recreate the notebook from scratch in a new commit to bypass the conflict entirely.
D. Use the Git CLI in the cluster's web terminal to force-push the conflicted merge (git push -force), overriding the remote branch with the local version and discarding changes.

Answer: A

Explanation:
In the Databricks Git folders integration, when merge conflicts arise in notebooks, the UI provides a visual diff editor that highlights conflicting code segments. Users can manually choose which changes to keep from each branch, edit directly in the notebook UI, and remove conflict markers.
After resolving, the engineer must mark the conflict as resolved, save, and commit the final version.
This process ensures that both contributors' valid code segments are merged correctly and version history is maintained.
Forcing a push (C) or deleting notebooks (B) introduces data loss or versioning issues. Aborting without review (A) violates collaborative best practices. Therefore, D is the only correct and Databricks-approved way to resolve notebook merge conflicts.

NEW QUESTION # 203
Which statement describes integration testing?

A. Validates an application use case
B. Requires manual intervention
C. Requires an automated testing framework
D. Validates behavior of individual elements of your application
E. Validates interactions between subsystems of your application

Answer: E

Explanation:
This is the correct answer because it describes integration testing. Integration testing is a type of testing that validates interactions between subsystems of your application, such as modules, components, or services. Integration testing ensures that the subsystems work together as expected and produce the correct outputs or results. Integration testing can be done at different levels of granularity, such as component integration testing, system integration testing, or end-to-end testing. Integration testing can help detect errors or bugs that may not be found by unit testing, which only validates behavior of individual elements of your application. Verified Reference: [Databricks Certified Data Engineer Professional], under "Testing" section; Databricks Documentation, under "Integration testing" section.

NEW QUESTION # 204
A healthcare analytics team is implementing a dimensional model in Delta Lake for patient care analysis.
They have a date dimension table and are evaluating design options to ensure it supports a wide range of time- based analyses.
Which design approach for the date dimension will support efficient time-based querying and aggregation?

A. Pre-calculate attributes like fiscal_period, quarter, month_name, day_of_week, and holiday.
B. Store the date as a string in the format YYYY-MM-DD for readability.
C. Store only the date value and calculate all time attributes dynamically in queries.
D. Create separate dimension tables for different calendar systems (fiscal, academic, etc.).

Answer: A

Explanation:
In dimensional modeling, Databricks recommends denormalized, attribute-rich dimension tables for performance and usability. A date dimension should include all commonly used derived time attributes such as fiscal period, quarter, month, weekday, and holiday flags. Precomputing these attributes ensures consistent business logic, eliminates repeated calculations during query time, and enables efficient filtering and aggregation. The documentation for Delta Lake and Lakehouse design explicitly advises precomputing these attributes for analytical workloads that depend heavily on time-based slicing. Options A and C degrade performance and consistency, while maintaining multiple calendar-specific dimension tables (B) complicates the model unnecessarily.

NEW QUESTION # 205
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