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

Section	Weight	Objectives
Data Governance	7%	- Use Unity Catalog for governance - Enforce data policies and standards - Manage data assets and metadata
Debugging and Deploying	10%	- Troubleshoot and debug pipelines - Deploy using Asset Bundles, CLI, and APIs - Implement CI/CD and DevOps practices
Developing Code for Data Processing using Python and SQL	22%	- Implement complex data processing logic - Use Databricks-specific libraries and APIs - Write efficient and maintainable code
Data Ingestion & Acquisition	7%	- Use Auto Loader and structured streaming - Handle incremental and batch data loads - Ingest data from diverse sources
Data Sharing and Federation	5%	- Implement Lakehouse Federation - Manage cross-platform data access - Use Delta Sharing for secure data sharing
Data Modelling	6%	- Design Medallion Architecture - Implement dimensional and relational models - Optimize table design and partitioning
Cost & Performance Optimisation	13%	- Optimize compute and storage resources - Improve query and pipeline performance - Apply cost management best practices
Ensuring Data Security and Compliance	10%	- Implement access control and permissions - Secure data at rest and in transit - Ensure data privacy and compliance
Data Transformation, Cleansing, and Quality	10%	- Apply data cleansing and validation rules - Implement schema evolution and management - Enforce data quality standards
Monitoring and Alerting	10%	- Track data lineage and metrics - Set up alerts and notifications - Monitor pipeline performance and health

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q52-Q57):

NEW QUESTION # 52
A data engineering team is implementing an append-only data pipeline using Delta Lake, and wants to ensure that data is never modified or deleted once written. Which Delta Lake feature should the data engineer enable to prevent modifications to existing data?

A. Delta APPEND_ONLY
B. Delta OPTIMIZE
C. Delta VACUUM
D. Delta Time Travel

Answer: A

Explanation:
Enabling the append-only table property enforces that data can only be inserted into the Delta table. Updates and deletes are blocked, ensuring that once data is written it is never modified or removed, which is essential for strict append-only pipeline guarantees.

NEW QUESTION # 53
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. 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.
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. Add the .whl file to Databricks Git Repos and assume automatic installation.

Answer: A

Explanation:
For secure, air-gapped Databricks deployments, the recommended practice is to host dependency files such as .whl packages in Unity Catalog Volumes -- a managed storage layer governed by Unity Catalog.
Once stored in a volume, these files can be safely referenced from cluster-scoped init scripts, which automatically execute installation commands (e.g., pip install
/Volumes/catalog/schema/path/PyYAML.whl) during cluster startup.
This ensures consistent environment setup across clusters and compliance with data governance rules.
User directories (A) lack enterprise security controls; private repositories (C) are not viable in air- gapped setups; and Git repos (D) do not trigger package installation. Therefore, B is the correct and officially approved method.

NEW QUESTION # 54
A data engineer wants to join a stream of advertisement impressions (when an ad was shown) with another stream of user clicks on advertisements to correlate when impressions led to monetizable clicks.
In the code below, Impressions is a streaming DataFrame with a watermark ("event_time", "10 minutes")

The data engineer notices the query slowing down significantly.
Which solution would improve the performance?

A. Joining on event time constraint: clickTime >= impressionTime AND clickTime <= impressionTime interval 1 hour
B. Joining on event time constraint: clickTime + 3 hours < impressionTime - 2 hours
C. Joining on event time constraint: clickTime == impressionTime using a leftOuter join
D. Joining on event time constraint: clickTime >= impressionTime - interval 3 hours and removing watermarks

Answer: A

NEW QUESTION # 55
A junior data engineer seeks to leverage Delta Lake's Change Data Feed functionality to create a Type 1 table representing all of the values that have ever been valid for all rows in a bronze table created with the property delta.enableChangeDataFeed = true. They plan to execute the following code as a daily job:
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Which statement describes the execution and results of running the above query multiple times?

A. Each time the job is executed, the target table will be overwritten using the entire history of inserted or updated records, giving the desired result.
B. Each time the job is executed, newly updated records will be merged into the target table, overwriting previous values with the same primary keys.
C. Each time the job is executed, the differences between the original and current versions are calculated; this may result in duplicate entries for some records.
D. Each time the job is executed, the entire available history of inserted or updated records will be appended to the target table, resulting in many duplicate entries.
E. Each time the job is executed, only those records that have been inserted or updated since the last execution will be appended to the target table giving the desired result.

Answer: D

Explanation:
Reading table's changes, captured by CDF, using spark.read means that you are reading them as a static source. So, each time you run the query, all table's changes (starting from the specified startingVersion) will be read.

NEW QUESTION # 56
A Delta table of weather records is partitioned by date and has the below schema:
date DATE, device_id INT, temp FLOAT, latitude FLOAT, longitude FLOAT
To find all the records from within the Arctic Circle, you execute a query with the below filter:
latitude > 66.3
Which statement describes how the Delta engine identifies which files to load?

A. The Hive metastore is scanned for min and max statistics for the latitude column
B. All records are cached to an operational database and then the filter is applied
C. All records are cached to attached storage and then the filter is applied Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from
D. The Delta log is scanned for min and max statistics for the latitude column
E. The Parquet file footers are scanned for min and max statistics for the latitude column

Answer: D

Explanation:
This is the correct answer because Delta Lake uses a transaction log to store metadata about each table, including min and max statistics for each column in each data file. The Delta engine can use this information to quickly identify which files to load based on a filter condition, without scanning the entire table or the file footers. This is called data skipping and it can improve query performance significantly. Verified Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; [Databricks Documentation], under "Optimizations - Data Skipping" section.
In the Transaction log, Delta Lake captures statistics for each data file of the table. These statistics indicate per file:
- Total number of records
- Minimum value in each column of the first 32 columns of the table
- Maximum value in each column of the first 32 columns of the table
- Null value counts for in each column of the first 32 columns of the table When a query with a selective filter is executed against the table, the query optimizer uses these statistics to generate the query result. it leverages them to identify data files that may contain records matching the conditional filter.
For the SELECT query in the question, The transaction log is scanned for min and max statistics for the price column.

NEW QUESTION # 57
......

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