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

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

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Databricks-Certified-Data-Engineer-Professional Examinations Actual Questions & Databricks-Certified-Data-Engineer-Professional Mock Exam

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q70-Q75):

NEW QUESTION # 70
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 Time Travel
B. Delta APPEND_ONLY
C. Delta OPTIMIZE
D. Delta VACUUM

Answer: B

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 # 71
A data engineer is attempting to execute the following PySpark code:
df = spark.read.table("sales")
result = df.groupBy("region").agg(sum("revenue"))
However, upon inspecting the execution plan and profiling the Spark job, they observe excessive data shuffling during the aggregation phase.
Which technique should be applied to reduce shuffling during the groupBy aggregation operation?

A. Use coalesce() after the aggregation.
B. Use broadcast join.
C. Repartition by region before aggregation.
D. Caching the DataFrame df.

Answer: C

Explanation:
Repartitioning the DataFrame by the grouping key ensures that records with the same region are colocated in the same partitions before the aggregation runs. This significantly reduces the amount of data shuffled during the groupBy operation, leading to more efficient execution.

NEW QUESTION # 72
A data engineer is running a groupBy aggregation on a massive user activity log grouped by user_id. A few users have millions of records, causing task skew and long runtimes. Which technique will fix the skew in this aggregation?

A. Filter out the skewed users before the aggregation.
B. Increase the Spark driver memory and retry.
C. Use reduceByKey instead of groupBy to avoid shuffles.
D. Use salting by adding a random prefix to skewed keys before aggregation, then aggregate again after removing the prefix.

Answer: D

Explanation:
Salting distributes records for heavily skewed keys across multiple partitions by adding a random prefix, which balances task execution during the aggregation. A second aggregation after removing the prefix correctly recombines the partial results, eliminating skew-related bottlenecks without losing accuracy.

NEW QUESTION # 73
Incorporating unit tests into a PySpark application requires upfront attention to the design of your jobs, or a potentially significant refactoring of existing code.
Which statement describes a main benefit that offset this additional effort?

A. Troubleshooting is easier since all steps are isolated and tested individually
B. Yields faster deployment and execution times
C. Ensures that all steps interact correctly to achieve the desired end result
D. Improves the quality of your data
E. Validates a complete use case of your application

Answer: A

Explanation:
Unit tests are small, isolated tests that are used to check specific parts of the code, such as functions or classes.

NEW QUESTION # 74
A data engineer wants to refactor the following DLT code, which includes multiple table definitions with very similar code.

In an attempt to programmatically create these tables using a parameterized table definition, the data engineer writes the following code.

The pipeline runs an update with this refactored code, but generates a different DAG showing incorrect configuration values for these tables.
How can the data engineer fix this?

A. Wrap the for loop inside another table definition, using generalized names and properties to replace with those from the inner table definition.
B. Load the configuration values for these tables from a separate file, located at a path provided by a pipeline parameter.
C. Convert the list of configuration values to a dictionary of table settings, using table names as keys.
D. Move the table definition into a separate function, and make calls to this function using different input parameters inside the for loop.

Answer: D

Explanation:
In the provided refactored code, the for loop dynamically attempts to define multiple tables, but the use of a loop within the DLT (@dlt.table) decorator does not work properly because it results in a single function reference being overwritten for each iteration. This leads to an incorrect DAG because all the table definitions end up pointing to the last iteration of the loop.

NEW QUESTION # 75
......

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