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

Section	Weight	Objectives
Topic 1: Debugging and Deploying	10%	- Implement CI/CD and DevOps practices - Troubleshoot and debug pipelines - Deploy using Asset Bundles, CLI, and APIs
Topic 2: Data Sharing and Federation	5%	- Use Delta Sharing for secure data sharing - Implement Lakehouse Federation - Manage cross-platform data access
Topic 3: Developing Code for Data Processing using Python and SQL	22%	- Write efficient and maintainable code - Implement complex data processing logic - Use Databricks-specific libraries and APIs
Topic 4: Monitoring and Alerting	10%	- Set up alerts and notifications - Monitor pipeline performance and health - Track data lineage and metrics
Topic 5: Cost & Performance Optimisation	13%	- Optimize compute and storage resources - Apply cost management best practices - Improve query and pipeline performance
Topic 6: Ensuring Data Security and Compliance	10%	- Implement access control and permissions - Secure data at rest and in transit - Ensure data privacy and compliance
Topic 7: Data Ingestion & Acquisition	7%	- Ingest data from diverse sources - Handle incremental and batch data loads - Use Auto Loader and structured streaming
Topic 8: Data Governance	7%	- Use Unity Catalog for governance - Manage data assets and metadata - Enforce data policies and standards
Topic 9: Data Transformation, Cleansing, and Quality	10%	- Implement schema evolution and management - Apply data cleansing and validation rules - Enforce data quality standards
Topic 10: Data Modelling	6%	- Implement dimensional and relational models - Optimize table design and partitioning - Design Medallion Architecture

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q236-Q241):

NEW QUESTION # 236
A data architect has heard about lake's built-in versioning and time travel capabilities. For auditing purposes they have a requirement to maintain a full of all valid street addresses as they appear in the customers table.
The architect is interested in implementing a Type 1 table, overwriting existing records with new values and relying on Delta Lake time travel to support long-term auditing. A data engineer on the project feels that a Type 2 table will provide better performance and scalability. Which piece of information is critical to this decision?

A. Delta Lake only supports Type 0 tables; once records are inserted to a Delta Lake table, they cannot be modified.
B. Delta Lake time travel cannot be used to query previous versions of these tables because Type 1 changes modify data files in place.
C. Delta Lake time travel does not scale well in cost or latency to provide a long-term versioning solution.
D. Data corruption can occur if a query fails in a partially completed state because Type 2 tables requires setting multiple fields in a single update.
E. Shallow clones can be combined with Type 1 tables to accelerate historic queries for long-term versioning.

Answer: C

Explanation:
Delta Lake's time travel feature allows users to access previous versions of a table, providing a powerful tool for auditing and versioning. However, using time travel as a long-term versioning solution for auditing purposes can be less optimal in terms of cost and performance, especially as the volume of data and the number of versions grow. For maintaining a full history of valid street addresses as they appear in a customers table, using a Type 2 table (where each update creates a new record with versioning) might provide better scalability and performance by avoiding the overhead associated with accessing older versions of a large table. While Type 1 tables, where existing records are overwritten with new values, seem simpler and can leverage time travel for auditing, the critical piece of information is that time travel might not scale well in cost or latency for long-term versioning needs, making a Type 2 approach more viable for performance and scalability.

NEW QUESTION # 237
A data engineer is tasked with ensuring that a Delta table in Databricks continuously retains deleted files for 15 days (instead of the default 7 days), in order to permanently comply with the organization's data retention policy. Which code snippet correctly sets this retention period for deleted files?

A. spark.conf.set("spark.databricks.delta.deletedFileRetentionDuration", "15 days")
B. from delta.tables import *
deltaTable = DeltaTable.forPath(spark, "/mnt/data/my_table")
deltaTable.deletedFileRetentionDuration = "interval 15 days"
C. spark.sql("VACUUM my_table RETAIN 15 HOURS")
D. spark.sql("ALTER TABLE my_table SET TBLPROPERTIES
('delta.deletedFileRetentionDuration' = 'interval 15 days')")

Answer: D

Explanation:
The deleted file retention period in Delta Lake is controlled by the table property delta.deletedFileRetentionDuration. Setting this property via ALTER TABLE ensures the retention policy is persistently enforced at the table level, extending deleted file retention to 15 days in compliance with organizational requirements.

NEW QUESTION # 238
Which configuration parameter directly affects the size of a spark-partition upon ingestion of data into Spark?

A. spark.sql.files.maxPartitionBytes
B. spark.sql.adaptive.advisoryPartitionSizeInBytes
C. spark.sql.files.openCostInBytes
D. spark.sql.autoBroadcastJoinThreshold
E. spark.sql.adaptive.coalescePartitions.minPartitionNum

Answer: A

Explanation:
This is the correct answer because spark.sql.files.maxPartitionBytes is a configuration parameter that directly affects the size of a spark-partition upon ingestion of data into Spark. This parameter configures the maximum number of bytes to pack into a single partition when reading files from file- based sources such as Parquet, JSON and ORC. The default value is 128 MB, which means each partition will be roughly 128 MB in size, unless there are too many small files or only one large file.

NEW QUESTION # 239
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. Use salting by adding a random prefix to skewed keys before aggregation, then aggregate again after removing the prefix.
B. Increase the Spark driver memory and retry.
C. Use reduceByKey instead of groupBy to avoid shuffles.
D. Filter out the skewed users before the aggregation.

Answer: A

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 # 240
A user wants to use DLT expectations to validate that a derived table report contains all records from the source, included in the table validation_copy.
The user attempts and fails to accomplish this by adding an expectation to the report table definition.
Which approach would allow using DLT expectations to validate all expected records are present in this table?

A. Define a SQL UDF that performs a left outer join on two tables, and check if this returns null values for report key values in a DLT expectation for the report table.
B. Define a temporary table that perform a left outer join on validation_copy and report, and define an expectation that no report key values are null
C. Define a view that performs a left outer join on validation_copy and report, and reference this view in DLT expectations for the report table
D. Define a function that performs a left outer join on validation_copy and report and report, and check against the result in a DLT expectation for the report table

Answer: C

Explanation:
To validate that all records from the source are included in the derived table, creating a view that performs a left outer join between the validation_copy table and the report table is effective. The view can highlight any discrepancies, such as null values in the report table's key columns, indicating missing records. This view can then be referenced in DLT (Delta Live Tables) expectations for the report table to ensure data integrity. This approach allows for a comprehensive comparison between the source and the derived table.

NEW QUESTION # 241
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