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

Section	Objectives
Databricks Lakehouse Platform Architecture	- Medallion architecture (Bronze, Silver, Gold) - Data governance concepts (Unity Catalog basics) - Workspace and cluster architecture
Data Ingestion and Processing	- Structured Streaming fundamentals - ETL pipeline design patterns - Batch and streaming ingestion with Auto Loader
Delta Lake and Data Management	- Time travel and versioning - Delta Lake transactions and ACID properties - Schema evolution and enforcement
Production Pipelines and Orchestration	- Error handling and recovery strategies - Databricks Workflows - Job scheduling and monitoring
Data Modeling and Transformation	- Dimensional modeling concepts - Performance optimization techniques - Spark SQL transformations

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q189-Q194):

NEW QUESTION # 189
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. Load the configuration values for these tables from a separate file, located at a path provided by a pipeline parameter.
B. Convert the list of configuration values to a dictionary of table settings, using different input the for loop.
C. Convert the list of configuration values to a dictionary of table settings, using table names as keys.
D. Wrap the loop inside another table definition, using generalized names and properties to replace with those from the inner table

Answer: C

Explanation:
The issue with the refactored code is that it tries to use string interpolation to dynamically create table names within the dlc.table decorator, which will not correctly interpret the table names.
Instead, by using a dictionary with table names as keys and their configurations as values, the data engineer can iterate over the dictionary items and use the keys (table names) to properly configure the table settings. This way, the decorator can correctly recognize each table name, and the corresponding configuration settings can be applied appropriately.

NEW QUESTION # 190
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. Use reduceByKey instead of groupBy to avoid shuffles.
C. Filter out the skewed users before the aggregation.
D. Increase the Spark driver memory and retry.

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 # 191
A data pipeline uses Structured Streaming to ingest data from kafka to Delta Lake. Data is being stored in a bronze table, and includes the Kafka_generated timesamp, key, and value. Three months after the pipeline is deployed the data engineering team has noticed some latency issued during certain times of the day.
A senior data engineer updates the Delta Table's schema and ingestion logic to include the current timestamp (as recoded by Apache Spark) as well the Kafka topic and partition. The team plans to use the additional metadata fields to diagnose the transient processing delays.
Which limitation will the team face while diagnosing this problem?

A. New fields cannot be added to a production Delta table.
B. Updating the table schema requires a default value provided for each file added.
C. New fields will not be computed for historic records.
D. Spark cannot capture the topic partition fields from the kafka source.
E. Updating the table schema will invalidate the Delta transaction log metadata.

Answer: C

Explanation:
When adding new fields to a Delta table's schema, these fields will not be retrospectively applied to historical records that were ingested before the schema change. Consequently, while the team can use the new metadata fields to investigate transient processing delays moving forward, they will be unable to apply this diagnostic approach to past data that lacks these fields.

NEW QUESTION # 192
The DevOps team has configured a production workload as a collection of notebooks scheduled to run daily using the Jobs UI. A new data engineering hire is onboarding to the team and has requested access to one of these notebooks to review the production logic.
What are the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data?

A. Can Edit
B. Can Run
C. Can Manage
D. Can Read
E. No permissions

Answer: D

NEW QUESTION # 193
A team of data engineer are adding tables to a DLT pipeline that contain repetitive expectations for many of the same data quality checks.
One member of the team suggests reusing these data quality rules across all tables defined for this pipeline.
What approach would allow them to do this?

A. Add data quality constraints to tables in this pipeline using an external job with access to pipeline configuration files.
B. Use global Python variables to make expectations visible across DLT notebooks included in the same pipeline.
C. Maintain data quality rules in a separate Databricks notebook that each DLT notebook of file.
D. Maintain data quality rules in a Delta table outside of this pipeline's target schema, providing the schema name as a pipeline parameter.

Answer: D

Explanation:
Maintaining data quality rules in a centralized Delta table allows for the reuse of these rules across multiple DLT (Delta Live Tables) pipelines. By storing these rules outside the pipeline's target schema and referencing the schema name as a pipeline parameter, the team can apply the same set of data quality checks to different tables within the pipeline. This approach ensures consistency in data quality validations and reduces redundancy in code by not having to replicate the same rules in each DLT notebook or file.

NEW QUESTION # 194
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