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

Section	Weight	Objectives
Topic 1: Data Quality and Governance	12%	- Data Lineage - Data Quality - Governance
Topic 2: Monitoring and Troubleshooting	16%	- Troubleshooting - Performance Optimization - Monitoring
Topic 3: Databricks Lakehouse Platform	24%	- Delta Lake - Data Management - Lakehouse Architecture - Unity Catalog
Topic 4: Data Processing	28%	- Spark SQL - Data Transformation - Structured Streaming - ETL Pipelines
Topic 5: Data Modeling and Storage	20%	- Storage Optimization - Data Modeling - File Formats

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q84-Q89):

NEW QUESTION # 84
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. Filter out the skewed users before the aggregation.
D. Use reduceByKey instead of groupBy to avoid shuffles.

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 # 85
In a Databricks Asset Bundle project, in the file resources/app.yml, the data engineer would like to deploy a Databricks Apps databricks_app_deployed and Volume volume_deployed and grant the Service Principal behind Databricks Apps permissions to READ and WRITE to the Volume.
How should the data engineer achieve the deployment?

Answer: C

Explanation:
This configuration correctly references the service principal created for the Databricks App using the deployed app resource identifier, and it grants the required READ and WRITE privileges at the Volume level. The privileges are specified using the correct Volume-specific permissions, ensuring the Databricks App can securely access the Volume after deployment.

NEW QUESTION # 86
Where in the Spark UI can one diagnose a performance problem induced by not leveraging predicate push-down?

A. In the Storage Detail screen, by noting which RDDs are not stored on disk
B. In the Delta Lake transaction log. by noting the column statistics
C. In the Query Detail screen, by interpreting the Physical Plan
D. In the Stage's Detail screen, in the Completed Stages table, by noting the size of data read from the Input column
E. In the Executor's log file, by gripping for "predicate push-down"

Answer: C

Explanation:
This is the correct answer because it is where in the Spark UI one can diagnose a performance problem induced by not leveraging predicate push-down. Predicate push-down is an optimization technique that allows filtering data at the source before loading it into memory or processing it further. This can improve performance and reduce I/O costs by avoiding reading unnecessary data. To leverage predicate push-down, one should use supported data sources and formats, such as Delta Lake, Parquet, or JDBC, and use filter expressions that can be pushed down to the source. To diagnose a performance problem induced by not leveraging predicate push-down, one can use the Spark UI to access the Query Detail screen, which shows information about a SQL query executed on a Spark cluster. The Query Detail screen includes the Physical Plan, which is the actual plan executed by Spark to perform the query. The Physical Plan shows the physical operators used by Spark, such as Scan, Filter, Project, or Aggregate, and their input and output statistics, such as rows and bytes. By interpreting the Physical Plan, one can see if the filter expressions are pushed down to the source or not, and how much data is read or processed by each operator.

NEW QUESTION # 87
Why are Pandas UDFs often preferred over traditional PySpark UDFs in performance-critical applications involving large datasets?

A. They minimize memory usage by streaming each row individually through a lightweight Python wrapper, avoiding batch processing overhead.
B. They leverage Apache Arrow to enable vectorized operations between the JVM and Python runtimes, reducing serialization costs and improving computational efficiency.
C. They allow row-level execution of functions in Python with native Spark optimization, removing the need for columnar execution.
D. They eliminate the JVM-Python boundary by bypassing serialization entirely, thereby avoiding data conversion overhead.

Answer: B

Explanation:
Pandas UDFs use Apache Arrow to transfer data between the JVM and Python in a columnar, vectorized format. This significantly reduces serialization overhead and enables efficient batch processing, resulting in much better performance than traditional row-by-row PySpark UDFs on large datasets.

NEW QUESTION # 88
A junior data engineer has manually configured a series of jobs using the Databricks Jobs UI.
Upon reviewing their work, the engineer realizes that they are listed as the "Owner" for each job.
They attempt to transfer "Owner" privileges to the "DevOps" group, but cannot successfully accomplish this task.
Which statement explains what is preventing this privilege transfer?

A. A user can only transfer job ownership to a group if they are also a member of that group.
B. Only workspace administrators can grant "Owner" privileges to a group.
C. The creator of a Databricks job will always have "Owner" privileges; this configuration cannot be changed.
D. Other than the default "admins" group, only individual users can be granted privileges on jobs.
E. Databricks jobs must have exactly one owner; "Owner" privileges cannot be assigned to a group.

Answer: E

Explanation:
A job cannot have more than one owner. A job cannot have a group as an owner.

NEW QUESTION # 89
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