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

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

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q50-Q55):

NEW QUESTION # 50
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?
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A. A user can only transfer job ownership to a group if they are also a member of that group.
B. Other than the default "admins" group, only individual users can be granted privileges on jobs.
C. The creator of a Databricks job will always have "Owner" privileges; this configuration cannot be changed.
D. Only workspace administrators can grant "Owner" privileges to a group.
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 # 51
What statement is true regarding the retention of job run history?

A. It is retained until you export or delete job run logs
B. t is retained for 60 days, during which you can export notebook run results to HTML
C. It is retained for 90 days or until the run-id is re-used through custom run configuration
D. It is retained for 60 days, after which logs are archived
E. It is retained for 30 days, during which time you can deliver job run logs to DBFS or S3

Answer: B

Explanation:
https://docs.databricks.com/en/workflows/jobs/monitor-job-runs.html

NEW QUESTION # 52
A data engineer is configuring Delta Sharing for a Databricks-to-Databricks scenario to optimize read performance. The recipient needs to perform time travel queries and streaming reads on shared sales data. Which configuration will provide the optimal performance while enabling these capabilities?

A. Share the entire schema WITHOUT HISTORY and rely on recipient-side caching for performance.
B. Use the open sharing protocol instead of Databricks-to-Databricks sharing for better performance.
C. Share tables WITH HISTORY, ensure tables don't have partitioning enabled, and enable CDF before sharing.
D. Share tables WITHOUT HISTORY and enable partitioning for better query performance.

Answer: C

Explanation:
The official Delta Sharing guidance specifies that in order for recipients to use time travel queries and streaming reads, providers must share Delta tables WITH HISTORY. Sharing history ensures the Delta log is included, which enables efficient access to table snapshots and incremental data streams.
Additionally, Change Data Feed (CDF) must be enabled prior to sharing if downstream consumers require streaming CDC queries. Without history, recipients cannot perform time travel or streaming queries. Open sharing supports static Delta tables but lacks streaming support.
Therefore, sharing tables WITH HISTORY and enabling CDF is the required configuration for both performance and functionality.

NEW QUESTION # 53
Which configuration parameter directly affects the size of a spark-partition upon ingestion of data into Spark?

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

Answer: C

Explanation:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from 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 # 54
Assuming that the Databricks CLI has been installed and configured correctly, which Databricks CLI command can be used to upload a custom Python Wheel to object storage mounted with the DBFS for use with a production job?

A. libraries
B. fs
C. configure
D. jobs
E. workspace

Answer: B

Explanation:
https://docs.databricks.com/en/archive/dev-tools/cli/dbfs-cli.html

NEW QUESTION # 55
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