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

Section	Objectives
Topic 1: Data Modeling and Transformation	- Dimensional modeling concepts - Performance optimization techniques - Spark SQL transformations
Topic 2: Databricks Lakehouse Platform Architecture	- Workspace and cluster architecture - Data governance concepts (Unity Catalog basics) - Medallion architecture (Bronze, Silver, Gold)
Topic 3: Delta Lake and Data Management	- Time travel and versioning - Schema evolution and enforcement - Delta Lake transactions and ACID properties
Topic 4: Data Ingestion and Processing	- ETL pipeline design patterns - Structured Streaming fundamentals - Batch and streaming ingestion with Auto Loader
Topic 5: Production Pipelines and Orchestration	- Error handling and recovery strategies - Job scheduling and monitoring - Databricks Workflows

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q112-Q117):

NEW QUESTION # 112
A data engineer is designing an append-only pipeline that needs to handle both batch and streaming data in Delta Lake. The team wants to ensure that the streaming component can efficiently track which data has already been processed. Which configuration should be set to enable this?

A. checkpointLocation
B. mergeSchema
C. overwriteSchema
D. partitionBy

Answer: A

Explanation:
When working with Delta Lake streaming ingestion, checkpointing is critical for maintaining fault tolerance and ensuring exactly-once data processing semantics.
The checkpointLocation parameter defines the directory where Spark Structured Streaming stores progress information, offsets, and metadata. This allows the engine to resume processing from the last committed offset without reprocessing previously ingested data.
Without checkpointing, each stream restart would reprocess all data, leading to duplicates.
Parameters like partitionBy or schema options (mergeSchema / overwriteSchema) affect table structure, not data lineage tracking. Therefore, the correct and required configuration for efficient streaming state management is checkpointLocation.

NEW QUESTION # 113
Which REST API call can be used to review the notebooks configured to run as tasks in a multi- task job?

A. /jobs/list
B. /jobs/runs/list
C. /jobs/runs/get
D. /jobs/get
E. /jobs/runs/get-output

Answer: D

Explanation:
https://docs.databricks.com/api/workspace/jobs/getresponses/settings/tasks/notebook_task/noteb ook_path

NEW QUESTION # 114
The security team is exploring whether or not the Databricks secrets module can be leveraged for connecting to an external database.
After testing the code with all Python variables being defined with strings, they upload the password to the secrets module and configure the correct permissions for the currently active Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from user. They then modify their code to the following (leaving all other variables unchanged).

Which statement describes what will happen when the above code is executed?

A. The connection to the external table will succeed; the string "redacted" will be printed.
B. An interactive input box will appear in the notebook; if the right password is provided, the connection will succeed and the encoded password will be saved to DBFS.
C. The connection to the external table will succeed; the string value of password will be printed in plain text.
D. The connection to the external table will fail; the string "redacted" will be printed.
E. An interactive input box will appear in the notebook; if the right password is provided, the connection will succeed and the password will be printed in plain text.

Answer: A

Explanation:
This is the correct answer because the code is using the dbutils.secrets.get method to retrieve the password from the secrets module and store it in a variable. The secrets module allows users to securely store and access sensitive information such as passwords, tokens, or API keys. The connection to the external table will succeed because the password variable will contain the actual password value. However, when printing the password variable, the string "redacted" will be displayed instead of the plain text password, as a security measure to prevent exposing sensitive information in notebooks.

NEW QUESTION # 115
A Structured Streaming job deployed to production has been experiencing delays during peak hours of the day. At present, during normal execution, each microbatch of data is processed in less than 3 seconds. During peak hours of the day, execution time for each microbatch becomes very inconsistent, sometimes exceeding 30 seconds. The streaming write is currently configured with a trigger interval of 10 seconds.
Holding all other variables constant and assuming records need to be processed in less than 10 seconds, which adjustment will meet the requirement?

A. Use the trigger once option and configure a Databricks job to execute the query every 10 seconds; this ensures all backlogged records are processed with each batch.
B. The trigger interval cannot be modified without modifying the checkpoint directory; to maintain the current stream state, increase the number of shuffle partitions to maximize parallelism.
C. Decrease the trigger interval to 5 seconds; triggering batches more frequently allows idle executors to begin processing the next batch while longer running tasks from previous batches finish.
D. Increase the trigger interval to 30 seconds; setting the trigger interval near the maximum execution time observed for each batch is always best practice to ensure no records are dropped.
E. Decrease the trigger interval to 5 seconds; triggering batches more frequently may prevent records from backing up and large batches from causing spill.

Answer: E

Explanation:
The adjustment that will meet the requirement of processing records in less than 10 seconds is to decrease the trigger interval to 5 seconds. This is because triggering batches more frequently may prevent records from backing up and large batches from causing spill. Spill is a phenomenon where the data in memory exceeds the available capacity and has to be written to disk, which can slow down the processing and increase the execution time. By reducing the trigger interval, the streaming query can process smaller batches of data more quickly and avoid spill. This can also improve the latency and throughput of the streaming job.

NEW QUESTION # 116
A developer has successfully configured their credentials for Databricks Repos and cloned a remote Git repository. They do not have privileges to make changes to the main branch, which is the only branch currently visible in their workspace. Which approach allows this user to share their code updates without the risk of overwriting the work of their teammates?

A. Use Repos to merge all differences and make a pull request back to the remote repository.
B. Use Repos to pull changes from the remote Git repository; commit and push changes to a branch that appeared as changes were pulled.
C. Use repos to merge all difference and make a pull request back to the remote repository.
D. Use Repos to create a new branch commit all changes and push changes to the remote Git repertory.
E. Use repos to create a fork of the remote repository commit all changes and make a pull request on the source repository

Answer: D

Explanation:
In Databricks Repos, when a user does not have privileges to make changes directly to the main branch of a cloned remote Git repository, the recommended approach is to create a new branch within the Databricks workspace. The developer can then make changes in this new branch, commit those changes, and push the new branch to the remote Git repository. This workflow allows for isolated development without affecting the main branch, enabling the developer to propose changes via a pull request from the new branch to the main branch in the remote repository. This method adheres to common Git collaboration workflows, fostering code review and collaboration while ensuring the integrity of the main branch.

NEW QUESTION # 117
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