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

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

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q92-Q97):

NEW QUESTION # 92
A data engineering team is collaborating on a Databricks project where each team member needs to develop and test code independently before merging changes into the main branch.
They want to avoid accidental overwrites or branch switching issues while ensuring that all work is version- controlled and can be integrated into their CI/CD pipeline.
How should the data engineer achieve collaboration?

A. Team members use the Databricks CLI to clone the Git repository and perform Git operations from a cluster's web terminal.
B. Team members edit notebooks directly in the workspace's shared folder and periodically copy changes into a Git folder for version control.
C. Each team member creates their own Databricks Git folder, mapped to the same remote Git repository, and works in their own development branch within their personal folder.
D. All team members work in the same Databricks Git folder and perform Git operations (pull, push, commit, branch switching) directly in that shared folder.

Answer: C

Explanation:
Using separate Databricks Git folders per user mapped to the same remote repository allows each team member to work independently on their own branch without interfering with others.
This prevents accidental overwrites or branch conflicts while ensuring all changes are version- controlled and easily integrated into CI/CD workflows.

NEW QUESTION # 93
A small company based in the United States has recently contracted a consulting firm in India to implement several new data engineering pipelines to power artificial intelligence applications. All the company's data is stored in regional cloud storage in the United States.
The workspace administrator at the company is uncertain about where the Databricks workspace used by the contractors should be deployed.
Assuming that all data governance considerations are accounted for, which statement accurately informs this decision?

A. Cross-region reads and writes can incur significant costs and latency; whenever possible, compute should be deployed in the same region the data is stored.
B. Databricks runs HDFS on cloud volume storage; as such, cloud virtual machines must be deployed in the region where the data is stored.
C. Databricks notebooks send all executable code from the user's browser to virtual machines over the open internet; whenever possible, choosing a workspace region near the end users is the most secure.
D. Databricks workspaces do not rely on any regional infrastructure; as such, the decision should be made based upon what is most convenient for the workspace administrator.
E. Databricks leverages user workstations as the driver during interactive development; as such, users should always use a workspace deployed in a region they are physically near.

Answer: A

Explanation:
This is the correct answer because it accurately informs this decision. The decision is about where the Databricks workspace used by the contractors should be deployed. The contractors are based in India, while all the company's data is stored in regional cloud storage in the United States. When choosing a region for deploying a Databricks workspace, one of the important factors to consider is the proximity to the data sources and sinks. Cross-region reads and writes can incur significant costs and latency due to network bandwidth and data transfer fees.
Therefore, whenever possible, compute should be deployed in the same region the data is stored to optimize performance and reduce costs.

NEW QUESTION # 94
Which approach demonstrates a modular and testable way to use DataFrame transform for ETL code in PySpark?

Answer: B

Explanation:
Using DataFrame.transform with a pure transformation function promotes modular, reusable, and easily testable ETL logic. Each transformation is encapsulated as a standalone function, can be independently unit tested, and composed cleanly in a pipeline without coupling to orchestration or class state.

NEW QUESTION # 95
Which REST API call can be used to review the notebooks configured to run as tasks in a multi- task job?

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

Answer: D

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

NEW QUESTION # 96
A data engineer is implementing Unity Catalog governance for a multi-team environment. Data scientists need interactive clusters for basic data exploration tasks, while automated ETL jobs require dedicated processing. How should the data engineer configure cluster isolation policies to enforce least privilege and ensure Unity Catalog compliance?

A. Create compute policies with STANDARD access mode for interactive workloads and DEDICATED access mode for automated jobs.
B. Use only DEDICATED access mode for both interactive workloads and automated jobs to maximize security isolation.
C. Configure all clusters with NO ISOLATION_SHARED access mode since Unity Catalog works with any cluster configuration.
D. Allow all users to create any cluster type and rely on manual configuration to enable Unity Catalog access modes.

Answer: A

Explanation:
Unity Catalog enforces governance and data isolation through cluster access modes and compute policies. According to Databricks documentation, "Interactive clusters that multiple users share should use Standard access mode, while automated jobs and production pipelines should use Dedicated access mode for stricter isolation." Standard access mode allows multiple users to share the same compute resources but still respects Unity Catalog permissions. Dedicated access mode isolates the job run's execution environment, ensuring that data access is limited to the job's identity. Configuring these modes within compute policies enforces least privilege and ensures all compute complies with Unity Catalog security standards. Options A and C are incorrect because using only Dedicated clusters reduces resource efficiency, while "No isolation" clusters are not Unity Catalog compliant.

NEW QUESTION # 97
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