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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Monitoring and Troubleshooting | 16% | - Performance Optimization - Troubleshooting - Monitoring |
| Databricks Lakehouse Platform | 24% | - Lakehouse Architecture - Unity Catalog - Data Management - Delta Lake |
| Data Processing | 28% | - ETL Pipelines - Data Transformation - Structured Streaming - Spark SQL |
| Data Modeling and Storage | 20% | - File Formats - Data Modeling - Storage Optimization |
| Data Quality and Governance | 12% | - Governance - Data Quality - Data Lineage |
Databricks Certified Data Engineer Professional Sample Questions:
A data engineer has configured their Databricks Asset Bundle with multiple targets in databricks.yml and deployed it to the production workspace. Now, to validate the deployment, they need to invoke a job named my_project_job specifically within the prod target context.
Assuming the job is already deployed, they need to trigger its execution while ensuring the target- specific configuration is respected. Which command will trigger the job execution?
- A. databricks execute my_project_job -e prod
- B. databricks run my_project_job -t prod
- C. databricks bundle run my_project_job -t prod
- D. databricks job run my_project_job --env prod
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Which method can be used to determine the total wall-clock time it took to execute a query?
- A. In the Spark UI, take the sum of all task durations that ran across all stages for all jobs associated with that query.
- B. Open the Query Profiler associated with that query and use the Aggregated task time metric.
- C. In the Spark UI, take the job duration of the longest-running job associated with that query.
- D. Open the Query Profiler associated with that query and use the Total wall-clock duration metric.
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A Data Engineer is building a fraud detection pipeline that calls out to Open AI, via a Python library, and needs to include an access token when using the API. Which Databricks CLI command should the Data Engineer use to create the secret?
- A. databricks tokens put-token KEY SCOPE; dbutils.secrets.get (KEY, SCOPE)
- B. databricks secrets put-secret KEY SCOPE; dbutils.secrets.get (KEY, SCOPE)
- C. databricks tokens put-token SCOPE KEY; dbutils.tokens.get (SCOPE, KEY)
- D. databricks secrets put-secret SCOPE KEY; dbutils.secrets.get (SCOPE, KEY)
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In order to facilitate near real-time workloads, a data engineer is creating a helper function to leverage the schema detection and evolution functionality of Databricks Auto Loader. The desired function will automatically detect the schema of the source directly, incrementally process JSON files as they arrive in a source directory, and automatically evolve the schema of the table when new fields are detected.
The function is displayed below with a blank:
Which response correctly fills in the blank to meet the specified requirements?
- A.
- B.
- C.
- D.
- E.
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A member of the data engineering team has submitted a short notebook that they wish to schedule as part of a larger data pipeline. Assume that the commands provided below produce the logically correct results when run as presented.
Which command should be removed from the notebook before scheduling it as a job?
- A. Cmd 4
- B. Cmd 3
- C. Cmd 2
- D. Cmd 5
- E. Cmd 6
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