Databricks-Certified-Professional-Data-Engineer: Databricks Certified Data Engineer Professional Junior Practice Questions
The free Databricks-Certified-Professional-Data-Engineer: Databricks Certified Data Engineer Professional questions that deal with junior, with answers and explanations. The full bank and the timed practice test cover every topic the exam asks about.
Question #6
The data engineer team is configuring environment for development testing, and production before beginning migration on a new data pipeline. The team requires extensive testing on both the code and data resulting from code execution, and the team want to develop and test against similar production data as possible. A junior data engineer suggests that production data can be mounted to the development testing environments, allowing pre production code to execute against production data. Because all users have Admin privileges in the development environment, the junior data engineer has offered to configure permissions and mount this data for the team. Which statement captures best practices for this situation?
Correct answer: C
Explanation
The best practice in such scenarios is to ensure that production data is handled securely and with proper access controls. By granting only read access to production data in development and testing environments, it mitigates the risk of unintended data modification. Additionally, maintaining isolated databases for different environments helps to avoid accidental impacts on production data and systems. References: • Databricks best practices for securing data: https://docs.databricks.com/security/index.html
Question #8
A junior data engineer has configured a workload that posts the following JSON to the Databricks REST API endpoint 2.0/jobs/create. Assuming that all configurations and referenced resources are available, which statement describes the result of executing this workload three times?

Correct answer: C
Explanation
This is the correct answer because the JSON posted to the Databricks REST API endpoint 2.0/jobs/create defines a new job with a name, an existing cluster id, and a notebook task. However, it does not specify any schedule or trigger for the job execution. Therefore, three new jobs with the same name and configuration will be created in the workspace, but none of them will be executed until they are manually triggered or scheduled. Verified References: [Databricks Certified Data Engineer Professional], under “Monitoring & Logging” section; [Databricks Documentation], under “Jobs API - Create” section.
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