Databricks Certified Data Engineer Professional : Databricks-Certified-Data-Engineer-Professional Exam Questions

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

Databricks Certified Data Engineer Professional Sample Questions:

1. A data architect is implementing Delta Sharing as part of their data governance strategy to enable secure data collaboration with external partners and internal business units. The architect must establish a permission framework that allows designated data stewards to create shares for their respective domains while maintaining security boundaries and audit compliance. Which specific permissions and roles must be assigned to enable users to create, configure, and manage Delta Shares while maintaining proper security governance and access controls?

A) Only workspace admins can create and manage shares
B) Any user with USE_CATALOG privilege can create shares
C) Users need the MANAGE SHARES permission on the workspace
D) Users need to be metastore admins or have CREATE SHARE privilege for the metastore

2. A faulty IoT sensor in a factory reports a temperature of -500, causing the LDP pipeline to fail the expectation, which only allows values between -100 and 200 degrees Celsius. The data engineer would like to further analyze the faulty data to better understand the reason behind this. How should the data engineer resolve the faulty data while ensuring data quality standards are maintained?

A) Ignore the error and simply re-run the pipeline, as Databricks will automatically skip the problematic record on the next run.
B) Change the expectation action from fail to warn so that invalid records are included in the output and the pipeline does not fail.
C) Fix the pipeline code and implement a quarantine logic to isolate the faulty data before re-running the pipeline.
D) Remove all expectations form the pipeline to prevent any future failures, regardless of data quality.

3. An analytics team wants to run a short-term experiment in Databricks SQL on the customer transactions Delta table (about 20 billion records) created by the data engineering team. Which strategy should the data engineering team use to ensure minimal downtime and no impact on the ongoing ETL processes?

A) Create a new table for the analytics team using a CTAS statement.
B) Shallow clone the table for the analytics team.
C) Deep clone the table for the analytics team.
D) Give the analytics team direct access to the production table.

4. A data engineer is implementing a job to download multiple PDF files from a third-party provided REST API endpoint by specifying different report types. The REST API is time-consuming and encounters intermittent errors, so the engineer wants to track each download activity to know when it fails and to retry partially, while providing scalable throughput. The engineer needs to download ten report types, and the list can be changed over time. How should the data engineer achieve this?

A) Define a list variable within a Notebook to loop through the report types to download them, and print the download results. Execute it as a Notebook tasks.
B) Use a foreach task with a list of report types as its inputs.
C) Define ten Notebook tasks to clearly track which report download failed.
D) Use a Delta Lake table to track each report download status as 10 rows, and use it as a source table to execute the download function as a Pandas UDF.

5. Which statement regarding stream-static joins and static Delta tables is correct?

A) Each microbatch of a stream-static join will use the most recent version of the static Delta table as of the job's initialization.
B) The checkpoint directory will be used to track updates to the static Delta table.
C) The checkpoint directory will be used to track state information for the unique keys present in the join.
D) Stream-static joins cannot use static Delta tables because of consistency issues.
E) Each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch.

Solutions: