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

Databricks Certified Data Engineer Professional Sample Questions:

1. Which statement describes Delta Lake Auto Compaction?

A) Optimized writes use logical partitions instead of directory partitions; because partition boundaries are only represented in metadata, fewer small files are written.
B) Data is queued in a messaging bus instead of committing data directly to memory; all data is committed from the messaging bus in one batch once the job is complete.
C) An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 1 GB.
D) Before a Jobs cluster terminates, optimize is executed on all tables modified during the most recent job.
E) An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 128 MB.

2. Which statement describes a key benefit of an end-to-end test?

A) It makes it easier to automate your test suite
B) It provides testing coverage for all code paths and branches.
C) It closely simulates real world usage of your application.
D) It pinpoint errors in the building blocks of your application.

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

4. 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 6
D) Cmd 2
E) Cmd 5

5. A data engineering team is setting up a Git project to automate integration tests using Databricks Asset Bundles and the Git provider's CI/CD functionalities. When a pull containing changes to their pipleline is sent, they need to run a Job to test their data pipeline. What is the correct databricks bundle command sequence to be executed from the Git provider's CI/CD automation for this task?

A) init, validate, deploy, run
B) deploy, run, validate
C) init, deploy, run, validate
D) validate, deploy, run

Solutions: