dbt Labs dbt-Analytics-Engineering Actual Tests : dbt Analytics Engineering Certification Exam

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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. A critical model depends on a third-party data source with periodic update delays. How could you structure your DAG to mitigate the impact of these delays on downstream reporting?

A) Utilize the 'defer' option to allow upstream models to run while awaiting the source update.
B) Configure a 'view' that gracefully handles missing data from the third-party source.
C) Employ a snapshot of the third-party source to create a historical record.
D) Set up an alert to notify users when the upstream data source is not refreshed on time.

2. A team member suggests building a dbt package for a set of reusable transformations your team frequently uses. When is this MOST likely a good idea?

A) The transformations are complex and involve domain-specific logic that might be beneficial to others.
B) The transformations are project-specific but used across multiple models.
C) The transformations are relatively simple and could be written as macros.
D) You need to limit access to these transformations to a specific team within your organization.

3. You introduce a new test, which unexpectedly leads to many models previously deemed 'successful' now failing.
What should your initial troubleshooting steps include?

A) Check if the test is revealing previously overlooked data quality problems.
B) Review the test's severity configuration (warning vs_ error).
C) Verify the test logic doesn't contain errors.
D) Temporarily raise the test's failure threshold to give you time to investigate.

4. A project stakeholder requests access to an intermediate stage of a dbt transformation pipeline to validate specific calculations.
You need to weigh this request carefully because:

A) Exposing intermediate results can lead to premature analysis based on incomplete data.
B) All of the above-
C) Granting access to intermediate data may confuse stakeholders who are not familiar with the full transformation process-
D) Raw data, development data, and production data often have different levels of data quality, completeness, and timeliness.

5. A newly-hired analyst needs to update a downstream model that depends on a chain of intermediate models. To minimize the potential for disruption, how can modularity help them?

A) By using macros, modifications are limited to a single location.
B) Snapshots ensure that historical data won't be impacted unintentionally.
C) Incremental materializations will help verify that the changes are successful.
D) With clear dependencies, they can pinpoint which upstream models to test.

Solutions: