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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

dbt Labs dbt Analytics Engineering Certification Sample Questions:

Question 1

You discover that dbt models run significantly slower in your production environment than in development. What potential causes should you investigate?

A. Inefficient use of snapshots or incremental models in your production dbt project_
B. Production data volume being significantly larger than development data.
C. Network latency impactingressing access to remote data sources.
D. Different database configurations or resource allocations (memory, CPU, etc.) between environments-

Question 2

You're tasked with adding functionality to your dbt project for:

A. Calculating complex financial metrics specific to your industry
B. Handling slowly changing dimensions.
C. Auditing data quality with comprehensive anomaly detection tests.

Question 3

A team member has opened a pull request, but conflicts arise when you attempt to merge it. Which of the following are essential steps in the conflict resolution process?

A. All of the above.
B. Communicate with your team member to understand the intent behind the conflicting changes.
C. Utilize Git's built-in merge conflict markers to identify areas of disagreement
D. Manually edit the affected files in your favorite text editor to resolve the differences.

Question 4

You've written dbt tests that assume a certain distribution of values in a column. Later, the upstream process generating the data changes unexpectedly. What's the most likely consequence?

A. The tests will fail, preventing the changed data from being loaded, preserving the old behavior.
B. The changed data distribution has no impact on downstream dbt models.
C. dbt will automatically adjust your tests to accommodate the new distribution.
D. Your dbt models may silently produce incorrect results due to the changed distribution.

Question 5

"The compiled SQL looks correct, but I'm still getting wrong results in my model!" What might you suggest to a colleague having this issue?

A. All of the above.
B. Review the logic in 'on-run-start' or 'on-run-end' hooks, as they could be modifying data-
C. Test the model in isolation using the dbt run -select flag to narrow down dependencies.
D. Examine the source tables for data quality issues like duplicates or missing values.

Solutions: