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dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. A critical model fails with a non-descriptive error. You suspect a third-party package used in the model. How might you narrow down the problem?

A) Run the package's models in isolation to see if the error reproduces.
B) Examine recent commits or releases of the package for changes that might have introduced a bug.
C) Temporarily disable the package and try running the model.
D) Add -log-format json to your dbt run command to get more structured error output.

2. You establish a process using dbt tests and snapshots to track data quality changes over time. To maximize the utility of this historical record for troubleshooting, what additional step would be beneficial?

A) Schedule regular alerts whenever a test result deviates significantly from previous runs.
B) Automatically purge snapshots older than a certain date to save storage resources.
C) Integrate metadata about dbt model versions and schema changes alongside the snapshots.
D) Convert the dbt test results and snapshots into time-series tables for easier trend analysis.

3. Your data warehouse environment undergoes a major version upgrade, introducing changes in SQL syntax and supported functions. How would you handle the migration of dbt projects to ensure compatibility?

A) Utilize custom macros to introduce conditional logic or abstract away syntax differences between versions, if feasible.
B) All of the above.
C) Thoroughly test all dbt models across your staging and development environments to identify necessary updates.
D) Create temporary branches in your dbt projects to isolate upgrade-related code changes for each environment

4. While refactoring models, you accidentally drop a table in your development environment that mirrors production. Which inherent characteristic of development environments likely contributed to this issue?

A) Development environments often have less stringent permissions to facilitate experimentation.
B) Developers working in development environments might expect the ability to make and revert changes with fewer consequences.
C) Both A and C-
D) Development datasets might be incomplete or outdated compared to productiom

5. You've finished a new feature but realize some related models now have minor formatting inconsistencies (e.g., spacing, indentation). What's the best way to handle this alongside your feature changes?

A) Create a separate branch and commit addressing only the formatting.
B) Use git add -p to stage only the feature-related changes, and leave formatting for a future task.
C) Briefly explain in your pull request description that some incidental formatting changes were included
D) Address the formatting issues in the same commits as the feature to avoid extra work.

Solutions: