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dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. You're setting up a development environment in a cloud data warehouse with usage-based billing. How might you approach cost management in this context?

A) Implement automated scripts to shut down the development environment during non-working hours.
B) Set up usage alerts in your data warehouse that notify the team if costs exceed certain thresholds.
C) Create a separate profiles.yml specifically for the development environment that utilizes smaller data warehouse resource classes.
D) Use dbt seeds to load smaller datasets for testing, reducing overall data processing costs.

2. You've merged a pull request but later discover it introduced a regression (unexpectedly broke existing functionality). What's the most common initial step to address this?

A) Open a new pull request with a fix for the regression.
B) Run git revert on the merge commit to undo the changes.
C) Use git blame or git log to pinpoint the commit that caused the issue.
D) Coordinate with the original author of the merged pull request.

3. (Multiple Select)

A) Tests comparing the latest data against historical baselines.
B) Tests checking for row count discrepancies between source systems and target tables.
C) Schema tests to confirm the existence of tables and columns.
D) not_null and unique tests on primary key columns.

4. (Multiple Select)

A) Centralized dbt packages for reusable code
B) Knowledge sharing platforms or internal wikis for documenting best practices.
C) Data discovery platforms to catalog available data assets
D) Automated code linters

5. You introduce performance optimizations to a subset of models. You need to benchmark both old and new versions in an isolated environment before deployment. How might you structure your dbt commands for this comparison?

A) Within a model, conditionally write to different schemas based on an environment variable, preserving both versions.
B) Use feature branches and switch between them with different dbt profiles for old and new code.
C) Utilize dbt seed to populate separate datasets representing the old and new model outputs for side-by-side analysis.
D) Add dbt hooks to temporarily archive the original output tables before overwriting them during benchmark runs.

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