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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. When integrating a new data source, you notice many existing models could benefit from it. What's the most maintainable way to incorporate the new data within a DRY approach?

A) Establish a naming convention for the new data elements to avoid hardcoding them within model SQL.
B) Build a snapshot of the new data combined with relevant data from existing models.
C) Add the new data source directly to each relevant model's SQL query.
D) Create a base model that incorporates the new data source and use the ref() function in downstream models.

2. You're setting up a CI/CD pipeline for dbt deployments. Which actions can help maintain data integrity and consistency between your development, staging, and production environments?

A) Enforce a branching strategy that mirrors environment progression (e.g., dev branch deploys to dev, etc. )
B) Utilize dbt logs and artifacts to track any changes made outside the standard deployment pipeline in each environment.
C) All of the above.
D) Implement automated tests that validate data quality and model assumptions across all environments.

3. A colleague asks, "Every time I make changes to my dbt_project.yml I have to wait for errors during dbt compile. Is there a better way?" What would you suggest?

A) Install a YAML linter within their code editor to catch formatting and basic schema errors.
B) Have them write dbt schema tests for their configuration files.
C) Thoroughly review dbts documentation for a complete list of valid properties to memorize.
D) Manually validate the dbt_project.yml against a fixed template file after each edit

4. You have complex dbt models involving incremental logic based on a timestamp column. To debug a production issue, you want to temporarily force a full rebuild of a specific incremental model. Which dbt command-line options could help?

A) Write a macro within the model to switch behavior based on an environment variable.
B) Add the -full-refresh flag to the dbt run command.
C) Use dbt artifacts or the compilation context to access the current run's timestamp and override model logic.
D) Manually update the timestamp column in the incremental model's output table.

5. You introduce a minor formatting change to a large SQL model. Afterwards, the results change unexpectedly. Which dbt-related issue might explain this, even if the SQL itself appears correct?

A) An upstream data issue only surfaces due to the formatting change making that part of the query execute differently.
B) There's a version mismatch between the dbt library used during development and in production.
C) You've used a reserved keyword as a column alias without properly quoting it.
D) A subtle indentation change in a CTE accidentally modifies the execution order within the query.

Solutions: