dbt Labs dbt-Analytics-Engineering : 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. You notice that your dbt jobs take longer than expected. You suspect potential issues with models referencing large raw tables unnecessarily. Which of the following strategies might help optimize performance? (Choose two)

A) Introduce CTEs within your models to pre-filter raw data where possible.
B) Move the raw table into a separate staging schema and grant access only to necessary models.
C) Add more indexes to all the raw tables in your warehouse.
D) Convert your most-used models to incremental materializations.

2. (Multiple Select)

A) Tables persist their results on disk, consuming storage resources.
B) Tables, by default, are rebuilt completely with each dbt run.
C) Tables generally offer the fastest query performance for pre-computed models.
D) Tables are well-suited for datasets that undergo frequent, wholesale changes.

3. You've updated a model's description in a YAML file to reflect a new business requirement. You've also added additional context to the column descriptions. After regenerating the documentation, you notice the model description has updated, but the column descriptions have not. What could be the most likely reasons?

A) You forgot to run dbt docs generate after modifying the YAML file.
B) The documentation was not configured correctly to display column descriptions.
C) You are viewing an older cached version of the documentatiom
D) The model's YAML file has incorrect syntax preventing column descriptions from rendering.

4. A large dimension table is joined frequently in your dbt models. Due to its size, these joins significantly impact performance. Which strategy might offer improvement?

A) Investigate database-specific techniques like hash joins or distribution keys for optimizing the join.
B) If the dimension data rarely changes, convert it to an incremental model.
C) Add a WHERE clause to the model to filter out unnecessary rows during the join.
D) Rewrite the join logic by nesting conditions within a CTE.

5. You're using dbt Cloud and want to tighten control over when production deployments can occur. What dbt Cloud feature, in conjunction with your CI/CD configuration, would allow you to enforce this?

A) Environment variables for sensitive credentials.
B) dbt Cloud's built-in source freshness checks.
C) Deployment jobs and schedules.
D) Webhooks for triggering jobs by external events.

Solutions: