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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. A source table is updated with a full overwrite every night. Your dbt job fails due to freshness checks. What's a likely cause and solution?

A) The loaded_at field is absent Add the corresponding column or remove the check-
B) You need to schedule the dbt job to run after the nightly update.
C) The freshness check is misconfigured- Adjust the count and period settings.
D) The source uses an incremental materialization. Change it to a 'table'

2. (Multiple Select)

A) There's an urgent request to update a single dashboard, and you know the exact subset of models to re-run.
B) You need to temporarily disable a computationally expensive model that isnt critical for daily reporting.
C) A critical upstream data source has been delayed, and you only want to refresh dependent models once the data arrives.
D) You've refactored a core model and need to quickly validate its output and downstream dependencies.

3. A downstream process relies on a critical model to be fully refreshed daily. How can you enforce this behavior using dbt_project.yml configuration?

A) Update the model configuration to materialized: table and add a persist_docs: true property.
B) Configure a schedule property within the models: section.
C) Set the model's materialized property to incremental _ refresh-
D) Apply a post-hook that triggers a full refresh of the model.

4. You're onboarding a new team member and need to set them up with a personal sandbox environment within your existing project. What factors do you need to consider?

A) How to configure a profile on their machine and guide them to isolate changes to their sandbox.
B) How to provision data warehouse resources if needed, such as a separate schema or potentially even a dedicated database.
C) All of the above.
D) Whether they need access to production data replicas and how to control this securely

5. A model passes its tests during development, but later fails when deployed on a less powerful production database. Which of the following might be the reason for this discrepancy?

A) There's a bug in the version of dbt that runs in production.
B) Source data distributions differ significantly between environments.
C) Performance-related test thresholds are too lenient in development.
D) The test was accidentally disabled in the production dbt project.

Solutions: