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 have a dbt model that depends on several upstream source tables. One of these source tables is occasionally updated very late, causing your job to fail if triggered at the usual time. What configuration could mitigate this?

A) Redesign the model with an incremental materialization strategy that gracefully handles partial updates.
B) Set the -full-refresh flag for the dbt job to ensure all tables are materialized.
C) Configure a dbt hook to run before the job, dynamically checking freshness of the source table.
D) Add a depends_on relationship pointing to the potentially late table within your model.

2. A performance-critical model depends on multiple complex transformations that would benefit from being pre-computed. Direct querying of this model should be as fast as possible. Which materialization is likely the best choice?

A) Ephemeral
B) View
C) Incremental
D) Table

3. 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A) Development datasets might be incomplete or outdated compared to productiom
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 environments often have less stringent permissions to facilitate experimentation.

4. Select the dbt materialization that typically DOES NOT persist its results to disk:

A) Ephemeral
B) Incremental
C) Snapshot
D) Table

5. Business requirements call for a report ranking products based on year-over-year (YoY) revenue growth. Historical data must be preserved. What's a potential issue in implementing this directly in a dbt model?

A) ref() functions can hinder performance when working with historical data.
B) Incremental materializations aren't suitable for trend analysis.
C) Data warehouses might not be optimized for period-over-period comparisons.
D) Views cannot perform complex date manipulation required for YoY calculations.

Solutions: