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dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. A marketing team needs a model to calculate the 30-day rolling average of new customer signups. The raw signups table is very large and updated hourly. Which approach strikes the best balance between accuracy and computational efficiency?

A) A table materialization that completely rebuilds the rolling average daily.
B) An incremental model that updates the rolling average calculations only for new signup records.
C) An ephemeral model that calculates the rolling average within a series of complex CTEs.
D) A view materialization that recalculates the 30-day rolling average for each query.

2. Your dbt project contains models that process data with varying levels of sensitivity. Which of the following might be reasons to utilize different schemas or even different targets within your data warehouse for these data sensitivity levels?

A) To optimize query performance, as certain models might benefit from specific data warehouse configurations based on their workload.
B) To implement stricter access controls limiting which users or roles can interact with sensitive data-
C) All of the above-
D) To comply with data governance regulations that mandate separation of different data types.

3. You want to include a detailed lineage diagram in your project documentation. Which of these approaches would you likely consider?

A) Use the built-in lineage functionality within dbt's standard documentation generation.
B) Manually create a diagram using a separate tool and add an image reference in a model's description.
C) Configure a third-party dbt package designed for enhanced lineage and visualization.
D) Write a custom macro that generates a lineage diagram and include it in relevant model descriptions.

4. Performance Bottleneck

A) Implement database-level indexes on columns involved in the joins, if appropriate.
B) Run EXPLAIN or equivalent on the compiled query to pinpoint the most expensive operations.
C) Add -full-refresh to dbt run on the model to ensure downstream models don't cause errors.
D) Introduce CTEs to pre-calculate aggregated data or common subexpressions, especially if reused repeatedly.

5. You've finished a new feature but realize some related models now have minor formatting inconsistencies (e.g., spacing, indentation). What's the best way to handle this alongside your feature changes?

A) Address the formatting issues in the same commits as the feature to avoid extra work.
B) Use git add -p to stage only the feature-related changes, and leave formatting for a future task.
C) Create a separate branch and commit addressing only the formatting.
D) Briefly explain in your pull request description that some incidental formatting changes were included

Solutions: