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dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. You're setting up CI/CD for your dbt project to automate deployments. Which aspects of your testing strategy might need adaptation for a production-focused CI/CD workflow?

A) Including smoke tests that run in production to verify core functionality after a deployment.
B) Disabling all tests that rely on creating new tables, to avoid impacting the production database.
C) Prioritizing tests with strict thresholds to avoid false positives from minor data variations in production.
D) Emphasizing tests with longer run times for thorough checks before deployment.

2. You need to set up a production-like environment for load testing and performance tuning of dbt models. How might you approach the resource allocation and provisioning of this environment?

A) Write dbt macros that artificially increase dataset sizes in development for simulating load.
B) Create a CI/CD job that dynamically scales a cloned production environment for the test, then scales it back down.
C) Run performance tests directly against production during a designated off-peak period.
D) Partner with your data infrastructure team to get guidance based on their experience.

3. You're working with a model that aggregates data across many countries. Which assumption about the data might be especially important to test for and handle in your model logic?

A) The presence of unexpected HTML tags within textual data fields.
B) Different regions may use varying currency exchange rates.
C) Encoding formats (UTF-8, etc.) may be inconsistent across source systems.
D) Time zones and daylight savings rules for timestamps could differ between data sources.

4. dbt Model Snippet:SQL

A) The SUM(order_amount) aggregation logic.
B) The source() function retrieving data from the incorrect schema.
C) The GROUP BY month clause leading to duplicate rows.
D) Incorrect Jinja usage within a macro used for templating.

5. (Multiple Select)

A) Minimizing explicit model dependencies to create loosely coupled components.
B) Using clear and consistent naming conventions across models and macros.
C) Preferring long, monolithic SQL files with minimal CTEs.
D) Encapsulating complex logic in well-named macros with descriptive comments.

Solutions: