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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. (Multiple Select)

A) Misspelling "orders" as "orderes".
B) The error_after freshness check syntax.
C) Indenting freshness incorrectly under the customers table.
D) Using an 's' at the end of tables.

2. A colleague asks, "Every time I make changes to my dbt_project.yml I have to wait for errors during dbt compile. Is there a better way?" What would you suggest?

A) Have them write dbt schema tests for their configuration files.
B) Install a YAML linter within their code editor to catch formatting and basic schema errors.
C) Manually validate the dbt_project.yml against a fixed template file after each edit
D) Thoroughly review dbts documentation for a complete list of valid properties to memorize.

3. Your dbt models create tables, but you notice a consistent "_dbt" suffix appended to all table names. Where does this likely originate from, and how could you modify it?

A) This behavior is configurable in your dbt_projectyml settings related to model schemas.
B) There's likely a custom macro in your codebase adding the suffix
C) It's a naming convention enforced by the database engine, not directly controlled by dbt
D) Its a default behavior of dbt to help distinguish model names from pre-existing tables.

4. You've added detailed descriptions to your sources.yml file. Which of the following scenarios would still necessitate adding descriptions directly within your model files?

A) If the source table undergoes a schema change, and you want to document these changes historically within the model.
B) You always need both source and model-level descriptions for complete documentation.
C) If you want to provide additional context or logic specific to how a source is used within that model.
D) If different models utilize the same source table for different purposes.

5. After implementing thorough dbt tests, you confidently deploy changes. Yet, downstream Bl dashboards break, and stakeholders complain about inaccurate dat a. What could have gone wrong?

A) There are logic errors in your dbt models that the tests didn't catch.
B) Your tests were correct, but a bug in dbt prevented them from running successfully.
C) The tests you wrote were too strict, causing false positives.
D) Critical assumptions were made about the data in the Bl layer, independent of dbt models.

Solutions: