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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Deployment and Orchestration	15%	- Git version control integration - CI/CD with dbt Cloud - Jobs and scheduling in dbt Cloud - Environments (dev, staging, prod)
Topic 2: dbt Fundamentals	15%	- dbt workflow and best practices - dbt Core vs dbt Cloud - dbt project structure
Topic 3: Testing and Documentation	20%	- Schema tests (unique, not_null, accepted_values, relationships) - Documentation generation - Custom data tests - dbt docs and DAG visualization
Topic 4: Data Transformation Techniques	25%	- Macros and packages - Common table expressions and subqueries - Refactoring and incremental models - Jinja templating
Topic 5: Models	25%	- Seeds - Writing and managing SQL models - Materializations (table, view, ephemeral, incremental) - Snapshots - Sources and references

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q174-Q179):

NEW QUESTION # 174
Which is true about writing generic tests?
Choose 1 option.

A. They are written using the {% macro %} wrapper.
B. They do not need to be specified under a model's YAML configurations.
C. They must contain a {{ ref() }} snippet to a model.
D. They should accept one or both of these arguments: model and column_name.
E. They should always accept a column_name.

Answer: A

Explanation:
The correct answer is E: They are written using the {% macro %} wrapper.
Generic tests in dbt are implemented as macros, which means they must be defined using the {% macro %} Jinja syntax. These macros return a SQL query that evaluates to rows representing test failures. dbt executes these queries and marks the test as failed if any rows are returned. This requirement is explicitly documented:
all generic tests are macros located in a test directory or within a package.
Option A is partially correct but not universal. Generic tests typically accept arguments such as model or column_name, but dbt does not require both or either. Custom tests can accept any parameters defined by the user. Option B is incorrect because tests do not require ref(); dbt injects the model relation for you when the test is executed. Option C is false because generic tests must be declared in YAML under a model or source for dbt to run them. Option D is incorrect because tests do not always need a column_name (e.g., relationship tests, row-count tests, table-level validations).
Therefore, the defining and universally required characteristic is that generic tests are written as macros, making E the correct answer.

NEW QUESTION # 175
You've added new tests to a dbt model but are unsure how to determine the test coverage. What's a practical first step?

A. Examine the output of dbt docs generate and dbt docs serve.
B. Execute dbt test -select failing to identify any currently failing tests.
C. Review the compiled SQL files generated by dbt.
D. Employ a code coverage tool specifically designed for SQL.

Answer: A

Explanation:
dbt docs provides visual indicators of which models have tests, aiding in identifying gaps in your coverage strategy

NEW QUESTION # 176
You're working on a dbt project in a development environment where compute resources are limited. You want to prototype a new model quickly without incurring high materialization overhead. Which materialization would be most suitable for this?

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

Answer: C

Explanation:
Ephemeral materializations are ideal for rapid development. Since they don't persist results, you can iterate on your model's logic without worrying about storage and the compute cost of rebuilding.

NEW QUESTION # 177
You want to display the runtime metrics (e.g., execution time) of each model directly on the DAG node. Which challenges might you face when implementing this?

A. Adding too much detailed information directly to the DAG could lead to visual clutter and make it overwhelming.
B. All of the above
C. dbt documentation is primarily static; it might not be able to render dynamic metrics that change with each execution.
D. Retrieving runtime metrics for each historical run might not be natively supported by dbt

Answer: B

Explanation:
A: You might need to generate static summaries or find workarounds to display dynamic metrics. B. Collecting runtime metrics across all historical runs might require additional logging or integration with dbt artifacts (manifestjson, run_resultsjson)- C: The DAG visualization needs a balance between information and readability.

NEW QUESTION # 178
You've implemented the built-in dbt.get_column_values macro to display sample data values from a source column directly on the DAG. However, you notice performance issues when loading the docs. Which optimization strategy could you consider?

A. Increase the compute resources allocated to your dbt documentation generation process.
B. Utilize a dbt snapshot to materialize the macro results instead of computing them on the fly.
C. Modify the macro to only retrieve sample values during development and not for production documentation.
D. Replace dbt_get_column_values with a custom macro that only fetches the first few sample values.

Answer: B,C,D

Explanation:
B: Disabling expensive computations conditionally can boost performance for production environments. C: Snapshots pre-compute values, potentially improving DAG loading times. D: Reducing the amount of data retrieved by the macro could help with performance.

NEW QUESTION # 179
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