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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q284-Q289):

NEW QUESTION # 284
Which is true about writing generic tests?
Choose 1 option.

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

Answer: E

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 # 285
Which of the following is NOT a direct benefit of thorough source, table, and column descriptions in dbt?

A. Faster dbt model compilation and execution times.
B. Better data quality due to the automated enforcement of column descriptions
C. Increased discoverability for other team members working on the project
D. Improved lineage and data traceability within the documentation.

Answer: A

Explanation:
Explanation: While good descriptions have numerous benefits, they don't inherently speed up model compilation and execution within dbt-

NEW QUESTION # 286
You have a dbt model that performs complex aggregations. During development, you notice some models take excessively long to run, but they ultimately complete without errors. Which of the following might be true?

A. This behavior automatically triggers an optimization feature within dbt.
B. Downstream models will inherit the performance issues.
C. These models likely have issues with joins or CTE structure impacting performance.
D. The models are at risk of failing if the source data grows substantially.

Answer: C,D

Explanation:
A and B are likely true. Models without errors can still signify potential scaling issues and inefficient logic. C is not necessarily true. Downstream models won't inherit performance issues unless they directly rely on the output of these slow models. D is false. dbt has optimization features, but they often require manual configuration, not automatic triggering.

NEW QUESTION # 287
(Multiple Select)

A. Downstream models relying on the staging model will continue to function.
B. The dbt run will succeed, but the staging model will have null values due to the missing data.
C. The staging model run will fail.
D. dbt will issue a warning but continue processing.

Answer: B,C

Explanation:
A and C are correct. A missing dependency will cause SQL errors, and nulls would likely occur where the dropped table's data was used. B is incorrect. Downstream models will often fail if an upstream model fails. D is incorrect. dbt's strict dependency handling is likely to stop the process and throw errors.

NEW QUESTION # 288
Regular test runs reveal inconsistent and intermittent test failures, despite no obvious changes to models or source dat a. What debugging strategies might you use?

A. Temporarily switch to a more consistent testing environment.
B. Use dbfs logging feature to add detailed output to the test execution.
C. Examine data warehouse performance metrics for resource constraints during test runs.
D. Consider if external dependencies used within dbt tests are themselves unstable.

Answer: B,C,D

Explanation:
Each helps track down potential causes of non-deterministic test behavior. D might be too drastic early on.

NEW QUESTION # 289
......

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