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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q34-Q39):

NEW QUESTION # 34
You're implementing singular tests (accepted values). After providing a list of valid values, you notice some strange behavior: the test is passing even with records that contain unexpected dat a. What's the most likely issue?

A. The accepted_values test configuration has the wrong column specified.
B. The model's data type isn't compatible with the accepted_values test.
C. A downstream process is overwriting the results after the model is built.
D. Your test case is accidentally permitting null values, which bypass the check.

Answer: A,B,D

Explanation:
Each of these directly impacts the accepted_values test's effectiveness, while D is unlikely for a singular test.

NEW QUESTION # 35
What must happen before you can build models in dbt?
Choose 1 option.

A. Sources must have been defined in your dbt project.
B. Raw data must be cleaned.
C. Underlying data must be accessible on your data platform.
D. You must have created a service account in your data platform.

Answer: C

Explanation:
The correct answer is C: Underlying data must be accessible on your data platform.
dbt does not perform data ingestion or data loading. Instead, dbt operates after raw data is already available in your warehouse. This means that before dbt can build any models-whether staging, intermediate, or mart- layer models-the underlying source data must already exist and be accessible in the connected data platform (Snowflake, BigQuery, Redshift, Databricks, etc.). dbt uses SQL to transform existing relations; therefore, if the data platform cannot access the underlying tables or external sources, model execution will fail.
Option A is incorrect because sources do not need to be defined before building models. Models can be built without using sources at all. Source definitions are optional metadata and lineage declarations, not prerequisites.
Option B is incorrect because service accounts are not required; dbt can connect through any credential mechanism supported by the warehouse (OAuth, user accounts, tokens, etc.).
Option D is incorrect because dbt itself performs transformations on raw data-cleaning raw data beforehand is not required; in fact, that is one of dbt's main responsibilities.
Thus, the only true prerequisite is that the warehouse must contain accessible underlying data.

NEW QUESTION # 36
You're integrating a new dbt package and find its models conflict with naming conventions in your existing project. Which approach generally provides the most flexible solution?

A. Override the package's macros and redefine them within your project.
B. Manually edit the SQL within the conflicting package models.
C. Contact the package maintainers to request changes aligning with your conventions.
D. Utilize the dbt_project.yml file to re-alias the models provided by the package.

Answer: D

Explanation:
Re-aliasing at the project level offers a centralized and maintainable way to manage naming conflicts. Editing package code directly should be avoided if possible.

NEW QUESTION # 37
You're onboarding new developers to your dbt project. To simplify their setup, you want to provide a standard profiles.yml template. What should you consider when doing this?

A. Providing example dbt commands as comments directly within the file to guide new users.
B. Referencing environment variables to allow for easy customization by developers.
C. Prepopulating common credentials to make the initial setup hassle-free.
D. Including different target configurations to illustrate switching between development and production.

Answer: A,B,D

Explanation:
B promotes flexibility and security (avoids storing sensitive data). C demonstrates good practices. D enhances usability A is a major security risk!

NEW QUESTION # 38
While onboarding new data sources to your dbt project, you discover some have unexpectedly high null rates in key columns. How would you integrate tests for this into your dbt workflow?

A. Create not_null tests on the raw source tables in a separate dbt project dedicated to source validation.
B. Write tests within the initial staging models that calculate and check the null percentage.
C. Designate some nulls as acceptable by extending the not_null test with the OR condition.
D. Include a data quality monitoring step in your upstream ETL process before data reaches dbt

Answer: B,D

Explanation:
B ensures data entering dbt is checked at the first opportunity. C pushes quality checks as far upstream as possible- A is valid, but less integrated if sources are managed outside dbt's purview D masks the problem rather than addresses it.

NEW QUESTION # 39
......

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