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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q238-Q243):
NEW QUESTION # 238
While setting up profiles.yml, a colleague suggests hardcoding database passwords directly. You propose alternative approaches for the sake of security. Which of the following are valid arguments in favor of your approach?
- A. Credentials often need to be rotated, and hardcoding them would make updates cumbersome.
- B. Hardcoded credentials can be accidentally committed to version control, posing a security risk.
- C. Using environment variables or secrets managers improves maintainability and auditability of credentials.
- D. All of the above.
Answer: D
Explanation:
A: Version control should never contain sensitive data. B. Security practices often necessitate credential rotation. C: Centralizing credentials allows for more secure and controlled management
NEW QUESTION # 239
You must exclude a specific model from being included in the generated documentation entirely. How would you achieve this?
- A. Add a dæs: {enabled: false} entry to the model's YAML configuration.
- B. Use the enabled flag in your dbt_projectyml file to control model inclusion.
- C. Remove all descriptions and tags associated with the model.
- D. Its not possible to exclude individual models; you can only exclude entire folders.
Answer: B
Explanation:
The enabled flag in dbt_project.yml provides control over whether a model (or other resources) are includedin dbt operations, including doc generation. Check your dbt version documentation for the latest syntax
NEW QUESTION # 240
28. Consider this DAG:
* model_a # model_c # model_e
* model_b # model_d # model_f
(With model_c and model_d both feeding into the final layer.)
You execute:
dbt run --fail-fast
in production with 2 threads. During the run, model_b and model_c are running in parallel when model_b returns an error.
Assume there are no other errors in the model files, and model_c was still running when model_b failed.
Which model or models will successfully build as part of this dbt run? Choose 1 option.
- A. model_a, model_c
- B. model_a
- C. model_a, model_c, model_d, model_e, model_f
- D. model_a, model_c, model_e
Answer: A
Explanation:
The --fail-fast flag tells dbt to stop scheduling any new nodes as soon as one node fails. Importantly, dbt does not kill models that are already running; in-flight nodes are allowed to finish.
Here's what happens step by step with 2 threads:
* Roots model_a and model_b start first.
* model_a finishes successfully. That makes model_c eligible to run.
* dbt now runs model_b and model_c in parallel.
* While they are running, model_b fails.
* Because --fail-fast is set, dbt immediately stops scheduling any additional models (like model_d, model_e, or model_f).
* model_c was already running when model_b failed, so it is allowed to complete successfully.
Downstream models of either branch (model_d, model_e, and model_f) never start, because fail-fast prevents any further nodes from being queued after the first failure.
So, the only models that successfully build during this run are:
* model_a (completed before model_b failed)
* model_c (already running at the time of failure and allowed to finish) Hence the correct choice is B: model_a, model_c.
NEW QUESTION # 241
You've finished a new feature but realize some related models now have minor formatting inconsistencies (e.g., spacing, indentation). What's the best way to handle this alongside your feature changes?
- A. Address the formatting issues in the same commits as the feature to avoid extra work.
- B. Use git add -p to stage only the feature-related changes, and leave formatting for a future task.
- C. Create a separate branch and commit addressing only the formatting.
- D. Briefly explain in your pull request description that some incidental formatting changes were included
Answer: C
Explanation:
Separating formatting fixes improves code review and history clarity. It allows reviewers to focus on the feature's functionality first and address formatting in a dedicated way.
NEW QUESTION # 242
You define a new generic test on model customers in a YAML file:
version: 2
models:
- name: customers
columns:
- name: customer_id
tests:
- unique
- not_null
The next time your project compiles you get this error:
Raw Error:
mapping values are not allowed in this context
in "<unicode string>", line 7, column 21
What is the cause of this error?
- A. unique and not_null should not be elements in a list
- B. tests should be wrapped in double quotes (")
- C. tests should be a dictionary key, not a list
- D. unique and not_null should be indented at the same level as tests
Answer: D
Explanation:
This error occurs because the YAML structure is incorrectly indented, causing dbt's parser (and YAML itself) to misinterpret the test definitions. In dbt, generic tests must be declared as a list under the tests: key, but YAML is extremely sensitive to indentation levels. In the faulty YAML, unique and not_null are indented incorrectly relative to the tests: key, which produces the error: "mapping values are not allowed in this context." According to the dbt Testing documentation, valid generic test syntax follows this exact pattern:
columns:
- name: id
tests:
- unique
- not_null
The indentation under tests: must be consistent and aligned so that YAML interprets the items as list elements, not as malformed mappings. When indentation is wrong, YAML attempts to parse list entries as key-value mappings, which leads to the error seen during compilation.
dbt does not require generic test names to be quoted, nor does it expect tests to be a dictionary. The test list format is correct-only the indentation is wrong. Therefore, the root cause is incorrect YAML indentation, making Option D correct.
NEW QUESTION # 243
......
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