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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q27-Q32):

NEW QUESTION # 27
A developer imports a package from a private repository called timeformat for use within their project.
Which statement is correct? Choose 1 option.
Options:

A. "Including the package version/revision in the packages.yml file, for private git packages will result in an error."
B. "The package default schema can be overridden in the dbt_project.yml file as:" models:
timeformat:
+schema: timeseries
C. "The package can be installed by running the command dbt build."
D. "The package can be added with this configuration in the packages.yml file:" packages:
- local: /opt/dbt/timeformat

Answer: B

Explanation:
The only correct statement is Option C, because dbt allows you to override the schema for a package's models directly within your project's dbt_project.yml file. Packages behave just like subfolders within your own project, and therefore the standard configuration pattern applies:
models:
timeformat:
+schema: timeseries
This correctly overrides the package's default schema, allowing all models within the timeformat package to be built into the timeseries schema.
Why the other options are incorrect
Option A
Local packages can be added using a path reference, but that is not what a private Git repository refers to. A private repo must be added using git: plus SSH/HTTPS URL, not local: unless the developer literally cloned the repo into a local directory. The question specifically describes a private repository, so this is not the correct statement.
Option B
Packages are installed using dbt deps, not dbt build.
dbt build only runs models, tests, and snapshots; it does not install dependencies.
Option D
Including a version or revision is allowed for private Git-based packages. You can specify branches, tags, or commit hashes safely in packages.yml.

NEW QUESTION # 28

Answer:

Explanation:

Explanation:
Drops and recreates a definition of a query which is executed at the time it's referenced
# view
Inserts or updates records of an existing object in the warehouse using a delta
# incremental
Code belonging to the model is inserted into the places the model is referenced
# ephemeral
Drops and recreates an object in the warehouse as stored results when using dbt run
# table

NEW QUESTION # 29
Which two are true about version controlling code with Git?
Choose 2 options.

A. Code can be reverted to a previous state.
B. All the code changes along the lifecycle of a project are tracked.
C. Git automatically creates versions of files with suffixes.
D. Git prevents any sensitive fields from being saved in code.
E. When bugs are raised, email notifications are automatically sent by Git to repository users.

Answer: A,B

Explanation:
The correct answers are B: All the code changes along the lifecycle of a project are tracked, and E: Code can be reverted to a previous state.
Git is a distributed version control system designed to maintain a complete, chronological history of all code changes. Every commit records who made the change, when it occurred, and what the modification included.
This ensures transparency, reproducibility, and accountability across the development lifecycle, which makes B correct. Git also allows users to revert code to any previous commit, branch, or tag, making E correct as well. This capability is critical for recovering from mistakes, undoing faulty deployments, and ensuring stable releases.
Option A is incorrect because Git does not create file versions with suffixes; instead, it stores changes as snapshots within a repository. File suffixing is not part of Git's functionality.
Option C is incorrect because Git does not automatically send email notifications. Notification mechanisms come from hosting platforms like GitHub, GitLab, or Bitbucket-not from Git itself.
Option D is incorrect because Git does not prevent committing sensitive information. Developers must manually ensure secrets are excluded via .gitignore, secret managers, or pre-commit hooks. Git will store whatever is committed unless prevented through tooling.
Thus, only B and E correctly describe how Git supports version control in analytics engineering and dbt workflows.

NEW QUESTION # 30
You are working on a complex dbt model with many Common Table Expressions (CTEs) and decide to move some of those CTEs into their own model to make your code more modular.
Is this a benefit of this approach?
The new model can be documented to explain its purpose and the logic it contains.

A. Yes
B. No

Answer: A

Explanation:
Yes, this is a benefit of breaking large CTE-heavy SQL models into modular dbt models. According to dbt and Analytics Engineering best practices, modularity improves clarity, maintainability, and documentation quality. When CTEs remain embedded inside a single large SQL file, their purposes are often unclear, difficult to document, and hard for other developers to reuse. By extracting a logical CTE into its own model, dbt treats it as a first-class resource-meaning it can have its own description, tests, documentation, lineage, and metadata defined in YAML.
dbt's documentation system allows each model to include a description explaining what the transformation does, the assumptions being made, and the expected behavior of the data. This aligns with the Analytics Engineering principle of creating self-documenting pipelines, where transformations are transparent and easier for downstream users to understand.
Additionally, modular models improve lineage visualization in the DAG. Instead of a single model hiding multiple transformation layers, a modular structure reveals how data flows through each intermediate step, helping both debugging and governance. Modularization also enables reusability-other models can reference the intermediate model rather than rebuilding the same logic through duplicated CTEs, supporting DRY (Don't Repeat Yourself) principles.
Therefore, moving CTEs into separate dbt models absolutely provides a documentation benefit and improves the overall engineering quality of the project.

NEW QUESTION # 31
You run this command:
dbt build --select "source_status:fresher+" --state path/to/prod/artifacts Which two need to happen before it can be executed successfully?
Choose 2 options.

A. Test your sources with dbt test.
B. Invoke the command: dbt source freshness.
C. Define a freshness block on your source(s).
D. Add generic tests to your sources.
E. Invoke either dbt run or dbt build.

Answer: B,C

Explanation:
The selector source_status:fresher+ depends entirely on freshness metadata created by a previous dbt source freshness run. Source freshness results are stored in artifacts, and the --state flag points dbt to the location where these artifacts were previously generated. Without first running dbt source freshness, dbt has no stored freshness status and therefore cannot filter sources by "fresher" status. This makes option A required.
Additionally, source freshness checks only work when sources include a freshness block in their YAML configuration. This block specifies the loaded_at_field and the warn_after / error_after thresholds. If no freshness block exists, dbt does not compute freshness at all, meaning such sources cannot appear in the source_status:fresher+ selector. Therefore D is also required.
Options B and E are unrelated to source freshness artifacts and do not contribute to the preconditions for this selector. Option C does not impact freshness logic, as generic tests have no role in generating freshness metadata.
Thus, the two prerequisites are:
* Running dbt source freshness (to create freshness metadata).
* Defining a freshness configuration on the relevant sources.

NEW QUESTION # 32
......

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