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[General] Useful dbt-Analytics-Engineering Dumps, Latest dbt-Analytics-Engineering Exam Di

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【General】 Useful dbt-Analytics-Engineering Dumps, Latest dbt-Analytics-Engineering Exam Di

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q13-Q18):NEW QUESTION # 13
Which two are true for a dbt clone command?
Choose 2 options.
  • A. It requires the reference to a manifest from a previous dbt invocation.
  • B. It allows testing your code changes on downstream dependencies outside of dbt (such as a BI tool).
  • C. It allows comparison between manifests of source and target dbt runs, but does not create any objects itself.
  • D. It creates new versions of your dbt models suffixed with _v#.
  • E. It can be used to replicate data across different data warehouses.
Answer: A,B
Explanation:
The correct answers are A and C.
dbt clone is a command introduced to efficiently replicate existing relations (tables/views) from one schema
/environment to another within the same warehouse without fully rebuilding them. It typically leverages warehouse-level "clone" functionality (e.g., Snowflake zero-copy clone). To know what to clone, dbt needs information about previously built models; this is stored in artifacts like manifest.json. Therefore, A is correct:
it requires a reference to a previous manifest/state to know which models and locations to operate on.
C is also correct because cloning a production schema into a development schema enables you to test code changes while pointing downstream tools (like BI dashboards) at the cloned data. That way, BI or ML systems can exercise new dbt logic without impacting real production objects.
Option B describes state:modified / state comparison behavior, but dbt clone does create (or clone) objects, so B is false.
Option D is incorrect because clone works within a warehouse, not across different warehouses.
Option E is wrong: _v# suffixing is for model versioning, not cloning.
Thus, A and C correctly describe the behavior and purpose of dbt clone.

NEW QUESTION # 14

Answer:
Explanation:

Explanation:
(branch)
(commit)
(pull request)
In dbt development workflows, version control using Git is essential for ensuring collaborative, safe, and trackable changes to analytics code. The correct first step when making updates-such as adding new models-is to create a new Git branch. This isolates development work from the production (main) branch, preventing incomplete or experimental logic from affecting deployed transformations. Branching supports dbt' s modular development approach and aligns with best practices for analytics engineering.
Once the branch is created, the developer modifies SQL models, tests, macros, or documentation as required.
To permanently record these modifications in Git, the developer must commit the changes. A commit serves as a snapshot of progress and creates an auditable history of transformations made to the project, enabling rollbacks, diffs, and peer review.
After development is complete, the developer submits a pull request (PR). The pull request triggers CI checks-often including dbt build, schema tests, and contract validations-to ensure code quality and identify impacts on downstream models. PRs allow team members to review and comment before changes merge into the main branch, enforcing governance, consistency, and reliability. This workflow embodies the engineering rigor dbt encourages: modular development, testing, versioning, and peer review.

NEW QUESTION # 15
Which explanation describes how dbt infers dependencies between models?
Choose 1 option.
  • A. .yml configurations for sources and refs are parsed for dependency information.
  • B. The underlying SQL code is parsed and relationships are created from explicit table references.
  • C. Information is gathered from the use of source and ref macros.
  • D. All source and ref macros are resolved to database objects and dbt queries them for dependencies.
Answer: C
Explanation:
The correct answer is A: Information is gathered from the use of source and ref macros.
dbt determines the dependency graph - the DAG - by analyzing calls to ref() and source() inside model SQL files. These macros explicitly declare relationships between models. When a developer writes ref ('orders'), dbt interprets this as: "the current model depends on the orders model." Similarly, source() indicates dependencies on upstream raw data sources. This declarative approach allows dbt to build a structured and deterministic DAG without scanning SQL for implicit table references.
Option B is incorrect because dbt does not query database objects to infer dependencies; it resolves dependencies at compile time through metadata generated from model files. Option C is incorrect because dbt intentionally does not parse SQL to detect table names-this would be brittle and error-prone across warehouses. Instead, dbt requires explicit references to maintain reliability. Option D is incorrect because YAML files define metadata about models and sources but do not create dependency relationships between them.
Thus, the dependency graph is built exclusively by reading ref() and source() macro calls, which ensures clarity, correctness, and maintainability within the analytics engineering workflow.

NEW QUESTION # 16
Which is true about writing generic tests?
Choose 1 option.
  • A. They do not need to be specified under a model's YAML configurations.
  • B. They should always accept a column_name.
  • C. They are written using the {% macro %} wrapper.
  • D. They must contain a {{ ref() }} snippet to a model.
  • E. They should accept one or both of these arguments: model and column_name.
Answer: C
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 # 17
How can you overwrite configurations for models within a package?
Choose 1 option.
  • A. After installing the package, you can apply configurations on the models in the ./target folder.
  • B. By adding the configurations in dbt_project.yml within the package's namespace.
  • C. By specifying the configurations within the packages.yml file under the relevant package entry.
  • D. By navigating into the /dbt_packages folder and adding the configurations directly to the models.
Answer: B
Explanation:
The correct answer is B: By adding the configurations in dbt_project.yml within the package's namespace.
dbt packages are installed as read-only code, meaning you should never modify files inside /dbt_packages, nor should you attempt to overwrite generated artifacts inside /target. Instead, dbt provides a clean and officially supported method for overriding or extending configurations on models from packages: using the dbt_project.yml file.
Each package comes with its own namespace, and users can override model-level configurations by referencing that namespace inside the models: section of dbt_project.yml. dbt merges project-level configurations with package-level defaults, and configurations defined in dbt_project.yml take precedence.
This is the only correct and recommended way to override settings such as materializations, tags, schema, or custom config blocks on models imported through a package.
Option A is incorrect because packages.yml is used only to declare package dependencies-not to configure package models. Option C is incorrect because the ./target folder contains compiled and ephemeral artifacts that should never be edited. Option D is incorrect because modifying code in /dbt_packages violates dbt best practices and will be overwritten each time packages are reinstalled.
Therefore, overriding configurations must be done exclusively through dbt_project.yml.

NEW QUESTION # 18
......
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