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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Weight	Objectives
External Dependencies	10%	- Using packages - Managing snapshots - External sources integration
Developing dbt Models	20%	- Model design and structure 1. Incremental models 2. Ref and source functions 3. Materialization types - Model optimization 1. Model contracts 2. Performance tuning
Debugging and Error Resolution	15%	- Identifying modeling errors - Resolving data quality issues - Debugging techniques
dbt Models Governance	15%	- Project organization and structure - Version control integration - Naming conventions and standards
Managing Data Pipelines	15%	- CI/CD integration - Deployment strategies - Pipeline orchestration
Creating and Maintaining Documentation	10%	- Documentation standards - Descriptions and metadata - Generating documentation
Implementing dbt Tests	10%	- Built-in tests - Test configuration and execution - Custom tests
Leveraging dbt State	5%	- State management - State-aware operations

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q26-Q31):

NEW QUESTION # 26
Which is true about writing generic tests?
Choose 1 option.

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

Answer: B

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 # 27
You get a warning during dbt run:WARNlNG: Found 100 rows in 'stg_customers' that will become duplicates in the 'dim_customers' target.

A. It's safe to ignore this warning, as de-duplication is handled automatically by dbt.
B. Update the stg_customers source definition to include filtering logic to remove duplicate rows.
C. Increase the memory allocated for the database to handle the large number of duplicates.
D. Investigate your dim_customers modeling logic to ensure a unique key is correctly used to prevent duplicates.

Answer: D

Explanation:
This typically indicates a logic flaw in your model creating the target table. Modifying the source or increasing memory won't resolve the core issue.

NEW QUESTION # 28
Data Quality Issue

A. Inspect any filters or transformations within the model's SQL that might be unintentionally excluding data.
B. Run dbt compile followed by dbt run -select model_name to isolate the model and observe its output closely.
C. Run dbt test on the model and see if existing tests fail, indicating a possible regression.
D. Add specific schema tests to the model, asserting that the column in question should not contain nulls.

Answer: A,B,C,D

Explanation:
This requires a comprehensive approach covering logic errors, regression tests, proactive quality checks, and isolated execution.

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

Answer: A,E

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've implemented the built-in dbt.get_column_values macro to display sample data values from a source column directly on the DAG. However, you notice performance issues when loading the docs. Which optimization strategy could you consider?

A. Modify the macro to only retrieve sample values during development and not for production documentation.
B. Increase the compute resources allocated to your dbt documentation generation process.
C. Utilize a dbt snapshot to materialize the macro results instead of computing them on the fly.
D. Replace dbt_get_column_values with a custom macro that only fetches the first few sample values.

Answer: A,C,D

Explanation:
B: Disabling expensive computations conditionally can boost performance for production environments. C: Snapshots pre-compute values, potentially improving DAG loading times. D: Reducing the amount of data retrieved by the macro could help with performance.

NEW QUESTION # 31
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