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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q112-Q117):

NEW QUESTION # 112
You've defined development, staging, and production targets in your dbt project. You want to prevent accidental execution of destructive model materialization strategies (like drop or truncate) on the production target. How could you achieve this?

A. Implement a CI/CD process that includes checks to prevent specific dbt commands from running in the production environment
B. Create a separate profiles.yml file for production that disables dangerous materialization strategies.
C. Write a custom macro that only executes such operations on non-production targets.
D. Rely purely on thorough code reviews to prevent execution of these commands in production.

Answer: A,C

Explanation:
A Macros can provide conditional logic for model execution based on environments- C: Automated checks in CI/CD pipelines add a layer of protection. D: Relying solely on code reviews is prone to human error.

NEW QUESTION # 113
Your project has models calculating order metrics. Some models aggregate data daily; others do monthly roll-ups. What's a likely performance issue if all models are materialized as tables?

A. Unnecessary computations, as monthly models would rebuild daily data redundantly.
B. Testing would become impractical due to increased runtimes.
C. The DAG visualization would be too complex to understand.
D. Conflicts might arise due to models overwriting each other's data.

Answer: A

Explanation:
Rebuilding pre-aggregated data is inefficient. While testing becomes slower (D), the primary concern is computational waste.

NEW QUESTION # 114
Examine this query:
select *
from {{ ref('stg_orders') }}
where amount_usd < 0
You want to make this a generic test across multiple models.
Which set of two standard arguments should be used to replace {{ ref('stg_orders') }} and amount_usd?
Choose 1 option.

A. source and column
B. model and column_name
C. model and field
D. model_name and column_name

Answer: B

Explanation:
When converting a model-specific SQL query into a generic test, dbt expects the test macro to accept the two standard arguments used across all generic tests: model and column_name. These arguments allow dbt to automatically pass the correct table and the correct column when the test is applied in YAML.
The argument model represents the actual relation (table/view) being tested. dbt compiles the reference itself-so instead of writing ref('stg_orders'), generic tests always use {{ model }} to represent the referenced relation.
The argument column_name represents the actual column being tested. In this case, the hard-coded column amount_usd becomes the dynamic argument {{ column_name }}, allowing you to apply the logic to any numeric or validated column across multiple models.
So the generic version of your SQL becomes:
select *
from {{ model }}
where {{ column_name }} < 0
The other options are incorrect:
* source is not the standard argument for generic tests.
* model_name is not automatically passed by dbt.
* field is not a recognized standard test argument.
Thus, the two correct standard arguments are model and column_name.

NEW QUESTION # 115
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. Use git add -p to stage only the feature-related changes, and leave formatting for a future task.
B. Address the formatting issues in the same commits as the feature to avoid extra work.
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 # 116
you're working with a dbt project where a central model fct_orders is refactored. Several downstream models rely on this fact table. Which of the following is the MOST efficient way to determine which downstream models might be affected by this change?

A. Use the dbt Is command to generate a lineage graph.
B. Run dbt test to see which tests might fail.
C. Execute dbt run and observe for errors.
D. Manually review the code of all downstream models.
E. Utilize the dbt docs generate and dbt docs serve commands.

Answer: E

Explanation:
While options B, D, and E can indirectly hint at broken models, they're not the most efficient methods. The dbt docs commands allow you to directly visualize and explore dependencies within your dbt project, making it the ideal choice for this scenario.

NEW QUESTION # 117
......

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