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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Implementing dbt Tests	10%	- Test configuration and execution - Built-in tests - Custom tests
Topic 2: Creating and Maintaining Documentation	10%	- Generating documentation - Descriptions and metadata - Documentation standards
Topic 3: Debugging and Error Resolution	15%	- Identifying modeling errors - Debugging techniques - Resolving data quality issues
Topic 4: Leveraging dbt State	5%	- State-aware operations - State management
Topic 5: dbt Models Governance	15%	- Project organization and structure - Naming conventions and standards - Version control integration
Topic 6: Managing Data Pipelines	15%	- Deployment strategies - Pipeline orchestration - CI/CD integration
Topic 7: 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
Topic 8: External Dependencies	10%	- Using packages - Managing snapshots - External sources integration

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q124-Q129):

NEW QUESTION # 124
You want to automatically deploy updates to your documentation site hosted on a cloud service whenever changes to your dbt project are committed to your main branch. Which of the following is essential for this setup?

A. Configuring dbt Cloud to trigger documentation deploys.
B. Implementing a CI/CD pipeline that includes dbt build steps.
C. Enabling webhooks in your Git repository provider.
D. Write a custom script to check for project changes and handle deployment.

Answer: B,C,D

Explanation:
A). A CI/CD pipeline is needed to automate running dbt commands and the deployment process. B: Webhooks allow your CIICD system to get notified when changes are pushed to your repository, triggering the pipeline. D: A custom script could provide flexibility for check conditions and deployment logic-

NEW QUESTION # 125
You implement dbt tests to monitor data quality over time. Which of the following scenarios could these tests potentially help you detect?

A. Gradual degradation in source data completeness due to an API rate limit being reached
B. An accidental schema change by someone working outside of your dbt project-
C. Performance issues in your data warehouse impacting the speed of dbt model updates.
D. An unexpected spike in web traffic leading to invalid data entries in your source system-

Answer: A,B,D

Explanation:
A, C, and D relate to the changing quality of the data itself, which tests can surface. B (warehouse performance) is a separate issue.

NEW QUESTION # 126
You suspect discrepancies in a critical model. To troubleshoot, you need to understand the order of model execution, but the DAG is complex. What would be the BEST strategy to get this information?

A. Use dbt Is -select
B. Manually trace the dependencies in your model files.
C. Visually inspect the manifest.json file.
D. Run dbt docs generate and inspect the generated documentation.

Answer: D

Explanation:
While other options provide some information, dbt docs generate creates comprehensive documentation including a visual DAG to show execution flow clearly.

NEW QUESTION # 127
Which two are true for a dbt clone command?
Choose 2 options.

A. It allows comparison between manifests of source and target dbt runs, but does not create any objects itself.
B. It requires the reference to a manifest from a previous dbt invocation.
C. It can be used to replicate data across different data warehouses.
D. It allows testing your code changes on downstream dependencies outside of dbt (such as a BI tool).
E. It creates new versions of your dbt models suffixed with _v#.

Answer: B,D

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 # 128
After adding several new models to your project, you want to execute only those models and their direct dependencies. Which command combination best achieves this?

A. dbt run -models +model namel +model name2 ...
B. dbt run -exclude model namel model name2
C. dbt run followed by dbt test
D. dbt build

Answer: A

Explanation:
The + syntax with dbt run allows you to specify models and their upstream dependencies. Dbt build encompasses run and test along with snapshot execution.

NEW QUESTION # 129
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