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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Leveraging dbt State	5%	- State management - State-aware operations
Topic 2: Creating and Maintaining Documentation	10%	- Documentation standards - Generating documentation - Descriptions and metadata
Topic 3: Managing Data Pipelines	15%	- Deployment strategies - CI/CD integration - Pipeline orchestration
Topic 4: dbt Models Governance	15%	- Version control integration - Project organization and structure - Naming conventions and standards
Topic 5: Implementing dbt Tests	10%	- Custom tests - Built-in tests - Test configuration and execution
Topic 6: Developing dbt Models	20%	- Model design and structure 1. Ref and source functions 2. Materialization types 3. Incremental models - Model optimization 1. Model contracts 2. Performance tuning
Topic 7: External Dependencies	10%	- Using packages - Managing snapshots - External sources integration
Topic 8: Debugging and Error Resolution	15%	- Resolving data quality issues - Debugging techniques - Identifying modeling errors

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q81-Q86):

NEW QUESTION # 81
(Multiple Select)

A. Introduce a global persist_docs setting within your dbt_project.yml file to resolve schema inconsistencies.
B. Utilize a YAML validator tool independently of dbt to check for general formatting errors.
C. Refer to the dbt documentation for a comprehensive list of valid properties within schema files.
D. If available, compare your schema.yml to a previous working version to spot problematic changes.

Answer: B,C

Explanation:
Understanding valid schema properties and general YAML linting are key. Version comparison can be useful, while persist_docs is less relevant to initial compilation errors.

NEW QUESTION # 82
You're working with a dbt project in both development and production environments.
Which configuration technique is best suited to handle differences in database connection details between these environments?

A. Create separate branches for each environment and maintain different dbt_project.yml files in each branch.
B. Utilize environment variables in conjunction with the profiles.yml file.
C. Use the target flag when running dbt commands.
D. Define multiple profile sections within the dbt_project.yml

Answer: B

Explanation:
Environment variables and a corresponding profiles.yml provide the most flexible and secure way to manage environment-specific settings.

NEW QUESTION # 83
Several models perform similar filtering and aggregation on a frequently used source table. What's the most effective way to streamline this process and improve maintainability?

A. Create a snapshot to capture the pre-processed state of the source table.
B. Create an ephemeral model to prepare the data in a common format, referenced by downstream models.
C. Replicate the filtering and aggregation logic directly within each model.
D. Create a table materialization to hold the pre-processed data.

Answer: B

Explanation:
Ephemeral models within CTEs allow for reusable intermediate steps in data transformation and are perfect for avoiding logic duplication across multiple models.

NEW QUESTION # 84
You've added a new exposure to your project, but it doesn't appear in dbt docs. Here's part of your dbt_project.yml:YAML

A. You haven't run dbt docs serve after making changes to your project file.
B. The maturity property is missing within your exposure's configuration.
C. The owner property is not correctly formatted as an email address.
D. Exposures of type 'notebook' are not automatically detected by dbt docs generate.

Answer: D

Explanation:
dbt docs primarily works with model-based exposures. The others might be issues in specific cases, but check the exposure type first.

NEW QUESTION # 85
You've built a macro to implement a common custom test pattern. When using this macro in multiple models, you realize the tests are running much slower than expected. What might be the cause?

A. The macro's SQL logic is inefficiently querying the model or other data.
B. The macro is accidentally triggering downstream model rebuilds.
C. Too many tests are being defined on individual models.
D. This is expected behavior, and custom tests always incur a performance penalty.

Answer: A,C

Explanation:
Inefficient macro logic would be amplified with wide reuse. Having numerous tests, even simple ones, can add up. C is unlikely unless the macro directly references downstream models.

NEW QUESTION # 86
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