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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Objectives
Testing and Data Quality	- Data validation strategies 1. Schema testing practices 2. CI-based validation workflows - Built-in and custom tests 1. Generic tests (unique, not null, relationships) 2. Custom SQL tests
Analytics Engineering Foundations	- Modern data stack concepts (ELT vs ETL) 1. Role of dbt in analytics engineering 2. Warehouse-centric transformation workflows - SQL proficiency for analytics 1. Data modeling in SQL 2. Joins, aggregations, and window functions
Documentation and Lineage	- dbt documentation system 1. Model descriptions and metadata 2. Auto-generated docs site - Data lineage understanding 1. Directed acyclic graph (DAG) 2. Dependency tracking with ref()
Deployment and Orchestration	- Running dbt in production 1. CI/CD integration patterns 2. dbt Cloud and job scheduling - Environments and workflows 1. Development vs production environments 2. Version control with Git
dbt Core Concepts	- Models and materializations 1. Ref and source functions 2. Views, tables, incremental models - Project structure and configuration 1. Packages and dependencies 2. dbt_project.yml configuration

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q355-Q360):

NEW QUESTION # 355
In what situations might it be acceptable to use raw data directly for reporting or analytics purposes?

A. Generally, using raw data directly for reporting should be avoided due to potential quality and consistency issues.
B. When performance is a top priority, and the overhead of additional data transformations is undesirable.
C. In environments where strict data governance and transformation processes are in place.
D. When dealing with simple analyses, and there's high confidence in the quality of the raw data source.

Answer: A

Explanation:
Raw data is often unpredictable- However, under specific conditions like those listed in A, B, or C, it could be considered if the risks and trade-offs are well understood.

NEW QUESTION # 356
You want to host your dbt project documentation on an internal company server that's behind a firewall. Which approach would be best suited for this scenario?

A. Upload the static files generated by dbt docs generate to your internal web server.
B. Utilize the dbt docs serve command, making the local web server accessible within your private network.
C. Directly modify the HTML and CSS of the generated documentation site to add firewall-specific rules.
D. Deploy the docs using dbt Cloud's built-in hosting features.

Answer: A

Explanation:
Uploading the static files (HTML, CSS, JavaScript) to your internal server provides a straightforward way to host the documentation behind a firewall.

NEW QUESTION # 357
You need to parameterize a dbt model that filters data based on a country code. Which approach offers the BEST combination of flexibility and security?

A. Build a dynamic query string entirely within a Jinja macro.
B. Create a seed file mapping country codes to filter values and reference it in the model.
C. Pass the country code as a CLI argument when executing dbt run.
D. Use environment variables in conjunction with Jinja to substitute the country code.

Answer: D

Explanation:
Environment variables provide control and help prevent SQL injection risks. While the other options might work, they are less secure or less flexible.

NEW QUESTION # 358

Answer:

Explanation:

Explanation:
(new_project)
(models:)
(grants)
(require-dbt-version: '>= 1.0.0' )
The dbt_project.yml file acts as the configuration backbone for every dbt project. dbt requires that project names follow a strict naming convention, allowing only lowercase letters, numbers, and underscores.
Therefore, new_project is the only valid name among the choices. Names with spaces or hyphens, such as new project or new-project, violate dbt's project naming rules.
To configure how models behave-materializations, schemas, tags, or per-directory overrides-dbt uses the models: block inside dbt_project.yml. This block defines configuration inheritance rules and is different from the config() function, which is used inside SQL models, not in YAML files.
When granting permissions on database objects created by dbt, you use the grants configuration. This allows you to define access roles at the model level or project level for downstream BI or analytics tools, ensuring proper data governance and controlled data exposure.
Finally, compatibility with specific dbt versions is set using require-dbt-version. The correct YAML formatting uses quotes, such as '>= 1.0.0', ensuring dbt interprets the value as a string. This prevents accidental mis-parsing and enforces version requirements during dbt run or dbt build.

NEW QUESTION # 359
You're building tests for a dbt model that performs a join across multiple large datasets. You notice performance issues - tests are taking a very long time to run. What might be optimization strategies?

A. Focus on tests that can validate a subset of the data rather than the entire dataset.
B. Accept that tests on complex models will always be slow.
C. Refactor model SQL to use incremental logic to avoid full table scans.
D. Create snapshots of the source tables and test on those snapshots instead.

Answer: A,C

Explanation:
B and C target reducing the test's workload with sampling or optimizing the model itself. A might help under specific circumstances, but not always. D is a defeatist approach!

NEW QUESTION # 360
......

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