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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Objectives
Topic 1: 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. Joins, aggregations, and window functions 2. Data modeling in SQL
Topic 2: Documentation and Lineage	- Data lineage understanding 1. Directed acyclic graph (DAG) 2. Dependency tracking with ref() - dbt documentation system 1. Model descriptions and metadata 2. Auto-generated docs site
Topic 3: Deployment and Orchestration	- Running dbt in production 1. CI/CD integration patterns 2. dbt Cloud and job scheduling - Environments and workflows 1. Version control with Git 2. Development vs production environments
Topic 4: Testing and Data Quality	- Built-in and custom tests 1. Generic tests (unique, not null, relationships) 2. Custom SQL tests - Data validation strategies 1. Schema testing practices 2. CI-based validation workflows
Topic 5: dbt Core Concepts	- Project structure and configuration 1. dbt_project.yml configuration 2. Packages and dependencies - Models and materializations 1. Ref and source functions 2. Views, tables, incremental models

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q356-Q361):

NEW QUESTION # 356
You discover that a heavily used model is taking significantly longer to materialize. After investigation, you determine that the complexity of the model's logic is a contributing factor. Which of the following actions is LEAST likely to help improve performance?

A. Break down the model into smaller, intermediate models using CTEs.
B. Change the materialization from a table to a view.
C. Consider applying an incremental materialization strategy if suitable.
D. Investigate potential optimizations in the underlying SQL code.

Answer: B

Explanation:
Switching from a table to a view alone doesn't address the core performance issue of complex logic. Other options prioritize breaking down the transformations or directly optimizing the SQL itself.

NEW QUESTION # 357
When integrating a new data source, you notice many existing models could benefit from it. What's the most maintainable way to incorporate the new data within a DRY approach?

A. Create a base model that incorporates the new data source and use the ref() function in downstream models.
B. Add the new data source directly to each relevant model's SQL query.
C. Establish a naming convention for the new data elements to avoid hardcoding them within model SQL.
D. Build a snapshot of the new data combined with relevant data from existing models.

Answer: A

Explanation:
A base model serves as a modular point of integration for the new data. The ref() function promotes DRY principles and eases maintenance if the new source structure changes.

NEW QUESTION # 358
You want to restrict which models can refer to a specific model.
How can this be done?
Choose 1 option.

A. Set access to protected for that specific model.
B. Modify your data warehouse's permissions to restrict usage of the object.
C. Add model groups and set access to private.
D. Create another version with a _v2 suffix and set the latest version to 2 in the model's configuration.

Answer: C

Explanation:
The correct answer is A: Add model groups and set access to private.
dbt's access control system is designed to restrict which models may reference others purely within the dbt project itself, independent of data warehouse permissions. This system uses model groups and an access property, which can be set to public, protected, or private. Setting a model to private means that only models within the same group are allowed to reference it. Any attempts to reference the model from outside the group will cause a compilation error, enforcing strong boundaries between domains or data products within a dbt project.
Option C (protected) is incorrect because protected allows references from other groups but requires explicit configuration-the intention is stewardship, not restriction. It does not fully restrict referencing.
Option B is incorrect because dbt model access control is not tied to warehouse permissions. Warehouse- level grants cannot control dependency references inside the dbt DAG.
Option D is irrelevant to access control. Versioning does not restrict references; it only manages updates to published models.
Therefore, the only correct way to restrict which models can reference another model is by placing it into a model group and marking it as private, which enforces strict internal usage boundaries.

NEW QUESTION # 359
You need to run a custom Python script after your dbt models execute. Where and how can you configure this in your dbt_project.yml file?

A. Utilize the on-run-end hook configuration.
B. Create an 'integration test' and use the PythonFunction class-
C. Include the script as a 'seed' file with a specific execution order
D. Define it as a 'snapshot' to ensure it runs after all models.

Answer: A

Explanation:
on-run-end hooks allow you to execute arbitrary code at the end of a dbt run.

NEW QUESTION # 360
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. Testing would become impractical due to increased runtimes.
B. Conflicts might arise due to models overwriting each other's data.
C. Unnecessary computations, as monthly models would rebuild daily data redundantly.
D. The DAG visualization would be too complex to understand.

Answer: C

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

NEW QUESTION # 361
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