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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Objectives
Topic 1: Testing and Data Quality	- Built-in and custom tests 1. Custom SQL tests 2. Generic tests (unique, not null, relationships) - Data validation strategies 1. CI-based validation workflows 2. Schema testing practices
Topic 2: 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
Topic 3: Deployment and Orchestration	- Environments and workflows 1. Development vs production environments 2. Version control with Git - Running dbt in production 1. dbt Cloud and job scheduling 2. CI/CD integration patterns
Topic 4: Documentation and Lineage	- Data lineage understanding 1. Dependency tracking with ref() 2. Directed acyclic graph (DAG) - dbt documentation system 1. Model descriptions and metadata 2. Auto-generated docs site
Topic 5: dbt Core Concepts	- Project structure and configuration 1. Packages and dependencies 2. dbt_project.yml configuration - 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 (Q140-Q145):

NEW QUESTION # 140
"We can just load everything into our warehouse and let dbt handle it." How would you respond to this approach suggested by a stakeholder?

A. dbt models might become unnecessarily complex due to unfiltered data.
B. All of the above.
C. It could become expensive due to storage and compute costs, especially with large, raw datasets.
D. Introducing sensitive data sources requires careful consideration and might be better handled outside the warehouse.

Answer: B

Explanation:
All of these points are valid reasons to be selective about which sources you bring into dbt. Emphasize the tradeoffs in cost, maintainability, and security.

NEW QUESTION # 141
(Multiple Select)

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

Answer: C,D

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 # 142
You're working in a large, enterprise-level dbt deployment. Which aspect of using dbt documentation commands could become especially important in this context?

A. All of the above.
B. Version-controlling your documentation changes alongside your project code.
C. Optimizing the dbt docs generate process for very large projects to minimize execution time.
D. Developing strict documentation contribution guidelines for multiple teams and collaborators.

Answer: A

Explanation:
All aspects become increasingly important in larger projects:A: Optimization matters when many models create more complex documentation. B: Tracking documentation history alongside code is crucial for governance. C: Clear guidelines are essential with many contributors.

NEW QUESTION # 143
You need a model to track weekly cohort retention, with the ability to slice data across multiple dimensions. The underlying data is voluminous. Which approach lays a good foundation for scalability?

A. Introduce a JSON column to store dimensions dynamically, reducing the number of columns.
B. Normalize the cohort data and dimensions, using joins for analysis.
C. A wide table with columns for each potential filtering combination (country, product category, acquisition channel, etc.).
D. Pre-calculate all possible cohort and dimension combinations as an incremental model.

Answer: B

Explanation:
Normalization aids scalability. Options A and C suffer from combinatorial explosion. While JSON can offer flexibility, query performance can be unpredictable.

NEW QUESTION # 144
(Multiple Select)

A. Downstream models relying on the staging model will continue to function.
B. The staging model run will fail.
C. dbt will issue a warning but continue processing.
D. The dbt run will succeed, but the staging model will have null values due to the missing data.

Answer: B,D

Explanation:
A and C are correct. A missing dependency will cause SQL errors, and nulls would likely occur where the dropped table's data was used. B is incorrect. Downstream models will often fail if an upstream model fails. D is incorrect. dbt's strict dependency handling is likely to stop the process and throw errors.

NEW QUESTION # 145
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