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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Objectives
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
dbt Core Concepts	- Project structure and configuration 1. Packages and dependencies 2. dbt_project.yml configuration - Models and materializations 1. Views, tables, incremental models 2. Ref and source 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()
Testing and Data Quality	- Data validation strategies 1. CI-based validation workflows 2. Schema testing practices - 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. Warehouse-centric transformation workflows 2. Role of dbt in analytics engineering - SQL proficiency for analytics 1. Data modeling in SQL 2. Joins, aggregations, and window functions

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q321-Q326):

NEW QUESTION # 321
You frequently need to implement custom SQL tests that are very similar but with slight variations in parameters. Which dbt feature could streamline this process?

A. dbt macros
B. dbt snapshots
C. The relationships test
D. dbt seeds

Answer: A

Explanation:
Macros are ideal for parameterizing reusable test logic, making it easy to adapt the tests without excessive copy-pasting.

NEW QUESTION # 322
You're working in a highly regulated environment. Merges into the main branch require multiple approvals. Which additional steps might need to be incorporated into your merge process?

A. Use a protected branch workflow on the remote repository with required reviews.
B. Utilize signed commits to add a layer of traceability and verification-
C. Link pull requests to detailed change documentation and impact analysis.
D. All of the above

Answer: D

Explanation:
A: Clear documentation is essential for compliance and audit trails. B: Protected branches enforce the approval process at the repository level. C: Signed commits offer proof of authorship and agreement on the changes-

NEW QUESTION # 323
dbt Model Snippet:SQL

A. Your dbt_projectyml file has a model-level schema override.
B. There's a custom macro in use that dynamically changes schemas.
C. The stg_orders model uses a schema config in its own model SQL
D. Your sources.yml file is not configured correctly-

Answer: A

Explanation:
Model-level schema configurations override other settings. Check your project file for this model. The others are possible but less common patterns

NEW QUESTION # 324
You've included the popular 'dbt-utils' package in your project. Where should you primarily expect to find the models and macros it offers?

A. Within the 'seeds' directory of your dbt project.
B. Automatically materialized as new tables in your data warehouse.
C. Within your 'models' directory namespaced to distinguish them from your own models.
D. In the 'dbt_project.yml' file under a 'packages' section.

Answer: C

Explanation:
Packages extend your project's functionality by placing items within your model's directory offering seamless integration.

NEW QUESTION # 325
Examine the code:
select
left(customers.id, 12) as customer_id,
customers.name as customer_name,
case when employees.employee_id is not null then true else false end as is_employee, event_signups.event_name, event_signups.event_date, sum(case when visit_accomodations.type = 'hotel' then 1 end)::boolean as booked_hotel, sum(case when visit_accomodations.type = 'car' then 1 end)::boolean as booked_ride from customers
-- one customer can sign up for many events
left join event_signups
on left(customers.id, 12) = event_signups.customer_id
-- an event signup for a single customer can have many types of accommodations booked left join visit_accomodations on event_signups.signup_id = visit_accomodations.signup_id and left(customers.id, 12) = visit_accomodations.customer_id
-- an employee can be a customer
left join employees
on left(customers.id, 12) = employees.customer_id
group by 1, 2

Answer:

Explanation:

Explanation:
Operation
Correct Location
Aggregations (booked_hotel, booked_ride)
In a model dedicated to pre-aggregate data for reporting
Standardizing customer_id
In the first layer of models
Selecting customer_name
In the first layer of models
Joining employees
In a model between the first layer and final layer of models
In dbt's recommended modeling framework, transformations should be centralized according to their purpose and level of abstraction. Basic cleaning and column standardization-such as shortening customers.id using left(customers.id, 12)-belongs in the first layer of models, commonly known as staging models. This layer is responsible for producing clean, consistent, analytics-ready fields. Selecting raw descriptive fields like customers.name also belongs in this layer.
Joins that enrich a dataset by combining cleaned staging outputs across domains-such as joining customers with employees-belong in intermediate models. These models unify business logic that sits between the raw staging layer and the final aggregations used for reporting.
Finally, aggregations like counting types of accommodations (booked_hotel and booked_ride) belong in marts or reporting models, especially when they involve summarization that downstream tools (dashboards, BI reports) will consume. This keeps heavy business logic centralized and reusable.

NEW QUESTION # 326
......

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