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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Weight	Objectives
Models	25%	- Materializations (table, view, ephemeral, incremental) - Snapshots - Seeds - Writing and managing SQL models - Sources and references
dbt Fundamentals	15%	- dbt workflow and best practices - dbt Core vs dbt Cloud - dbt project structure
Deployment and Orchestration	15%	- CI/CD with dbt Cloud - Environments (dev, staging, prod) - Jobs and scheduling in dbt Cloud - Git version control integration
Data Transformation Techniques	25%	- Common table expressions and subqueries - Jinja templating - Macros and packages - Refactoring and incremental models
Testing and Documentation	20%	- dbt docs and DAG visualization - Schema tests (unique, not_null, accepted_values, relationships) - Custom data tests - Documentation generation

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dbt Labs dbt Analytics Engineering Certification Exam dbt-Analytics-Engineering Prüfungsfragen mit Lösungen (Q352-Q357):

352. Frage
You want to use a YAML anchor to reuse a standard block of descriptive text within multiple models. How would you implement this?

A. Insert a YAML anchor (&) in your dbt_projectyml file and reference it in your model files.
B. Create a YAML anchor within the relevant model file where the text should be reused.
C. YAML anchors are not directly supported in the context of dbt model and source descriptions-
D. Define a Jinja macro for the text block, and then call the macro within your descriptions.

Antwort: B

Begründung:
YAML anchors (&) and aliases (*) allow you to define reusable blocks of text within the same YAML file, such as model files or sources.yml.

353. Frage
(Multiple Select)

A. Preferring long, monolithic SQL files with minimal CTEs.
B. Using clear and consistent naming conventions across models and macros.
C. Encapsulating complex logic in well-named macros with descriptive comments.
D. Minimizing explicit model dependencies to create loosely coupled components.

Antwort: B,C

Begründung:
Well-documented macros and consistent naming boost readability. Large monolithic SQL files hinder maintainability.

354. Frage
You're integrating a new API-based data source. Select the most suitable way to incorporate this into your dbt project.

A. Create a source with a loaded_at_field and write custom logic to snapshot API data.
B. Write a dbt macro to encapsulate the API interaction logic.
C. Utilize a dbt package tailored for the specific API, if available.
D. Create a seed file to load the data directly into a table.

Antwort: C

Begründung:
Explanation: Pre-built packages streamline the integration of common APIs. Options A or B are workarounds, and D might be part of a custom solution but not the primary approach.

355. Frage
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

Antwort:

Begründung:

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.

356. Frage
You need to grant readonly access to the results of your dbt project to stakeholders outside your team. Considering security, which approach would be the MOST suitable?

A. Embed dbt model results within a Bl dashboard and manage access through the Bl tool.
B. Have stakeholders directly query the dbt models, providing access to the production data warehouse.
C. Regularly run dbt seed to export CSV files, which are then shared with stakeholders.
D. Create a designated reporting schema and set up views that map to the dbt model outputs

Antwort: A,D

Begründung:
A provides fine-grained control within the database. C uses the Bl tool for the access management layer. B is too broad in terms of access. D is manual and less secure.

357. Frage
......

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