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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Models	25%	- Writing and managing SQL models - Snapshots - Sources and references - Seeds - Materializations (table, view, ephemeral, incremental)
Topic 2: dbt Fundamentals	15%	- dbt project structure - dbt workflow and best practices - dbt Core vs dbt Cloud
Topic 3: Deployment and Orchestration	15%	- Git version control integration - CI/CD with dbt Cloud - Environments (dev, staging, prod) - Jobs and scheduling in dbt Cloud
Topic 4: Data Transformation Techniques	25%	- Macros and packages - Jinja templating - Common table expressions and subqueries - Refactoring and incremental models
Topic 5: Testing and Documentation	20%	- Schema tests (unique, not_null, accepted_values, relationships) - dbt docs and DAG visualization - Custom data tests - Documentation generation

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q84-Q89):

NEW QUESTION # 84
(Multiple Select)

A. Inserting rows for a generic date dimension table.
B. Adding test records to validate a new custom data test.
C. Populating a staging environment with representative data for testing.
D. Loading data to fact tables as part of the regular ETL process.

Answer: A,C

Explanation:
dbt seed is ideal for static data, often used in development and testing. Fact tables usually rely on upstream data sources.

NEW QUESTION # 85
You want to enhance the DAG visualization and highlight models that process sensitive dat a. How might you achieve this using macros?

A. Develop a custom dbt package that extends the standard DAG rendering with sensitivity indicators-
B. Embed additional icons or visual indicators directly within model descriptions using specialized macro logic-
C. Write a macro that dynamically applies custom CSS classes to nodes in the DAG based on model tags or descriptions.
D. Use the meta configuration in your models to control specific visualization properties in the DAG-

Answer: A,B,C

Explanation:
A: Custom CSS allows you to style the DAG flexibly based on model properties or tags. B: Macros can potentially be used to add small visual elements to descriptions. D: Packages can provide a more structured and potentially reusable way to achieve advanced DAG customization.

NEW QUESTION # 86
You frequently need to spin up short-lived development environments on demand for testing and experimentation. Which dbt feature would streamline this process, especially when it involves custom configuration?

A. Using dbt tags to selectively execute parts of your project.
B. Creating multiple deployment environments in dbt Cloud.
C. dbt profiles defined in your dbt_project.yml file.
D. dbt's -vars flag to override variable values.

Answer: C,D

Explanation:
A encapsulates environment settings in reusable blocks- B allows for ad-hoc adjustments when running dbt- C is relevant for cloud-based setups, but less flexible for on-the-fly changes- D relates to model selection, not environment setup.

NEW QUESTION # 87
You're testing a model containing financial calculations. Your stakeholders require a high degree of accuracy. Which consideration is most important when designing tests for this scenario?

A. Implementing a large number of generic tests to provide basic coverage.
B. Understanding acceptable tolerances and writing tests to check values within a defined range rather than exact matches.
C. Using floating-point-specific equality checks for calculated values.
D. Ensuring tests cover a comprehensive range of input scenarios and edge cases.

Answer: B,D

Explanation:
B recognizes that financial calculations often have rounding imprecision, necessitating tolerance-based checks. C is crucial as even minor input variations can propagate into significant errors for financial models. A is generally a bad practice; D focuses on quantity, not the specific quality needed here.

NEW QUESTION # 88
31. Your entire DAG looks like the image shown.

(Several stg_ models appear upstream, feeding into int_ and fct_ models.) The question asks:
"Was this modeling rule violated?
Staging models dependent on other staging models"

A. No
B. Yes

Answer: B

Explanation:
In dbt's recommended layered modeling architecture, the staging layer is intended to provide a clean, one- to-one representation of raw source tables. Each stg_ model should depend only on sources, not on other staging models. This ensures staging remains a simple, transparent layer where data is renamed, recast, standardized, and lightly transformed before being passed to intermediate and mart layers.
In the DAG shown, at least one staging model (for example, stg_line_items or stg_tpch_line_items) appears downstream of another staging model, meaning a stg_ model is referencing another stg_ model. This violates dbt's recommended modeling practice, because it creates unnecessary complexity in the staging layer and reduces modularity and transparency. Downstream layers such as intermediate (int_) and marts (fct_) should be used to combine, enrich, or join multiple staging outputs.
When staging models depend on each other, it becomes harder to trace lineage, reduces clarity about where transformations occur, and complicates the entire DAG. The proper pattern is:
* Sources # Staging (stg_) # Intermediate (int_) # Marts (fct_)
Since the DAG shows staging models referencing other staging models, the rules have indeed been violated.

NEW QUESTION # 89
......

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