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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q69-Q74):

NEW QUESTION # 69
A project is migrating from a staging environment to a production environment within the same data warehouse. However, the production data has more strict access controls. Which approaches could you take to manage this transition?

A. Use separate profiles.yml files for staging and production, each with their respective credentials.
B. All of the above.
C. Utilize a secrets management solution and integrate it into your dbt deployment process.
D. Store connection credentials as environment variables and reference them within the profiles.yml using Jinja templating.

Answer: B

Explanation:
A: Separate profiles allow storing environment-specific credentials. B: Environment variables keep sensitive data out of source control and allow for dynamic configuration of the profiles. yml file. C: Secrets managers offer more robust security for sensitive credentials.

NEW QUESTION # 70
(Multiple Select)

A. Macros can completely obfuscate dependencies, making them untraceable.
B. If macros themselves have dependencies on other macros, this adds an extra layer to consider.
C. If macros reference raw tables, those dependencies might not be reflected in dbt's lineage graphs.
D. Macros generally make dependency tracking easier by encapsulating logic.

Answer: B,C

Explanation:
Macros can introduce "hidden" dependencies. Also, if your macros build upon each other, you must track their internal dependencies as well.

NEW QUESTION # 71
(Multiple Select)

A. Implement a custom data test to specifically check the column for null values.
B. Switch the model materialization from 'view' to 'table'.
C. Add intermediate CTEs within the model's SQL to inspect data at different transformation stages.
D. Examine the source data for the model to verify the presence of expected values.

Answer: C,D

Explanation:
Verifying source data and stepwise debugging are key. Tests help but come later. Changing the materialization is unlikely to directly address the null-value issue.

NEW QUESTION # 72
Your team sometimes defines raw data sources directly in dbt models using .yml files within the models directory. When might this approach be problematic?

A. If the raw dataset's schema is subject to change.
B. If the raw dataset is accessed via a non-standard data warehouse connector.
C. If multiple models utilize the same raw dataset.
D. If the raw dataset requires complex transformations.

Answer: C

Explanation:
Defining raw sources within models leads to duplication and potential maintenance issues when multiple models share that dependency. A separate sources.yml file is generally preferred for shared datasets.

NEW QUESTION # 73
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 # 74
......

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