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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q54-Q59):

NEW QUESTION # 54
Your dbt_project.yml contains the following: YAML

A. No error: This is a valid configuration-
B. Compilation Error: Using "+" to indicate nested configuration is incorrect in -yml files.
C. Compilation Error: Invalid indentation causing a YAML parsing issue.
D. Runtime Error: Incorrectly specifying materialization within a nested YAML structure-

Answer: C

Explanation:
YAML relies on precise indentation. Materialization should be at the same level as the model name, not nested further.

NEW QUESTION # 55
(Multiple Select)

A. Continuous Integration (Cl) tools.
B. Data integration testing frameworks (e.g., Great Expectations)
C. Tools specializing in schema change detection.
D. Anomaly detection platforms.

Answer: B,C,D

Explanation:
All of these address different aspects of advanced data validation that complement the basic tests within dbt. Cl tools (D) are involved in running these checks but are not the data validation tools themselves.

NEW QUESTION # 56
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. Yes
B. No

Answer: A

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 # 57
Which of the following are essential elements of a well-structured pull request description?

A. References to related issues in the project tracker, if applicable.
B. A concise title summarizing the overall changes.
C. Explanations of the reasoning behind major changes and implementation choices.
D. Detailed logs of each Git command used during development.

Answer: A,B,C

Explanation:
A: Clear titles aid quick understanding of the PRs purpose. C: Explanations help reviewers and future developers understand the thought process behind the code. D: Issue references create traceability and context.

NEW QUESTION # 58
You discover that dbt models run significantly slower in your production environment than in development. What potential causes should you investigate?

A. Different database configurations or resource allocations (memory, CPU, etc.) between environments-
B. Inefficient use of snapshots or incremental models in your production dbt project_
C. Production data volume being significantly larger than development data.
D. Network latency impactingressing access to remote data sources.

Answer: A,C,D

Explanation:
Each of these directly affects performance and could differ significantly between environments. C is relevant, but less likely the sole cause if development is performant.

NEW QUESTION # 59
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