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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q79-Q84):

NEW QUESTION # 79
(Multiple Select)

A. Apply an incremental materialization strategy to subsequent models if appropriate.
B. Increase the query_comment timeout setting in your dbt_project. yml
C. Use CTEs or views in the source definition to pre-filter columns or apply initial transformations.
D. Introduce a source snapshot to create a point-in-time copy of the data.

Answer: A,C

Explanation:
Pre-filtering at the source definition and incremental downstream models reduce processing. Timeouts and snapshots address different issues.

NEW QUESTION # 80
You need to build a model to identify customers who haven't placed orders in the past 90 days. Performance with this model is critical, as it will trigger a re-engagement campaign. Which strategy is likely to yield the best results?

A. Filter the orders table for dates within the past 90 days, then find customers without corresponding order IDs.
B. Create a materialized view that contains only active customers and join against the orders table.
C. Join the customers and orders table, group by customer ID and use a HAVING clause to filter inactive customers.
D. IJse a window function to rank order dates for each customer and filter those where the most recent order is older than 90 days.

Answer: C

Explanation:
While other options might work, joining and using a HAVING clause tends to be a performant pattern for this use case in most databases. Pre-filtering customers in a materialized view could be useful but depends on how frequently that data changes.

NEW QUESTION # 81
You're considering using dbt's --store-failures flag when running tests. Which of the following are potential implications of using this flag?

A. The overall dbt test command may run faster-
B. This flag alters how dbt calculates test results
C. Troublesl-ming failing tests will become easier, as data related to the failure is persisted.
D. Successful models will not be rebuilt if downstream dbt tests are marked as failures.

Answer: C

Explanation:
A is the primary purpose of -store-failures. B is not how dependencies work in dbt. C is unlikely as additional data storage is involved. D is not the case; it influences preservation of data associated with the test, not its logic.

NEW QUESTION # 82
You're setting up a CI/CD pipeline for dbt deployments. Which actions can help maintain data integrity and consistency between your development, staging, and production environments?

A. Implement automated tests that validate data quality and model assumptions across all environments.
B. All of the above.
C. Enforce a branching strategy that mirrors environment progression (e.g., dev branch deploys to dev, etc. )
D. Utilize dbt logs and artifacts to track any changes made outside the standard deployment pipeline in each environment.

Answer: B

Explanation:
A: Tests ensure consistent behavior and catch issues early across environments. B: Structured branching aligns code changes to the environments they affect. C: Tracking unexpected changes is vital for auditing and troubleshooting.

NEW QUESTION # 83
Which explanation describes how dbt infers dependencies between models?
Choose 1 option.

A. .yml configurations for sources and refs are parsed for dependency information.
B. Information is gathered from the use of source and ref macros.
C. The underlying SQL code is parsed and relationships are created from explicit table references.
D. All source and ref macros are resolved to database objects and dbt queries them for dependencies.

Answer: B

Explanation:
The correct answer is A: Information is gathered from the use of source and ref macros.
dbt determines the dependency graph - the DAG - by analyzing calls to ref() and source() inside model SQL files. These macros explicitly declare relationships between models. When a developer writes ref ('orders'), dbt interprets this as: "the current model depends on the orders model." Similarly, source() indicates dependencies on upstream raw data sources. This declarative approach allows dbt to build a structured and deterministic DAG without scanning SQL for implicit table references.
Option B is incorrect because dbt does not query database objects to infer dependencies; it resolves dependencies at compile time through metadata generated from model files. Option C is incorrect because dbt intentionally does not parse SQL to detect table names-this would be brittle and error-prone across warehouses. Instead, dbt requires explicit references to maintain reliability. Option D is incorrect because YAML files define metadata about models and sources but do not create dependency relationships between them.
Thus, the dependency graph is built exclusively by reading ref() and source() macro calls, which ensures clarity, correctness, and maintainability within the analytics engineering workflow.

NEW QUESTION # 84
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