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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q25-Q30):

NEW QUESTION # 25
You've added detailed descriptions to your sources.yml file. Which of the following scenarios would still necessitate adding descriptions directly within your model files?

A. If different models utilize the same source table for different purposes.
B. If the source table undergoes a schema change, and you want to document these changes historically within the model.
C. You always need both source and model-level descriptions for complete documentation.
D. If you want to provide additional context or logic specific to how a source is used within that model.

Answer: A,B,D

Explanation:
A: Model descriptions allow for tailored explanations on how a source table is specifically used within the context of the model. B: Maintaining historical context of changes directly within the model can be valuable for traceability. C: Differing model use cases can warrant distinct descriptions for the same source table.

NEW QUESTION # 26

Answer:

Explanation:

Explanation:
(new_project)
(models:)
(grants)
(require-dbt-version: '>= 1.0.0' )
The dbt_project.yml file acts as the configuration backbone for every dbt project. dbt requires that project names follow a strict naming convention, allowing only lowercase letters, numbers, and underscores.
Therefore, new_project is the only valid name among the choices. Names with spaces or hyphens, such as new project or new-project, violate dbt's project naming rules.
To configure how models behave-materializations, schemas, tags, or per-directory overrides-dbt uses the models: block inside dbt_project.yml. This block defines configuration inheritance rules and is different from the config() function, which is used inside SQL models, not in YAML files.
When granting permissions on database objects created by dbt, you use the grants configuration. This allows you to define access roles at the model level or project level for downstream BI or analytics tools, ensuring proper data governance and controlled data exposure.
Finally, compatibility with specific dbt versions is set using require-dbt-version. The correct YAML formatting uses quotes, such as '>= 1.0.0', ensuring dbt interprets the value as a string. This prevents accidental mis-parsing and enforces version requirements during dbt run or dbt build.

NEW QUESTION # 27
You're working with a model that aggregates data across many countries. Which assumption about the data might be especially important to test for and handle in your model logic?

A. Encoding formats (UTF-8, etc.) may be inconsistent across source systems.
B. Different regions may use varying currency exchange rates.
C. The presence of unexpected HTML tags within textual data fields.
D. Time zones and daylight savings rules for timestamps could differ between data sources.

Answer: A,B,D

Explanation:
Each of these introduces unique challenges for aggregating data globally. While D can be an issue, it's less central to this specific scenario.

NEW QUESTION # 28
You've defined a complex custom singular test. While testing it initially on a development dataset, it times out. Which optimization techniques might be worth trying?

A. Ensure the test SQL uses appropriate indexes in the underlying database.
B. Disable the test in production environments and run it only in development.
C. Remove any redundant joins or subqueries from the test logic.
D. Rewrite the test using a dbt macro for better maintainability.

Answer: A,C

Explanation:
A and C target improving query performance. B deals with code organization, not speed. D sacrifices data quality for speed

NEW QUESTION # 29
The dataset raw user activity is critical to several models. You've been informed that a major schema change is scheduled for this source. Select the best way to proactively mitigate the potential impact of this change on your dbt project.

A. Pause all dbt jobs until the schema change risk is fully assessed.
B. Create custom data tests that validate the critical columns in raw_user_activity.
C. Communicate with the relevant data team to create a temporary staging table mirroring the old schema.
D. Run dbt test after the schema changes and address any failures.

Answer: B

Explanation:
Custom data tests proactively ensure that even if the schema changes, the critical data in raw_user_activity remains valid and consistent for your models. Other options are reactive or might introduce unnecessary downtime.

NEW QUESTION # 30
......

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