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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q303-Q308):

NEW QUESTION # 303
You have an urgent hotfix that needs to be deployed while a larger feature branch is still under review. Which strategies could you consider?

A. Merge the hotfix into the main branch and then rebase the feature branch on top of the new main branch.
B. Temporarily revert the feature branch changes, deploy the hotfix, and then restore the feature branch.
C. Merge the hotfix into the feature branch and wait for the entire feature to be approved.
D. Create a dedicated branch from the main branch specifically for the hotfix.

Answer: A,D

Explanation:
B: A dedicated hotfix branch isolates changes and provides the quickest path to deployment. D: Rebasing the feature branch after the hotfix ensures it includes the necessary fix.

NEW QUESTION # 304
(Multiple Select)

A. Ensuring a primary key column has no null values.
B. Confirming that the total count of orders in a day matches a rollup value in a pre-calculated summary table.
C. Checking if values in a specific column fall within a valid range based on business logic.
D. Verifying that a paid_at timestamp column always occurs after a signup_at timestamp column.

Answer: A,D

Explanation:
Generic tests (not_null and unique) handle basic structural checks. Options C and D involve domain specific logic or external comparisons better suited for custom tests.

NEW QUESTION # 305
You create tests to validate counts and timestamps against a baseline dataset. The tests fail unexpectedly. What might be possible root causes?

A. There are differences in data types or time zone handling between the model and baseline.
B. Your tests are too strict and need more generous tolerances.
C. Your upstream ETL process has a logic error, leading to incorrect data.
D. The baseline dataset itself has become corrupted

Answer: A,B,C,D

Explanation:
Each scenario could cause a baseline comparison test to fail, covering issues in the data pipeline, the baseline, and the test logic itself.

NEW QUESTION # 306
Security best practices dictate that data warehouse access should follow a "least privilege" principle. How would you implement this within dbt environments?

A. Leverage dbt's built-in grants mechanism to fine-tune object-level access within models.
B. A combination of the above approaches.
C. Create separate data warehouse user accounts for development, staging, and production, each with minimal required permissions.
D. Restrict access to sensitive data in development and staging environments using encryption or masking techniques.

Answer: B

Explanation:
A: Separate accounts limit the impact of any security breach- B: dbt grants provide model-aware access control. C: Data protection should be present even in non-production environments.

NEW QUESTION # 307
(Multiple Select)

A. Run dbt Is to confirm that all dependencies for the macro and the model are resolved correctly.
B. Implement unit tests specifically for the macro to validate its output under different inputs
C. Add {{ log(macros, info=true) statements within the macro to track how it executes-
D. Check the generated column names in the compiled SQL to ensure they match existing columns in the data.

Answer: B,D

Explanation:
Validate the generated SQL output from the macro, and use dedicated tests to ensure the macro's logic is sound. Logging can be helpful, and dbt Is is generally for dependency checking, not dynamic object validation.

NEW QUESTION # 308
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