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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q30-Q35):

NEW QUESTION # 30
You have a complex dbt model that is essential for production reporting. To minimize the risk of breaking this model during development, what strategy might you employ?

A. Rely on your data warehouse's native rollback functionality to reverse changes if needed.
B. Create a duplicate model in production and only sync changes once they're fully tested-
C. Configure production jobs to use a pinned version of the dbt model.
D. Write extensive tests targeting this model in your production environment

Answer: B,C

Explanation:
A provides stability for the critical part of production- C offers a staging-like area within production. B is good, but doesn't prevent the breakage upstream. D isnt generally how dbt models operate-

NEW QUESTION # 31
You discover that dbt models create objects with the default database user's permissions. However, a policy mandates that analytics objects must be owned by a dedicated, less privileged account. How could you adapt your dbt workflow?

A. Write dbt macros that contain permission management logic as part of the model deployment.
B. Implement pre-hooks or post-hooks to execute ALTER OWNER statements after model creation.
C. Configure the target database to automatically assign desired ownership to new objects in the schema
D. Modify your dbt profile to use the less privileged account's credentials during job execution.

Answer: B,D

Explanation:
A gives direct control within dbt C changes what dbt "does" by running as a different user- B might exist but be too broad, D overcomplicates models.

NEW QUESTION # 32
Which of the following scenarios might warrant you using the --no-compile flag with dbt docs generate? When you have syntax errors in your dbt models that would prevent compilation.

A. recompilation.
B. When you want to quickly preview documentation changes without triggering a full model build.
C. When your CI/CD pipeline requires you to generate documentation as a separate step from model compilation.
D. When you're deploying documentation updates to a production environment and want to minimize risks associated with model

Answer: A,C,D

Explanation:
B: The -no-compile flag speeds up docs generation when a full model build is unnecessary- C: It isolates documentation updates in production environments to reduce risk.
D: This flag provides control within CIICD pipelines when needed

NEW QUESTION # 33
You've built a macro to implement a common custom test pattern. When using this macro in multiple models, you realize the tests are running much slower than expected. What might be the cause?

A. The macro's SQL logic is inefficiently querying the model or other data.
B. Too many tests are being defined on individual models.
C. This is expected behavior, and custom tests always incur a performance penalty.
D. The macro is accidentally triggering downstream model rebuilds.

Answer: A,B

Explanation:
Inefficient macro logic would be amplified with wide reuse. Having numerous tests, even simple ones, can add up. C is unlikely unless the macro directly references downstream models.

NEW QUESTION # 34
Which two are true for a dbt clone command?
Choose 2 options.

A. It allows comparison between manifests of source and target dbt runs, but does not create any objects itself.
B. It requires the reference to a manifest from a previous dbt invocation.
C. It can be used to replicate data across different data warehouses.
D. It allows testing your code changes on downstream dependencies outside of dbt (such as a BI tool).
E. It creates new versions of your dbt models suffixed with _v#.

Answer: B,D

Explanation:
The correct answers are A and C.
dbt clone is a command introduced to efficiently replicate existing relations (tables/views) from one schema
/environment to another within the same warehouse without fully rebuilding them. It typically leverages warehouse-level "clone" functionality (e.g., Snowflake zero-copy clone). To know what to clone, dbt needs information about previously built models; this is stored in artifacts like manifest.json. Therefore, A is correct:
it requires a reference to a previous manifest/state to know which models and locations to operate on.
C is also correct because cloning a production schema into a development schema enables you to test code changes while pointing downstream tools (like BI dashboards) at the cloned data. That way, BI or ML systems can exercise new dbt logic without impacting real production objects.
Option B describes state:modified / state comparison behavior, but dbt clone does create (or clone) objects, so B is false.
Option D is incorrect because clone works within a warehouse, not across different warehouses.
Option E is wrong: _v# suffixing is for model versioning, not cloning.
Thus, A and C correctly describe the behavior and purpose of dbt clone.

NEW QUESTION # 35
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