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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q175-Q180):

NEW QUESTION # 175
Security best practices dictate that data warehouse access should follow a "least privilege" principle. How would you implement this within dbt environments?

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

Answer: A

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 # 176
A large dimension table is joined frequently in your dbt models. Due to its size, these joins significantly impact performance. Which strategy might offer improvement?

A. Add a WHERE clause to the model to filter out unnecessary rows during the join.
B. If the dimension data rarely changes, convert it to an incremental model.
C. Investigate database-specific techniques like hash joins or distribution keys for optimizing the join.
D. Rewrite the join logic by nesting conditions within a CTE.

Answer: C

Explanation:
While other options could help marginally, they don't address the large-table join issue head- on. Database-level join optimizations are crucial for this scenario.

NEW QUESTION # 177
You have created a model called stg_tasks and now you need to implement tests.
You provide this in schema.yml:
version: 2
models:
- name: stg_tasks
columns:
- name: completed_at
tests:
- not_null:
- config:
where: "state = 'completed'"
You receive this compilation error:
[WARNING]: Did not find matching node for patch with name 'stg_tasks' in the 'models' section of file
'models/example/schema.yml'
How can you change the configuration on the not_null test to fix this compiler error?

A. tests:
- not_null:
config:
where: "state = 'completed'"
B. tests:
- not_null:
- config:
where: "state = 'completed'"
C. tests:
- not_null:
- config:
where: "state = 'completed'"
Choose 1 option.
D. tests:
- not_null:
config:
where: "state = 'completed'"

Answer: D

Explanation:
In dbt, when you configure a generic test like not_null in YAML, the configuration for that test must be a mapping, not another list item. The correct structure is:
tests:
- not_null:
config:
where: "state = 'completed'"
In your original YAML, you wrote:
tests:
- not_null:
- config:
where: "state = 'completed'"
The extra dash (- config) makes config an element of a list rather than a key under the not_null test. This breaks the expected shape of the test definition. When dbt parses the schema.yml, it fails to correctly interpret the patch for the stg_tasks model, which leads to the warning: "Did not find matching node for patch with name 'stg_tasks'...".
By removing the extra dash and nesting config directly under not_null, dbt now reads this as a single generic test named not_null with a config block that passes the where argument. This allows dbt to correctly attach the test to the completed_at column of the stg_tasks model and eliminates the compiler warning.
Therefore, Option B is the only structurally valid configuration and is the correct answer.

NEW QUESTION # 178
You've implemented a macro to standardize descriptions across a set of models. After updating the macro and regenerating docs, the changes don't seem to be reflected. What could be the cause?

A. The documentation generator is caching the old version of the macro.
B. The macro might only be applied to specific model types.
C. Your macro implementation has a subtle logic error.
D. Changes to macros sometimes require a full re-compilation of all models for the updates to take effect in the documentation.

Answer: A,C,D

Explanation:
A: Errors in macro logic can prevent intended results. B: Similar to other content, macros can be cached, so clearing your browser cache might be needed. C: Due to dependency relationships, macro changes might sometimes require re-compilation of affected models to take full effect in the docs.

NEW QUESTION # 179
Code Review

A. An explanation of the error's root cause and the reasoning behind your chosen solution.
B. An updated lineage graph produced by dbt docs generate to visualize the fix's impact on dependencies.
C. A second reviewer assigned specifically to assess the potential side effects of the fix.
D. Additional schema tests on the model to prevent similar issues from recurring.

Answer: A

Explanation:
Contextualizing the fix is crucial during code review. Docs, tests, and additional reviews are fantastic but might not always be feasible within time constraints.

NEW QUESTION # 180
......

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