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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q26-Q31):

NEW QUESTION # 26
You're refactoring a complex dbt model and realize breaking it down into smaller models would improve readability. However, you're concerned about performance overhead due to the additional materializations. How would you address this concern?

A. Utilize ephemeral materializations for intermediate steps, where appropriate_
B. Profile your models to pinpoint bottlenecks and optimize those specifically.
C. Rely on source freshness checks to determine if intermediate models need to be rebuilt_
D. Switch to incremental materializations for all intermediate models.

Answer: B

Explanation:
Don't optimize prematurely. Profiling lets you identify the most performance-critical parts of your model before changing materializations. The other options may help but targeted optimizations are key.

NEW QUESTION # 27
You're using dbt Cloud and want to tighten control over when production deployments can occur. What dbt Cloud feature, in conjunction with your CI/CD configuration, would allow you to enforce this?

A. Environment variables for sensitive credentials.
B. Webhooks for triggering jobs by external events.
C. Deployment jobs and schedules.
D. dbt Cloud's built-in source freshness checks.

Answer: C

Explanation:
B enables you to define specific times or conditions for production deployments. Others are less directly related to controlling the timing.

NEW QUESTION # 28

Answer:

Explanation:

Explanation:
Update int_order_items by replacing:
* {{ source('tpch', 'orders') }} # {{ ref('stg_tpch_orders') }}
* {{ source('tpch', 'lineitem') }} # {{ ref('stg_tpch_line_items') }}
In dbt's recommended modeling pattern, sources should be referenced only in the staging layer. Downstream models (intermediate and mart/fact models) should reference staging models via ref(), not the raw source() directly. This keeps all raw-to-clean logic centralized in the staging layer and makes refactoring and documentation easier.
In the original DAG, int_order_items depends directly on tpch.orders and tpch.lineitem via source(), and staging models also depend on those same sources. That creates parallel paths from the sources and breaks the clean layered architecture.
By updating the int_order_items SQL to use:
from {{ ref('stg_tpch_orders') }}
join {{ ref('stg_tpch_line_items') }} ...
instead of:
from {{ source('tpch', 'orders') }}
join {{ source('tpch', 'lineitem') }} ...
you ensure that int_order_items sits entirely downstream of the staging layer. The new DAG becomes linear: sources # staging # intermediate (int_order_items) # any further marts. This improves modularity, reusability of cleaned logic, and makes the DAG easier to reason about and maintain.

NEW QUESTION # 29
(Multiple Select)

A. Implement unit tests specifically for the macro to validate its output under different inputs
B. Run dbt Is to confirm that all dependencies for the macro and the model are resolved correctly.
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: A,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 # 30
You're considering using dbt's --store-failures flag when running tests. Which of the following are potential implications of using this flag?

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

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 # 31
......

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