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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Weight	Objectives
Developing dbt Models	20%	- Model design and structure 1. Ref and source functions 2. Incremental models 3. Materialization types - Model optimization 1. Performance tuning 2. Model contracts
Debugging and Error Resolution	15%	- Identifying modeling errors - Debugging techniques - Resolving data quality issues
dbt Models Governance	15%	- Naming conventions and standards - Project organization and structure - Version control integration
Leveraging dbt State	5%	- State-aware operations - State management
External Dependencies	10%	- Managing snapshots - Using packages - External sources integration
Creating and Maintaining Documentation	10%	- Descriptions and metadata - Generating documentation - Documentation standards
Managing Data Pipelines	15%	- Deployment strategies - Pipeline orchestration - CI/CD integration
Implementing dbt Tests	10%	- Test configuration and execution - Custom tests - Built-in tests

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q76-Q81):

NEW QUESTION # 76
You're refactoring a dbt project with circular dependencies (e.g., Model A references Model B, which references Model A).
Select the most appropriate strategy:

A. Introduce a redundant intermediate model to resolve the circularity.
B. Add the -full-refresh flag to dbt run to force execution.
C. Rewrite SQL logic to remove the circular references.
D. Create snapshots to break the dependency chain.

Answer: C

Explanation:
Circular dependencies are a fundamental design flaw. 'Snapshots' and -full-refresh are workarounds. Introducing redundant models creates maintenance overhead.

NEW QUESTION # 77
A sudden data quality issue occurs in production. You need to quickly reproduce the problem in a lower environment but retain a point-in-time snapshot of production dat a. Which strategies might you employ? Create a backup of the production database and restore it to a separate development or testing database.

A. staging area.
B. A combination of the above, depending on time constraints and available resources.
C. Utilize data replication features (if supported by your data warehouse) to create a near real-time mirror of production data in a
D. Use dbt snapshots to capture critical production tables during a defined timeframe for later use in troubleshooting.

Answer: B

Explanation:
A: Backups offer static point-in-time snapshots. B: Replication might be ideal for immediate mirroring, if available. C: Snapshots are lightweight yet can be time-sensitive.

NEW QUESTION # 78
Given this dbt_project.yml:
name: "jaffle_shop"
version: "1.0.0"
config-version: 2
profile: "snowflake"
model-paths: ["models"]
macro-paths: ["macros"]
snapshot-paths: ["snapshots"]
target-path: "target"
clean-targets:
- "logs"
- "target"
- "dbt_modules"
- "dbt_packages"
models:
jaffle-shop:
+materialized: table
...and this warning when compiling your project:
[WARNING]: Configuration paths exist in your dbt_project.yml file which do not apply to any resources.
There are 1 unused configuration paths:
- models.jaffle-shop
What is the root cause?
A run hook in the jaffle_shop project was defined with an incorrect regular expression.

A. Yes
B. No

Answer: B

Explanation:
The true root cause is not related to run hooks or regular expressions. The warning clearly indicates that dbt found a configuration path that does not match any actual model path inside the project. In this case, the config block uses the key models.jaffle-shop, which is incorrect because dbt model paths must match the folder structure and cannot contain hyphens unless the folder itself contains a hyphen. Since the project folder is named jaffle_shop (with an underscore), dbt cannot map the configuration to any model.
dbt evaluates configuration paths based on directory names under the models/ folder. A mismatch between the folder name and the configuration key causes dbt to ignore the configuration entirely. This results in the warning:
"Configuration paths exist in your dbt_project.yml file which do not apply to any resources." This warning is specifically triggered when dbt identifies unused configuration paths due to typos, incorrect nesting, or invalid identifiers. dbt does not interpret hyphens (-) as valid Python identifiers for resource names, so a config path like models.jaffle-shop will never be applied.
Therefore, the assertion that the cause is a "run hook with an incorrect regular expression" is incorrect. The actual root cause is that the configuration path uses the wrong name (jaffle-shop instead of jaffle_shop).

NEW QUESTION # 79
You've included the popular 'dbt-utils' package in your project. Where should you primarily expect to find the models and macros it offers?

A. Within your 'models' directory namespaced to distinguish them from your own models.
B. Automatically materialized as new tables in your data warehouse.
C. In the 'dbt_project.yml' file under a 'packages' section.
D. Within the 'seeds' directory of your dbt project.

Answer: A

Explanation:
Packages extend your project's functionality by placing items within your model's directory offering seamless integration.

NEW QUESTION # 80
You have a dbt macro that generates a list of models to execute dynamically. Which dbt command should you use in conjunction with this macro to actually run those models?

A. dbt parse to pre-compile the project graph, allowing model selection by reference.
B. dbt build to create a task graph, extracting model nodes, and running them as needed.
C. dbt debug to step through model execution while using the macro's output
D. dbt Is to retrieve project metadata and feed it into your custom execution logic.

Answer: A

Explanation:
A makes the dbt graph available, including macros, during execution- B gets metadata, but not for execution. C helps troubleshoot, but not run macros. D isn't designed for dynamic macro output

NEW QUESTION # 81
......

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