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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q93-Q98):

NEW QUESTION # 93
You debug a production issue by running a model in a testing environment with additional logging output. Which dbt features or command-line additions would facilitate this?

A. The -warn-error flag to treat warnings as errors, making your run fail louder on potential issues.
B. Custom dbt flags (using -vars) to control the logging level of the SQL execution.
C. Environment variables to conditionally enable verbose logging macros inside the model.
D. dbt hooks to redirect model-related logs to a centralized logging platform.

Answer: A,B,C

Explanation:
These provide control during execution:A changes model behavior. B makes execution more sensitive. C alters dbt's log handling.

NEW QUESTION # 94
You're working with a dbt project in both development and production environments.
Which configuration technique is best suited to handle differences in database connection details between these environments?

A. Create separate branches for each environment and maintain different dbt_project.yml files in each branch.
B. Define multiple profile sections within the dbt_project.yml
C. Utilize environment variables in conjunction with the profiles.yml file.
D. Use the target flag when running dbt commands.

Answer: C

Explanation:
Environment variables and a corresponding profiles.yml provide the most flexible and secure way to manage environment-specific settings.

NEW QUESTION # 95
Your development environment uses a smaller-scale version of the production warehouse. You often debug issues that depend on data volume, requiring a way to temporarily "scale up" your development environment. Which strategies are feasible?

A. Run a separate dbt project against production data for testing, but target a staging schema.
B. Write dbt macros that use temporary tables to simulate larger datasets during development
C. Dynamically increase resource allocation (CPU, memory) within your data warehouse, if supported.
D. Selectively snapshot a subset of production data into your development environment.

Answer: A,C,D

Explanation:
A directly addresses the resource limitation, if possible. C offers a controlled way to bring in real data. D creates a sandboxed environment within production. B is clever, but unlikely to be truly representative.

NEW QUESTION # 96
You've defined several source models using .yml files within your models directory. When you run dbt compile, they don't appear in the DAG visualization. What's the MOST likely cause?

A. You haven't used the ref() function correctly within your source models.
B. You haven't explicitly configured these sources in your dbt_project.yml file.
C. The source tables are missing appropriate permissions in your database.
D. The source definitions require additional schema tests to be valid.

Answer: B

Explanation:
While other issues are possible, sources must have configurations in dbt_project.yml to be included in the DAG.

NEW QUESTION # 97
While onboarding new data sources to your dbt project, you discover some have unexpectedly high null rates in key columns. How would you integrate tests for this into your dbt workflow?

A. Write tests within the initial staging models that calculate and check the null percentage.
B. Designate some nulls as acceptable by extending the not_null test with the OR condition.
C. Create not_null tests on the raw source tables in a separate dbt project dedicated to source validation.
D. Include a data quality monitoring step in your upstream ETL process before data reaches dbt

Answer: A,D

Explanation:
B ensures data entering dbt is checked at the first opportunity. C pushes quality checks as far upstream as possible- A is valid, but less integrated if sources are managed outside dbt's purview D masks the problem rather than addresses it.

NEW QUESTION # 98
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