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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q198-Q203):

NEW QUESTION # 198
A user reports a critical security issue discovered in a third-party dbt package you use. Where could you most likely find updated information about potential vulnerabilities or fixes?

A. Directly from the maintainer of the dbt package, often in a GitHub repository or similar
B. The locally generated dbt documentation for the installed packages.
C. Within dbt Cloud's central package documentation and release notes.
D. The packages.yml file in your dbt project.

Answer: A

Explanation:
Package maintainers are the primary source of truth- However, options like A and C might provide additional resources:A. Your packages.yml might help identify the package C: dbt Cloud might aggregate some vulnerability information.

NEW QUESTION # 199
What must happen before you can build models in dbt?
Choose 1 option.

A. Raw data must be cleaned.
B. You must have created a service account in your data platform.
C. Sources must have been defined in your dbt project.
D. Underlying data must be accessible on your data platform.

Answer: D

Explanation:
The correct answer is C: Underlying data must be accessible on your data platform.
dbt does not perform data ingestion or data loading. Instead, dbt operates after raw data is already available in your warehouse. This means that before dbt can build any models-whether staging, intermediate, or mart- layer models-the underlying source data must already exist and be accessible in the connected data platform (Snowflake, BigQuery, Redshift, Databricks, etc.). dbt uses SQL to transform existing relations; therefore, if the data platform cannot access the underlying tables or external sources, model execution will fail.
Option A is incorrect because sources do not need to be defined before building models. Models can be built without using sources at all. Source definitions are optional metadata and lineage declarations, not prerequisites.
Option B is incorrect because service accounts are not required; dbt can connect through any credential mechanism supported by the warehouse (OAuth, user accounts, tokens, etc.).
Option D is incorrect because dbt itself performs transformations on raw data-cleaning raw data beforehand is not required; in fact, that is one of dbt's main responsibilities.
Thus, the only true prerequisite is that the warehouse must contain accessible underlying data.

NEW QUESTION # 200
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. dbt hooks to redirect model-related logs to a centralized logging platform.
B. Environment variables to conditionally enable verbose logging macros inside the model.
C. Custom dbt flags (using -vars) to control the logging level of the SQL execution.
D. The -warn-error flag to treat warnings as errors, making your run fail louder on potential issues.

Answer: B,C,D

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

NEW QUESTION # 201
You're setting up a dbt project with a model that generates a large intermediate result. You're concerned about performance. Which approaches could be helpful?

A. Investigate database-specific features (e.g., temporary tables ) and refactor dbt logic to leverage them.
B. Use a staging schema for the intermediate model and adjust the job's materialization settings.
C. Deploy the dbt project to a smaller development environment to reduce processing overhead
D. Schedule the model to run only during off-peak hours.

Answer: A,B

Explanation:
A allows customizing how the dataset is handled. B can potentially lead to performance gains. C may help, but isnt a direct solution. D is unrelated to the core issue.

NEW QUESTION # 202
Your team sometimes defines raw data sources directly in dbt models using .yml files within the models directory. When might this approach be problematic?

A. If multiple models utilize the same raw dataset.
B. If the raw dataset's schema is subject to change.
C. If the raw dataset requires complex transformations.
D. If the raw dataset is accessed via a non-standard data warehouse connector.

Answer: A

Explanation:
Defining raw sources within models leads to duplication and potential maintenance issues when multiple models share that dependency. A separate sources.yml file is generally preferred for shared datasets.

NEW QUESTION # 203
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