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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q61-Q66):

NEW QUESTION # 61
Several of your users report that dashboards built on top of your dbt models are intermittently displaying stale dat a. Which of the following might be a potential cause?

A. All of the above.
B. Some models have incorrect materializations, causing them to not update when expected
C. Source freshness checks are configured incorrectly, allowing older data to be processed
D. There are dependency issues in your DAG affecting execution order.

Answer: A

Explanation:
Each of these could lead to stale data Start by examining freshness checks, then materializations, and if those are correct, inspect the DAG for execution bottlenecks.

NEW QUESTION # 62
Examine the configuration for the source:
sources:
- name: jaffle_shop
schema: jaffle_shop_raw_current
tables:
- name: orders
identifier: customer_orders
Which reference to the source is correct?

A. {{ source('jaffle_shop', 'customer_orders') }}
B. {{ source('jaffle_shop', 'orders') }}
C. {{ source('jaffle_shop_raw_current', 'orders') }}
D. {{ source('jaffle_shop_raw_current', 'customer_orders') }}

Answer: B

Explanation:
In dbt, the source() function resolves a source by its declared source name and table name, not by the physical schema or identifier in the warehouse. The YAML block defines a source named jaffle_shop, and under that source, a table named orders. The identifier: customer_orders field tells dbt that although the logical table name is orders, the actual physical object in the warehouse is named customer_orders.
dbt always expects the syntax:
{{ source(source_name, table_name) }}
Here, the correct reference uses jaffle_shop as the source name and orders as the table name because these are the logical names assigned in the YAML. dbt internally resolves the physical table name via the identifier field, so the model should not reference customer_orders directly.
Option A and B are incorrect because the first argument is not the schema; dbt does not use schemas in the source() call. Option D is incorrect because customer_orders is the warehouse identifier, not the logical table name recognized by dbt.
Therefore, the correct reference is:
{{ source('jaffle_shop', 'orders') }}
This ensures consistent modeling, dependency tracking, and accurate documentation.

NEW QUESTION # 63
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 the raw dataset is accessed via a non-standard data warehouse connector.
B. If the raw dataset's schema is subject to change.
C. If multiple models utilize the same raw dataset.
D. If the raw dataset requires complex transformations.

Answer: C

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 # 64
You need to grant readonly access to the results of your dbt project to stakeholders outside your team. Considering security, which approach would be the MOST suitable?

A. Embed dbt model results within a Bl dashboard and manage access through the Bl tool.
B. Create a designated reporting schema and set up views that map to the dbt model outputs
C. Have stakeholders directly query the dbt models, providing access to the production data warehouse.
D. Regularly run dbt seed to export CSV files, which are then shared with stakeholders.

Answer: A,B

Explanation:
A provides fine-grained control within the database. C uses the Bl tool for the access management layer. B is too broad in terms of access. D is manual and less secure.

NEW QUESTION # 65
Your team follows a convention where commit messages include a relevant issue tracker reference (e.g., JIRA- 123). You've been working on multiple features and fixes. Which of the following best promotes clean commits?

A. Don't worry about commit messages, as the pull request title and description are more important.
B. Break down your changes into smaller, atomic commits, each referencing the issue it addresses.
C. Commit all your changes at once with a generic message like "Project updates."
D. Include all relevant issue references in a single commit message when you're ready to create a pull request.

Answer: B

Explanation:
Atomic commits focus on a single change, making history easier to understand. Referencing relevant issues creates a link between code changes and project tasks.

NEW QUESTION # 66
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