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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Objectives
Topic 1: dbt Core Concepts	- Models and materializations 1. Views, tables, incremental models 2. Ref and source functions - Project structure and configuration 1. dbt_project.yml configuration 2. Packages and dependencies
Topic 2: Analytics Engineering Foundations	- Modern data stack concepts (ELT vs ETL) 1. Role of dbt in analytics engineering 2. Warehouse-centric transformation workflows - SQL proficiency for analytics 1. Joins, aggregations, and window functions 2. Data modeling in SQL
Topic 3: Documentation and Lineage	- dbt documentation system 1. Model descriptions and metadata 2. Auto-generated docs site - Data lineage understanding 1. Directed acyclic graph (DAG) 2. Dependency tracking with ref()
Topic 4: Testing and Data Quality	- Data validation strategies 1. Schema testing practices 2. CI-based validation workflows - Built-in and custom tests 1. Generic tests (unique, not null, relationships) 2. Custom SQL tests
Topic 5: Deployment and Orchestration	- Running dbt in production 1. CI/CD integration patterns 2. dbt Cloud and job scheduling - Environments and workflows 1. Version control with Git 2. Development vs production environments

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q103-Q108):

NEW QUESTION # 103
You identify the need to refactor several models with similar transformations. Which of the following is the first step to increase modularity and promote the DRY philosophy?

A. Analyze commonalities and differences in the transformations to determine refactoring potential.
B. Create CTEs to segment logical steps within the existing model SQL.
C. Use a text editor's "find and replace" function to update repetitive code blocks.
D. Test individual models thoroughly to establish a baseline before making changes.

Answer: A

Explanation:
Understanding the shared and unique aspects of the transformations is key to designing an effective modularization strategy.

NEW QUESTION # 104
You're setting up a new dbt project on a recently provisioned data warehouse. What actions are essential before your initial development begins?

A. All of the above
B. Configuring monitoring and alerting to track data warehouse usage and events related to dbt jobs
C. Establishing access controls and user roles specific to the dbt project's requirements.
D. Defining and implementing a schema and naming conventions for your dbt models within the data warehouse.

Answer: A

Explanation:
A: Organization is key for maintainability and collaboration. B: Secure access is fundamental from the outset C: Visibility into job execution and resource usage is crucial.

NEW QUESTION # 105
A model refactors a frequently used SQL calculation. Traditionally this logic was copied across multiple models. Which of the following is the LEAST likely benefit of refactoring this calculation into a macro?

A. Improved readability of models
B. Reduced error potential due to a single source of logic
C. Faster dbt job execution time
D. Simplified dependency tracking

Answer: C

Explanation:
While macros can sometimes marginally improve performance, their primary benefits are maintainability, readability, and clear dependency tracking.

NEW QUESTION # 106
You define a source table and include an override:YAML

A. You need to create a macro named 'my_project' that contains the specific overriding logic.
B. The overrides property is not valid for tables within your dbt_project.yml.
C. The overrides property needs to be defined at the source level, not the table level.
D. The value 'my_project' is not a recognized keyword by dbt.

Answer: C

Explanation:
While dbt supports overrides, they generally work at higher levels within your project file, not at the model level.

NEW QUESTION # 107
Your model has a contract on it.
When renaming a field, you get this error:
This model has an enforced contract that failed.
Please ensure the name, data_type, and number of columns in your contract match the columns in your model's definition.
| column_name | definition_type | contract_type | mismatch_reason |
|-------------|------------------|----------------|-----------------------|
| ORDER_ID | TEXT | TEXT | missing in definition |
| ORDER_KEY | TEXT | | missing in contract |
Which two will fix the error? Choose 2 options.

A. Add order_key to the model SQL.
B. Remove order_id from the model SQL.
C. Remove order_id from the contract.
D. Add order_key to the contract.
E. Remove order_key from the contract.

Answer: C,D

Explanation:
dbt model contracts enforce that the column names, data types, and number of columns defined in the contract exactly match the columns produced by the compiled SQL. If any column appears in one location (the contract or the SQL) but not in the other, dbt raises an enforcement error.
In this scenario, the error message shows:
* ORDER_ID is missing in the model definition, meaning the SQL no longer contains a column named order_id, but the contract still expects it.
* ORDER_KEY is missing in the contract, meaning the model SQL now contains a new column, but this column has not been added to the contract.
To fix the mismatch, you must remove columns from the contract that no longer exist in the SQL and add to the contract any new columns that now appear in the SQL.
Therefore:
* Option A - Remove order_id from the contract - is correct because the column no longer exists in the model SQL.
* Option D - Add order_key to the contract - is correct because the SQL now produces this column.
Options B and C incorrectly alter the wrong side of the definition, and Option E would create a mismatch in the opposite direction.
Thus, the correct fixes are A and D.

NEW QUESTION # 108
......

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