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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q131-Q136):

NEW QUESTION # 131
You maintain a large dbt project with various reporting models that source data from a set of core business tables. These core tables often include columns with sensitive information. What technique could ensure compliance and maintainability when restricting access to this data?

A. Use views to create different projections of the core tables, exposing only the necessary columns.
B. Create incremental models that directly remove the sensitive columns before reporting models utilize them.
C. Apply row-level security within the data warehouse itself for fine-grained control.
D. Write complex WHERE clauses in each reporting model to exclude rows containing sensitive data.

Answer: A

Explanation:
Views provide a modular and maintainable way to define access restrictions. This approach centralizes the filtering logic, preventing repeated and potentially inefficient WHERE clauses across models.

NEW QUESTION # 132
dbt Model Snippet:SQL

A. Your sources.yml file is not configured correctly-
B. There's a custom macro in use that dynamically changes schemas.
C. Your dbt_projectyml file has a model-level schema override.
D. The stg_orders model uses a schema config in its own model SQL

Answer: C

Explanation:
Model-level schema configurations override other settings. Check your project file for this model. The others are possible but less common patterns

NEW QUESTION # 133
A developer has updated multiple models in their dbt project, materialized as tables and views.
They want to run and test all models upstream and downstream from the modified models that are materialized as views.
What command will achieve this? Choose 1 option.

A. dbt build --select +state:modified, +config.materialized:view+
B. dbt build --select +state:modified+
C. dbt build --select +state:modified +materialized:view+
D. dbt build --select +state:modified, config.materialized:view+
E. dbt build --select @state:modified+, @config.materialized:view+

Answer: A

Explanation:
The requirement is:
* Select all models that have been modified # this uses the state selector:state:modified
* But only those modified models that are materialized as views # this uses the config selector:config.
materialized:view
* Then include upstream and downstream dependencies of those models # this requires adding + around the selectors.
To combine both selectors, dbt uses a comma-separated list within --select, meaning both selectors must match simultaneously.
The correct syntax is:
dbt build --select +state:modified, +config.materialized:view+
This command:
* Finds models whose state is modified.
* Filters them to only those materialized as views.
* Includes upstream (+) and downstream (+) dependencies.
* Runs and tests the selected nodes because dbt build includes running models, tests, seeds, and snapshots.
Why the other options are wrong:
* A and C use invalid selector syntax (config.materialized:view+ must be prefixed with +).
* B only selects modified models but does not filter by materialization type.
* D uses materialized:view, which is invalid without the config. prefix.
Thus, only Option E satisfies all conditions and uses correct dbt selector syntax.

NEW QUESTION # 134
Which types of transformations are often best suited for raw data as an initial step in your dbt project?

A. Both A and C-
B. Applying business logic and complex aggregations for reporting purposes.
C. Renaming columns and performing basic data cleaning
D. Casting and typecasting of columns to ensure consistent data types-

Answer: A

Explanation:
A: Consistent data types across sources are essential for further modeling. C Initial cleaning and renaming improve the usability of raw data.

NEW QUESTION # 135
Consider this DAG:
app_data.detail_categories -> stg_detail_categories -> skills_with_details app_data.details -> stg_details -> lessons_with_details What will support making this DAG more modular? Choose 1 option.

A. Union stg_detail_categories and stg_details in the staging layer to reduce the need for downstream joins.
B. Combine lessons_with_details and skills_with_details into one wide table called curriculum_with_details.
C. Join stg_detail_categories and stg_details in the staging layer to reduce the need for downstream joins.
D. Consolidate the two staging models into one model and then use this downstream for both skills_with_details and curriculum_with_details.
E. Examine the SQL of lessons_with_details and skills_with_details to see if there is a candidate for an intermediate model.

Answer: E

Explanation:
Modularity in dbt means breaking transformations into small, reusable steps that are easy to reason about, test, and reuse. In this DAG, two staging models (stg_detail_categories and stg_details) feed into two mart- level models (skills_with_details and lessons_with_details). If there is repeated logic in those two mart models-such as common joins, filters, or derived fields-that logic is a good candidate to be extracted into an intermediate model.
Option E follows dbt best practices: inspect the SQL of lessons_with_details and skills_with_details and identify shared logic to move into a new intermediate model (for example, int_details_with_categories). Both downstream models can then reference this intermediate model using ref(), centralizing logic and improving maintainability.
Options A and B push joins/unions into the staging layer, which should focus on clean, 1:1 representations of sources-not combining multiple sources. Option C collapses distinct staging models into one, reducing clarity and breaking the source-to-staging contract. Option D creates an overly wide, less reusable final table instead of focusing on shared reusable logic.
Therefore, the action that truly increases modularity is Option E: creating an intermediate model based on common SQL patterns.

NEW QUESTION # 136
......

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