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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q191-Q196):

NEW QUESTION # 191
You've included the popular 'dbt-utils' package in your project. Where should you primarily expect to find the models and macros it offers?

A. Within the 'seeds' directory of your dbt project.
B. Within your 'models' directory namespaced to distinguish them from your own models.
C. Automatically materialized as new tables in your data warehouse.
D. In the 'dbt_project.yml' file under a 'packages' section.

Answer: B

Explanation:
Packages extend your project's functionality by placing items within your model's directory offering seamless integration.

NEW QUESTION # 192
(Multiple Select)

A. Adding test records to validate a new custom data test.
B. Populating a staging environment with representative data for testing.
C. Loading data to fact tables as part of the regular ETL process.
D. Inserting rows for a generic date dimension table.

Answer: B,D

Explanation:
dbt seed is ideal for static data, often used in development and testing. Fact tables usually rely on upstream data sources.

NEW QUESTION # 193
You want to implement a macro that displays a warning icon next to models in the DAG that haven't been tested. Which of the following would be necessary to make this work?

A. All of the above.
B. The ability for the macro to query the results of test executions from the dbt run-
C. A custom CSS class for the warning icon and a way to apply it dynamically using the macro.
D. Configuration in your dbt_projectyml file to associate models with the relevant tests.

Answer: A

Explanation:
A: You'll need CSS to style the warning icon- B. The macro needs to determine if the associated tests for each model have passed or failed. C: dbt needs a way to understand which tests are related to which models for the macro to function correctly.

NEW QUESTION # 194
You're ready to integrate your feature branch into the main branch. Which scenarios would necessitate a pull request and code review process?

A. You are the only contributor to the dbt project.
B. You are working on a personal dbt project.
C. In all dbt projects, pull requests should be mandatory.
D. You are working in a collaborative team environment with established quality control practices.

Answer: C,D

Explanation:
C: Code reviews are crucial in collaborative settings for improving quality and sharing knowledge. D: While not strictly mandatory always, pull requests are a best practice even when working alone as they encourage structured change tracking-

NEW QUESTION # 195
(Multiple Select)

A. Using separate dbt profiles for production and development environments
B. Implementing pre-hooks and post-hooks to perform tasks before and after dbt model execution.
C. Integrating dbt with your CI/CD tool and using job-specific triggers or filters.
D. Scheduling less frequent jobs using dbt Cloud's built-in job scheduler

Answer: A,C,D

Explanation:
A enables environment-specific commands. B provides a way to launch different jobs. C handles scheduled tasks within dbt Cloud.

NEW QUESTION # 196
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