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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: Testing and Data Quality	- Data validation strategies 1. CI-based validation workflows 2. Schema testing practices - Built-in and custom tests 1. Generic tests (unique, not null, relationships) 2. Custom SQL tests
Topic 3: 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. Data modeling in SQL 2. Joins, aggregations, and window functions
Topic 4: Deployment and Orchestration	- Running dbt in production 1. dbt Cloud and job scheduling 2. CI/CD integration patterns - Environments and workflows 1. Development vs production environments 2. Version control with Git
Topic 5: 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()

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q105-Q110):

NEW QUESTION # 105
You've been tasked with customizing the appearance of the generated documentation. Which of the following would be the most efficient starting point?

A. Write a macro that programmatically generates a new documentation structure based on your requirements_
B. Investigate overriding the default CSS and JavaScript files used by the documentation site.
C. Search for third-party dbt packages designed for documentation customization.
D. Edit the json files created during the dbt docs generate process to adjust the display of information.

Answer: B,C

Explanation:
A: Overriding the CSS and potentially JavaScript provides customization flexibility without completely rebuilding the documentation structure. D: Extending dbt's functionality with packages often offers pre-built solutions for more extensive customization of the documentation site.

NEW QUESTION # 106
(Multiple Select)

A. Using SELECT * whenever possible to avoid explicitly specifying needed columns.
B. Preferring correlated subqueries over joins when relationships are one-to-one.
C. Leveraging appropriate indexes on columns used in joins and WHERE clauses.
D. Avoiding excessive use of UNION operations, especially within loops.

Answer: C,D

Explanation:
UNION operations can be expensive. Indexing is essential for query optimization. SELECT * and correlated subqueries are generally less performant.

NEW QUESTION # 107
You're refactoring a project with long model files containing intertwined logic. Which of the following is NOT a typical sign of a good candidate for applying modularity principles?

A. The models are relatively small and perform simple transformations.
B. Portions of the logic form distinct conceptual steps in the transformation process.
C. Renaming code blocks would improve readability.
D. The same SQL patterns are repeated with minor variations.

Answer: A

Explanation:
Small and simple models might already be sufficiently modular. The other options signal that refactoring into reusable components would be beneficial.

NEW QUESTION # 108
You assume two columns of type 'numeric' will always align in terms of precision and scale (number of decimal places). What's a key way to design your models and tests to be resilient even if this assumption changes?

A. Use explicit casting functions to enforce matching precision during calculations.
B. Always store all numeric values as strings to prevent these issues.
C. Write tests to compare the column metadata rather than their contents.
D. Avoid relying on exact equality when comparing values from these columns.

Answer: A,D

Explanation:
These strategies allow for flexibility with numeric comparisons, making your models less sensitive to changes in precision. C is a good idea but not a full solution. D is generally bad practice.

NEW QUESTION # 109
You are working with git to version control the dbt logic.
Order these steps to add or modify transformations to your dbt project.

Answer:

Explanation:

* Create a new branch in git and switch to itB. Update the dbt code according to the new logic
C. Create a Pull / Merge RequestD. Run some automated CI checks and/or manual review of the updated codeE. Merge the updated code to the main git branch
* Version control best practices in dbt follow the same engineering workflow used in modern software development. The first step is always to create a new git branch and switch into it, ensuring all work is isolated from production-ready code and allowing your team to develop safely without affecting others.
Once inside the feature branch, you update the dbt code according to the new logic, which may include modifying models, tests, macros, or documentation.
* Next, you create a Pull Request (PR), also known as a Merge Request, to propose integrating your changes into the main branch. This is important because dbt projects are collaborative, and PRs facilitate peer review, enforce project standards, and prevent regressions. Once the PR is created, automated CI pipelines-such as running dbt build, schema tests, data quality checks, and code-style checks-are executed. Reviewers may also manually inspect code for logic correctness, naming conventions, and modeling consistency.
* After all checks have passed and reviewers approve the PR, the final step is to merge the updated code into the main branch, making the new transformations part of the production dbt project. This workflow ensures governance, reliability, and auditable development practices, all of which are core principles in analytics engineering.

NEW QUESTION # 110
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