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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Objectives
Topic 1: 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
Topic 2: 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 3: 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 4: 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 5: Testing and Data Quality	- Data validation strategies 1. Schema testing practices 2. CI-based validation workflows - Built-in and custom tests 1. Custom SQL tests 2. Generic tests (unique, not null, relationships)

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q88-Q93):

NEW QUESTION # 88
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 # 89
You're refactoring a complex dbt model and realize breaking it down into smaller models would improve readability. However, you're concerned about performance overhead due to the additional materializations. How would you address this concern?

A. Profile your models to pinpoint bottlenecks and optimize those specifically.
B. Switch to incremental materializations for all intermediate models.
C. Rely on source freshness checks to determine if intermediate models need to be rebuilt_
D. Utilize ephemeral materializations for intermediate steps, where appropriate_

Answer: A

Explanation:
Don't optimize prematurely. Profiling lets you identify the most performance-critical parts of your model before changing materializations. The other options may help but targeted optimizations are key.

NEW QUESTION # 90
You want to use a YAML anchor to reuse a standard block of descriptive text within multiple models. How would you implement this?

A. YAML anchors are not directly supported in the context of dbt model and source descriptions-
B. Define a Jinja macro for the text block, and then call the macro within your descriptions.
C. Create a YAML anchor within the relevant model file where the text should be reused.
D. Insert a YAML anchor (&) in your dbt_projectyml file and reference it in your model files.

Answer: C

Explanation:
YAML anchors (&) and aliases (*) allow you to define reusable blocks of text within the same YAML file, such as model files or sources.yml.

NEW QUESTION # 91
You discover a vulnerability in a third-party lineage-enhancing dbt package you use. Where might you first look for information and potential fixes?

A. Only the authors of the dbt package would be able to address the vulnerability.
B. The GitHub repository or documentation of the dbt package.
C. Centralized dbt documentation or a discussion forum.
D. Directly in the manifest.json or packages.yml files within your project

Answer: B

Explanation:
The package maintainer is the primary source for updates and vulnerability fixes. However, other resources like A & C might provide additional information or community solutions.

NEW QUESTION # 92
You want to standardize descriptions across a large number of models that share the same underlying source schemas. Which approach might be the most efficient?

A. Manually copy and paste the descriptions into each model's YAML file.
B. Write a Jinja macro that dynamically generates descriptions based on metadata.
C. Create a CSV lookup file and maintain custom source descriptions within the file.
D. Focus on using a version control system and enforce descriptions directly within the source system.

Answer: B

Explanation:
Jinja macros offer flexibility and automation, making them ideal for generating consistent descriptions across many models based on source metadata

NEW QUESTION # 93
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