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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Objectives
Topic 1: Documentation and Lineage	- Data lineage understanding 1. Directed acyclic graph (DAG) 2. Dependency tracking with ref() - dbt documentation system 1. Model descriptions and metadata 2. Auto-generated docs site
Topic 2: Deployment and Orchestration	- Environments and workflows 1. Version control with Git 2. Development vs production environments - Running dbt in production 1. CI/CD integration patterns 2. dbt Cloud and job scheduling
Topic 3: Analytics Engineering Foundations	- SQL proficiency for analytics 1. Joins, aggregations, and window functions 2. Data modeling in SQL - Modern data stack concepts (ELT vs ETL) 1. Warehouse-centric transformation workflows 2. Role of dbt in analytics engineering
Topic 4: 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
Topic 5: dbt Core Concepts	- Project structure and configuration 1. Packages and dependencies 2. dbt_project.yml configuration - Models and materializations 1. Ref and source functions 2. Views, tables, incremental models

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q82-Q87):

NEW QUESTION # 82
A dbt deployment pipeline fails on the production environment, yet everything works as intended in development and staging. Which areas would you investigate when troubleshooting?

A. Check if all required permissions are granted to the dbt user on the production database.
B. Consider schema differences, if any between the production data warehouse and other environments.
C. Verify the credentials and connection details between production and other environments in your profiles-yml.
D. Ensure consistency of dependency versions across different environments-

Answer: D

Explanation:
A: Connection errors lead to deployment failures. B: Mismatched schemas can break models. C: Lack of permissions prevents dbt from performing its job. D: Inconsistent dependency versions across environments can sometimes introduce subtle differences in behavior, leading to production-specific issues.

NEW QUESTION # 83
(Multiple Select)

A. Defining generic tests that apply to multiple data sources.
B. Populating a lookup table with country codes.
C. Loading configuration data for production environments that differs from development settings.
D. Inserting large volumes of historical transactional data for backfilling purposes.

Answer: B,C

Explanation:
seeds are ideal for static reference data and environment-specific configurations. Large data backfills are usually handled outside of 'seeds'. Generic tests would be defined in the tests section.

NEW QUESTION # 84
You're refactoring a complex SQL query into a dbt model. You notice that the model's output has significantly fewer rows than expected. Which of the following are potential causes?

A. There may be a LIMIT statement leftover from development.
B. Your model has accidental WHERE conditions that are too restrictive.
C. An incorrect materialization setting on the model.
D. Upstream data has quality issues with null values or unexpected joins.

Answer: A,B,D

Explanation:
All except D could limit the result set more than expected. Materialization settings impact how tables are built, not the SQL logic itself.

NEW QUESTION # 85
Which of the following is NOT a direct benefit of thorough source, table, and column descriptions in dbt?

A. Better data quality due to the automated enforcement of column descriptions
B. Improved lineage and data traceability within the documentation.
C. Faster dbt model compilation and execution times.
D. Increased discoverability for other team members working on the project

Answer: C

Explanation:
Explanation: While good descriptions have numerous benefits, they don't inherently speed up model compilation and execution within dbt-

NEW QUESTION # 86
Which two are true about version controlling code with Git?
Choose 2 options.

A. Git prevents any sensitive fields from being saved in code.
B. Git automatically creates versions of files with suffixes.
C. All the code changes along the lifecycle of a project are tracked.
D. When bugs are raised, email notifications are automatically sent by Git to repository users.
E. Code can be reverted to a previous state.

Answer: C,E

Explanation:
The correct answers are B: All the code changes along the lifecycle of a project are tracked, and E: Code can be reverted to a previous state.
Git is a distributed version control system designed to maintain a complete, chronological history of all code changes. Every commit records who made the change, when it occurred, and what the modification included.
This ensures transparency, reproducibility, and accountability across the development lifecycle, which makes B correct. Git also allows users to revert code to any previous commit, branch, or tag, making E correct as well. This capability is critical for recovering from mistakes, undoing faulty deployments, and ensuring stable releases.
Option A is incorrect because Git does not create file versions with suffixes; instead, it stores changes as snapshots within a repository. File suffixing is not part of Git's functionality.
Option C is incorrect because Git does not automatically send email notifications. Notification mechanisms come from hosting platforms like GitHub, GitLab, or Bitbucket-not from Git itself.
Option D is incorrect because Git does not prevent committing sensitive information. Developers must manually ensure secrets are excluded via .gitignore, secret managers, or pre-commit hooks. Git will store whatever is committed unless prevented through tooling.
Thus, only B and E correctly describe how Git supports version control in analytics engineering and dbt workflows.

NEW QUESTION # 87
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