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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q168-Q173):

NEW QUESTION # 168
Your development environment uses a smaller, sampled dataset compared to production for efficiency. When a new performance-sensitive model is deployed, users report significant slowdowns. What might be some of the potential causes?

A. Your data warehouse might have different resource configurations or indexing in the production environment.
B. Performance optimizations made in development might not translate directly to the larger production data volumes.
C. All of the above-
D. The smaller dataset in development didn't reveal performance issues that become apparent at the scale of production data

Answer: C

Explanation:
A: Sampled data obscures performance problems related to data volume. B: Optimizations designed for small data might not be effective at scale. C: Differences between environments can drastically affect performance.

NEW QUESTION # 169
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 # 170
"Sometimes columns that should have data are showing null values!" exclaims a colleague. What initial debugging steps would you suggest beyond examining the model logic itself?

A. Check any recent changes to source definitions or transformations on upstream data.
B. All of the above.
C. Verify that any where clauses or filters in the model are not inadvertently too restrictive.
D. Review if there are snapshots or merge strategies that might impact how the table is updated.

Answer: B

Explanation:
Don't limit yourself to the single model in question! Data flows throughout the project, and changes elsewhere might have cascading effects.

NEW QUESTION # 171
Your dbt project contains sensitive dat
a. You need to load seed data (e.g., lookup tables) while ensuring it's not accidentally committed to version control. How can you best achieve this?

A. Write the seed data into log files, excluding them from version control.
B. Create separate seed files and add them to your .gitignore or equivalent.
C. Employ a secrets management tool and integrate it with your dbt deployment.
D. Utilize environment variables to store seed data values.

Answer: B

Explanation:
Seed files designed for local use should be kept out of version control. While the other options have merits, they don't directly prevent sensitive data from being committed.

NEW QUESTION # 172
You encounter a dbt model using a series of nested subqueries to achieve complex filtering. Performance is poor. Which common alternative is likely to provide a performance boost?

A. Rewrite the subqueries into equivalent JOIN statements where possible.
B. Materialize the intermediate results of the subqueries as snapshots.
C. Replace the nested logic with a custom dbt macro to improve modularity.
D. Convert subqueries into nested CTEs for better readability.

Answer: A

Explanation:
Databases typically optimize joins more effectively than deeply nested subqueries. The other options don't directly address the core performance issue.

NEW QUESTION # 173
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