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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Objectives
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()
Analytics Engineering Foundations	- SQL proficiency for analytics 1. Data modeling in SQL 2. Joins, aggregations, and window functions - Modern data stack concepts (ELT vs ETL) 1. Warehouse-centric transformation workflows 2. Role of dbt in analytics engineering
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
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
dbt Core Concepts	- Project structure and configuration 1. dbt_project.yml configuration 2. Packages and dependencies - 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 (Q131-Q136):

NEW QUESTION # 131
You're merging changes from a feature branch into your main branch. Which of the following Git commands or strategies help keep a clean and easy-to-follow project history?

A. Using git merge to create a merge commit.
B. Employing git squash merge for condensing feature branch commits.
C. Utilizing git rebase main before merging to linearize the history.
D. All of the above can be valid strategies, each with different trade-offs.

Answer: D

Explanation:
A Merge commits preserve branch structure but can make history more complex. B: Rebasing creates a linear history, making it simpler to follow C: Squash merges combine multiple commits, simplifying the history of major features. The best approach depends on your team's preferences and the feature's complexity

NEW QUESTION # 132
Performance Bottleneck

A. Implement database-level indexes on columns involved in the joins, if appropriate.
B. Run EXPLAIN or equivalent on the compiled query to pinpoint the most expensive operations.
C. Add -full-refresh to dbt run on the model to ensure downstream models don't cause errors.
D. Introduce CTEs to pre-calculate aggregated data or common subexpressions, especially if reused repeatedly.

Answer: B,D

Explanation:
EXPLAIN diagnoses the query plan, while CTEs can restructure for optimization. Indexes are database-level changes. A full refresh is unnecessary and disregards incremental logic.

NEW QUESTION # 133
(Multiple Select)

A. The fct_orders table might lack an appropriate index for the warehouse_code column.
B. Dynamically generating column names in a loop is inefficient for most databases.
C. The nested CASE statement with repeated conditional checks is causing redundant calculations.
D. The Jinja templating itself introduces a significant processing overhead.

Answer: B,C

Explanation:
Dynamic column creations and repeated CASEevaluations often hinder database optimization. Indexes matter too, while Jinja itself is less likely the core bottleneck.

NEW QUESTION # 134
A team member has opened a pull request, but conflicts arise when you attempt to merge it. Which of the following are essential steps in the conflict resolution process?

A. Manually edit the affected files in your favorite text editor to resolve the differences.
B. All of the above.
C. Communicate with your team member to understand the intent behind the conflicting changes.
D. Utilize Git's built-in merge conflict markers to identify areas of disagreement

Answer: B

Explanation:
A: You'll need to carefully edit files to combine the changes correctly. B: Git conflict markers visually highlight where conflicts need to be addressed. C: Communication is key to determining the best way to reconcile conflicting changes and maintain code intent.

NEW QUESTION # 135
You need to run a custom Python script after your dbt models execute. Where and how can you configure this in your dbt_project.yml file?

A. Define it as a 'snapshot' to ensure it runs after all models.
B. Include the script as a 'seed' file with a specific execution order
C. Create an 'integration test' and use the PythonFunction class-
D. Utilize the on-run-end hook configuration.

Answer: D

Explanation:
on-run-end hooks allow you to execute arbitrary code at the end of a dbt run.

NEW QUESTION # 136
......

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