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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q112-Q117):

NEW QUESTION # 112
While refactoring models, you accidentally drop a table in your development environment that mirrors production. Which inherent characteristic of development environments likely contributed to this issue?

A. Development environments often have less stringent permissions to facilitate experimentation.
B. Development datasets might be incomplete or outdated compared to productiom
C. Both A and C-
D. Developers working in development environments might expect the ability to make and revert changes with fewer consequences.

Answer: C

Explanation:
A Relaxed permissions enable rapid prototyping but can lead to accidents. C: Developers generally take a more 'free-to-change' approach in development environments-

NEW QUESTION # 113
A package you depend on has released a major update with breaking changes. How might you approach the migration?

A. Update your packages.yml to point to the new version and immediately refactor all your dependent models.
B. Review the release notes and the migration guide provided by the package maintainers.
C. Pin the package to the older version in your packages.yml file and plan a controlled migration.
D. Fork the package and maintain your own custom version indefinitely.

Answer: B

Explanation:
Understanding the changes is key' Then plan a staged migration to minimize disruption. Forking should be a last resort.

NEW QUESTION # 114
You accidentally pushed a large file containing sensitive data to a public remote repository. What immediate actions should you take?

A. Use git reset -hard to revert the commit and remove the file locally.
B. Contact the repository hosting service for assistance.
C. Permanently remove the sensitive file from Git history using tools like git filter-branch.
D. Both B and C.

Answer: B,C

Explanation:
B: You must ensure the sensitive data is completely removed from the history of the repository. C: The hosting service might have additional security measures or be able to assist in preventing the sensitive data from being accessed

NEW QUESTION # 115
(Multiple Select)

A. Implement a custom data test to specifically check the column for null values.
B. Examine the source data for the model to verify the presence of expected values.
C. Add intermediate CTEs within the model's SQL to inspect data at different transformation stages.
D. Switch the model materialization from 'view' to 'table'.

Answer: B,C

Explanation:
Verifying source data and stepwise debugging are key. Tests help but come later. Changing the materialization is unlikely to directly address the null-value issue.

NEW QUESTION # 116
Which types of transformations are often best suited for raw data as an initial step in your dbt project?

A. Casting and typecasting of columns to ensure consistent data types-
B. Applying business logic and complex aggregations for reporting purposes.
C. Both A and C-
D. Renaming columns and performing basic data cleaning

Answer: C

Explanation:
A: Consistent data types across sources are essential for further modeling. C Initial cleaning and renaming improve the usability of raw data.

NEW QUESTION # 117
......

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