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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q296-Q301):

NEW QUESTION # 296
A new feature you're developing requires significant changes across multiple models in your dbt project. Which branching strategy would be the most suitable and why?

A. Create separate branches for each model you'll be modifying to minimize conflicts.
B. Avoid using branches altogether, as they can introduce unnecessary complexity into a dbt workflow
C. Create a feature branch for the new development to isolate your work.
D. Work directly on the main branch, as it ensures your changes are always up-to-date.

Answer: C

Explanation:
Feature branches provide a dedicated workspace allowing you to work on changes without impacting the stability of the main branch. This is vital for larger changesets.

NEW QUESTION # 297
A new dataset is loaded into your warehouse as a table daily. You need to create a dbt source for it. Which is the MOST essential element to include in your configuration?

A. Schema and table name.
B. A source freshness check.
C. A detailed description in the dbt_projectyml.
D. An enabled flag set to 'True'

Answer: A

Explanation:
At a minimum, dbt needs to know where to find this source table. Freshness checks and others are valuable but not strictly required for the source to function.

NEW QUESTION # 298
You run a new dbt job, but several models don't execute. Logs indicate: "Database Error in model. The error message seems unrelated to your recent model code changes. What's a possible non- SQL cause?

A. There's a configuration error in your project file causing dbt to skip certain models.
B. A macro used in multiple models has an error that prevents the models from compiling correctly.
C. You've accidentally deleted a source table that these models depend on.
D. dbt's connection to the database has incorrect credentials or is encountering network issues.

Answer: D

Explanation:
Sometimes, what appears as a model error has roots in database connectivity. The others could be the cause, but check your project-wide connection setup first.

NEW QUESTION # 299
(Multiple Select)

A. Introduce a source snapshot to create a point-in-time copy of the data.
B. Apply an incremental materialization strategy to subsequent models if appropriate.
C. Use CTEs or views in the source definition to pre-filter columns or apply initial transformations.
D. Increase the query_comment timeout setting in your dbt_project. yml

Answer: B,C

Explanation:
Pre-filtering at the source definition and incremental downstream models reduce processing. Timeouts and snapshots address different issues.

NEW QUESTION # 300
You've implemented a macro to standardize descriptions across a set of models. After updating the macro and regenerating docs, the changes don't seem to be reflected. What could be the cause?

A. Changes to macros sometimes require a full re-compilation of all models for the updates to take effect in the documentation.
B. Your macro implementation has a subtle logic error.
C. The macro might only be applied to specific model types.
D. The documentation generator is caching the old version of the macro.

Answer: A,B,D

Explanation:
A: Errors in macro logic can prevent intended results. B: Similar to other content, macros can be cached, so clearing your browser cache might be needed. C: Due to dependency relationships, macro changes might sometimes require re-compilation of affected models to take full effect in the docs.

NEW QUESTION # 301
......

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