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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Objectives
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
dbt Core Concepts	- Project structure and configuration 1. Packages and dependencies 2. dbt_project.yml configuration - Models and materializations 1. Views, tables, incremental models 2. Ref and source functions
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
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
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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q137-Q142):

NEW QUESTION # 137
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. Create an 'integration test' and use the PythonFunction class-
C. Include the script as a 'seed' file with a specific execution order
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 # 138
A colleague has made updates to some model descriptions and wants you to quickly regenerate the docs to only include those changes. Which command variation would be the most efficient without a full recompilation?

A. dbt docs generate --models +[model_namel
B. Utilize dbt's incremental model feature along with dbt docs generate -no-compile.
C. It's not possible to partially regenerate dbt docs; you need to run the full dbt docs generate command.
D. dbt docs generate -select [model_name]

Answer: D

Explanation:
The -select flag limits the documentation generation to the specified model(s). However, for this to work smoothly, dependencies need to be considered. If the updated model depends on others, they might need to be included or recompiled for the docs to be correct.

NEW QUESTION # 139
You encounter the following error when running dbt compile:Runtime Error in model customer _ churn (models/transformations/customer_churn.sql)

A. The fct_orders model depends on other models that haven't compiled successfully.
B. You need to add a schema test to validate the existence of the fct orders table.
C. There's a typo in how you're referencing the fct_orders object in your SQL.
D. The fct_orders source is not configured correctly in your dbt_project.yml file.

Answer: C

Explanation:
Explanation: This is a runtime error indicating incorrect usage of fct_orders. Options A and C could cause errors but of a different nature. Schema tests won't fix a referencing error.

NEW QUESTION # 140
Your project has models calculating order metrics. Some models aggregate data daily; others do monthly roll-ups. What's a likely performance issue if all models are materialized as tables?

A. Unnecessary computations, as monthly models would rebuild daily data redundantly.
B. Testing would become impractical due to increased runtimes.
C. Conflicts might arise due to models overwriting each other's data.
D. The DAG visualization would be too complex to understand.

Answer: A

Explanation:
Rebuilding pre-aggregated data is inefficient. While testing becomes slower (D), the primary concern is computational waste.

NEW QUESTION # 141
Select the dbt materialization that typically DOES NOT persist its results to disk:

A. Table
B. Ephemeral
C. Incremental
D. Snapshot

Answer: B

Explanation:
Ephemeral materializations are computed within CTEs (Common Table Expressions). Their results exist only during the query's execution and are useful for intermediate steps within complex transformations.

NEW QUESTION # 142
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