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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Objectives
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
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
Analytics Engineering Foundations	- Modern data stack concepts (ELT vs ETL) 1. Warehouse-centric transformation workflows 2. Role of dbt in analytics engineering - SQL proficiency for analytics 1. Joins, aggregations, and window functions 2. Data modeling in SQL
Deployment and Orchestration	- Environments and workflows 1. Development vs production environments 2. Version control with Git - Running dbt in production 1. dbt Cloud and job scheduling 2. CI/CD integration patterns
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 (Q198-Q203):

NEW QUESTION # 198
You've defined a model as follows:SQL

A. A pure SQL issue: The count() function isn't supported for the data type of a column in the 'cust_orders' source.
B. An incorrect materialization strategy causing the wrong data to be populated in a downstream source.
C. A data type mismatch between a column in 'stg_customers' and how the query is using it for the join.
D. A Jinja templating error: Misusing the source() function within the count() function.

Answer: A

Explanation:
While the others might cause problems, this error message strongly indicates a database- level type incompatibility, not a dbt-specific issue.

NEW QUESTION # 199
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. Utilize environment variables to store seed data values.
D. Employ a secrets management tool and integrate it with your dbt deployment.

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 # 200
You've added new tests but notice they aren't failing even though you've intentionally introduced errors. What's a LIKELY cause?

A. Your dbt project has multiple targets configured.
B. Your tests have incorrect severity thresholds.
C. You haven't compiled your project since writing the new tests.
D. The tests are defined in seeds.csv files.

Answer: C

Explanation:
Compilation is necessary for new tests to be recognized. Others are less likely, though severity thresholds could be a factor if misconfigured.

NEW QUESTION # 201
You get a compilation error: "Configuration key 'enabled' not found". Where in your dbt_project.yml would you start looking to fix this, and why?

A. In the top-level profiles.yml, as the enabled key is specific to setting up project profiles.
B. Within the models section, as models often use the enabled property.
C. Under a specific source definition, as sources can also sometimes be conditionally enabled.
D. In a seeds.yml file or the seeds section, where enabled is a valid property.

Answer: B

Explanation:
Since it's a generic error, start with the most common location - model configurations. The others might be valid in certain scenarios.

NEW QUESTION # 202
(Multiple Select)

A. Implementing a robust suite of schema tests to validate object references-
B. Setting up a linter for your SQL files, either as part of your IDE or code editor
C. Defining source freshness thresholds to catch issues with missing tables or views-
D. Using the 'defer' option to partially run a DAG and expose errors early

Answer: A,B

Explanation:
Linters improve code hygiene, and schema tests proactively validate assumptions about model dependencies. 'Defer' helps with logic errors, and freshness checks are for source health.

NEW QUESTION # 203
......

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