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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Objectives
dbt Core Concepts	- Models and materializations 1. Ref and source functions 2. Views, tables, incremental models - Project structure and configuration 1. dbt_project.yml configuration 2. Packages and dependencies
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
Testing and Data Quality	- Built-in and custom tests 1. Generic tests (unique, not null, relationships) 2. Custom SQL tests - Data validation strategies 1. CI-based validation workflows 2. Schema testing practices
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
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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q250-Q255):

NEW QUESTION # 250
(Multiple Select)

A. The column ordering within the table is changed.
B. Additional records are appended with new data.
C. A small percentage of records have null values introduced in a field.
D. A column is renamed

Answer: C,D

Explanation:
Renaming or introducing nulls can directly break downstream SQL that references that column. B (adding data) is generally less disruptive. Column order (D) is usually irrelevant for analytics.

NEW QUESTION # 251
You need to test whether the values of a specific column have changed over time. What might be the best approach to implement this kind of test within your dbt project?

A. rely on native version history features in your data warehouse (if available).
B. Use a snapshot to preserve a point-in-time reference of the column.
C. Write a dbt macro to create a temporary table with historical data to compare against.
D. Use a custom 'accepted_values' test together with a dbt seed.

Answer: A,B,C

Explanation:
A provides a clean comparison point C enables more complex historical analysis. D leverages built-in warehouse features, if you have them.

NEW QUESTION # 252
You get a warning during dbt run:WARNlNG: Found 100 rows in 'stg_customers' that will become duplicates in the 'dim_customers' target.

A. Investigate your dim_customers modeling logic to ensure a unique key is correctly used to prevent duplicates.
B. Update the stg_customers source definition to include filtering logic to remove duplicate rows.
C. Increase the memory allocated for the database to handle the large number of duplicates.
D. It's safe to ignore this warning, as de-duplication is handled automatically by dbt.

Answer: A

Explanation:
This typically indicates a logic flaw in your model creating the target table. Modifying the source or increasing memory won't resolve the core issue.

NEW QUESTION # 253
You've defined a unique test on a key column within a model. When running dbt test, you encounter a surprising failure. Which of the following might be the cause?

A. There are truly duplicate values in the column.
B. Upstream data was updated unexpectedly, leading to previously non-existent duplicates.
C. An incorrect join in your model is inadvertently creating duplicate rows.
D. The unique test is too strict and doesn't align with the business definition of uniqueness.

Answer: A,B,C,D

Explanation:
All of these could cause a previously passing test to fail, highlighting the interplay between data, model logic, and test definitions.

NEW QUESTION # 254
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. An enabled flag set to 'True'
C. A detailed description in the dbt_projectyml.
D. A source freshness check.

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 # 255
......

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