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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Objectives
Topic 1: 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
Topic 2: 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
Topic 3: 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
Topic 4: 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
Topic 5: 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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q126-Q131):

NEW QUESTION # 126
Examine the configuration for the source:
sources:
- name: jaffle_shop
schema: jaffle_shop_raw_current
tables:
- name: orders
identifier: customer_orders
Which reference to the source is correct?

A. {{ source('jaffle_shop_raw_current', 'customer_orders') }}
B. {{ source('jaffle_shop_raw_current', 'orders') }}
C. {{ source('jaffle_shop', 'customer_orders') }}
D. {{ source('jaffle_shop', 'orders') }}

Answer: D

Explanation:
In dbt, the source() function resolves a source by its declared source name and table name, not by the physical schema or identifier in the warehouse. The YAML block defines a source named jaffle_shop, and under that source, a table named orders. The identifier: customer_orders field tells dbt that although the logical table name is orders, the actual physical object in the warehouse is named customer_orders.
dbt always expects the syntax:
{{ source(source_name, table_name) }}
Here, the correct reference uses jaffle_shop as the source name and orders as the table name because these are the logical names assigned in the YAML. dbt internally resolves the physical table name via the identifier field, so the model should not reference customer_orders directly.
Option A and B are incorrect because the first argument is not the schema; dbt does not use schemas in the source() call. Option D is incorrect because customer_orders is the warehouse identifier, not the logical table name recognized by dbt.
Therefore, the correct reference is:
{{ source('jaffle_shop', 'orders') }}
This ensures consistent modeling, dependency tracking, and accurate documentation.

NEW QUESTION # 127
You encounter a dbt model using a series of nested subqueries to achieve complex filtering. Performance is poor. Which common alternative is likely to provide a performance boost?

A. Rewrite the subqueries into equivalent JOIN statements where possible.
B. Replace the nested logic with a custom dbt macro to improve modularity.
C. Materialize the intermediate results of the subqueries as snapshots.
D. Convert subqueries into nested CTEs for better readability.

Answer: A

Explanation:
Databases typically optimize joins more effectively than deeply nested subqueries. The other options don't directly address the core performance issue.

NEW QUESTION # 128
Regular test runs reveal inconsistent and intermittent test failures, despite no obvious changes to models or source dat a. What debugging strategies might you use?

A. Temporarily switch to a more consistent testing environment.
B. Use dbfs logging feature to add detailed output to the test execution.
C. Examine data warehouse performance metrics for resource constraints during test runs.
D. Consider if external dependencies used within dbt tests are themselves unstable.

Answer: B,C,D

Explanation:
Each helps track down potential causes of non-deterministic test behavior. D might be too drastic early on.

NEW QUESTION # 129
(Multiple Select)

A. model-paths
B. test-paths
C. database
D. materialized: view

Answer: A,B

Explanation:
model-paths and test-paths specify project directory structures- Options C would be within a model configuration, and D could be in a profile.

NEW QUESTION # 130
You've written dbt tests that assume a certain distribution of values in a column. Later, the upstream process generating the data changes unexpectedly. What's the most likely consequence?

A. dbt will automatically adjust your tests to accommodate the new distribution.
B. Your dbt models may silently produce incorrect results due to the changed distribution.
C. The changed data distribution has no impact on downstream dbt models.
D. The tests will fail, preventing the changed data from being loaded, preserving the old behavior.

Answer: B

Explanation:
This highlights the risk of outdated assumptions. Without failing tests, you may not even realize your analysis is now based on flawed data.

NEW QUESTION # 131
......

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