Free Practice Questions for DBT Analytics Engineering Certification

🔄 Last checked for updates July 19th, 2026

Study with 438 exam-style practice questions designed to help you prepare for the DBT Analytics Engineering. All questions are aligned with the latest exam guide and include detailed explanations to help you master the material.

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Exam Details

Key information about DBT Analytics Engineering

Official study guide

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Question formats CertSafari offers

Multiple choice
Ordering
Matching
True/False
Fill in the blank

price:

$200

language:

English

exam format:

Multiple-choice, Fill-in-the-blank, Matching, Hotspot, Build list, Discrete Option Multiple Choice (DOMC)

passing score:

65% or higher

prerequisites:

SQL proficiency, 6+ months of experience with dbt (Core or Cloud), foundational Git skills

delivery method:

Online proctored

duration minutes:

120

supported version:

dbt Core 1.7

number of questions:

certification validity:

2 years

Exam Topics & Skills Assessed

Skills measured (from the official study guide)

Domain 1: Developing and optimizing dbt models

Subdomain 1.1: Identifying and verifying any raw object dependencies

Identifying and verifying any raw object dependencies

Subdomain 1.2: Understanding core dbt materializations

Understanding core dbt materializations

Subdomain 1.3: Conceptualizing modularity and how to incorporate DRY principles

Conceptualizing modularity and how to incorporate DRY principles

Subdomain 1.4: Using commands such as build, run, test, docs, show, snapshot, and seed

Using commands such as build, run, test, docs, show, snapshot, and seed

Subdomain 1.5: Creating a logical flow of models and building clean DAGs

Creating a logical flow of models and building clean DAGs

Subdomain 1.6: Defining configurations in dbt_project.yml

Defining configurations in dbt_project.yml

Subdomain 1.7: Using dbt Packages

Using dbt Packages

Subdomain 1.8: Creating Python Models

Creating Python M odels

Subdomain 1.9: Providing access to users to models with the "grants" config

Providing access to users to models with the " grants " config

Subdomain 1.10: Creating snapshots in YAML

Creating snapshots in YA ML

Subdomain 1.11: Selecting the optimal incremental strategy based on a dataset's characteristics

S electing the optimal incremental strategy based on a dataset ' s characteristics

Subdomain 1.12: Validating model logic and schema definitions in dry-runs using the --empty flag

V alidating model logic and schema definitions in dry-runs using the -- empty flag

Subdomain 1.13: Running models in sample mode using the --sample flag

Running models in sample mode using the --sample flag

Subdomain 1.14: Understanding advanced dbt materializations such as microbatch

Understanding advanced dbt materializations such as microbatch

Domain 2: Managing dbt models governance

Subdomain 2.1: Adding contracts to models to ensure the shape of models

Adding contracts to models to ensure the shape of models

Subdomain 2.2: Creating different versions of our models and deprecating the old ones

Creating different versions of our models and deprecating the old ones

Subdomain 2.3: Defining constraints in YAML to enforce data integrity at the platform level

Defining constraints in YAML to enforce data integrity at the platform level

Domain 3: Debugging data modeling errors

Subdomain 3.1: Understanding logged error messages

Understanding logged error messages

Subdomain 3.2: Troubleshooting using compiled code

Troubleshooting using compiled code

Subdomain 3.3: Troubleshooting .yml compilation errors

Troubleshooting .yml compilation errors

Subdomain 3.4: Developing and implementing a fix and testing it prior to merging

Developing and implementing a fix and testing it prior to merging

Subdomain 3.5: Managing dbt behavior with flags

Managing dbt behavior with flags

Domain 4: Troubleshooting and optimizing dbt pipelines

Subdomain 4.1: Troubleshooting and managing failure points in the DAG

Troubleshooting and managing failure points in the DA G

Subdomain 4.2: Using dbt clone

Using dbt clone

Domain 5: Implementing dbt tests

Subdomain 5.1: Using generic, singular, custom, custom generic, and unit tests on a wide variety of models and sources

Using generic, singular, custom, custom generic, and unit tests on a wide variety of models and sources

Subdomain 5.2: Testing assumptions for dbt models and sources

Testing assumptions for dbt models and sources

Subdomain 5.3: Implementing various testing steps in the workflow

Implementing various testing steps in the workflow

Domain 6: Implementing and maintaining external dependencies

Subdomain 6.1: Implementing dbt exposures

Implementing dbt exposures

Subdomain 6.2: Implementing source freshness

Implementing source freshness

Domain 7: Leveraging the dbt state

Subdomain 7.1: Understanding state and state selection

Understanding state and state selection

Subdomain 7.2: Using dbt retry

Using dbt retry

Techniques & products

dbt Core

dbt Cloud

SQL

Git

Jinja

Macros

dbt Packages

Materializations (table, view, incremental, ephemeral)

dbt Snapshots

dbt Analyses

dbt Seeds

dbt Exposures

Source Freshness

dbt State

dbt run

dbt test

dbt docs

dbt seed

dbt compile

dbt source freshness

dbt docs generate

dbt build

dbt run-operation

dbt retry

dbt snapshot

dbt clone

Directed Acyclic Graphs (DAGs)

dbt_project.yml

.yml files

Model Contracts

Model Versioning

Model Deprecation

Model Access

Git branching strategies

Git commands (fetch, pull, merge)

Pull Requests

Data platforms

Data warehouses

Common Table Expressions (CTEs)

Window functions

Aggregations

Joins

Python models

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