Master dbt (Data Build Tool) and assess your knowledge with 5 expertly crafted practice exams, covering 500+ unique questions that blend both conceptual understanding and real-world scenarios. This course helps you revise core dbt concepts, solidify your understanding of data transformations, modeling, testing, documentation, and deployment. Whether preparing for interviews or enhancing your practical expertise, these practice exams simulate real-world challenges and test your readiness for dbt projects in production environments.

Topics Covered in Practice Exams

Overview of dbt

• Definition, purpose, key features, and benefits

• Use cases: data transformation, modeling, and data quality testing

• dbt’s role in the modern data stack and integration with Snowflake, BigQuery, Redshift, and Databricks

Definition, purpose, key features, and benefits

Use cases: data transformation, modeling, and data quality testing

dbt’s role in the modern data stack and integration with Snowflake, BigQuery, Redshift, and Databricks

Installation and Setup

• Installing dbt (CLI and Cloud options)

• Initializing and structuring a dbt project

• Configuring profiles and connecting to different data warehouses

Installing dbt (CLI and Cloud options)

Initializing and structuring a dbt project

Configuring profiles and connecting to different data warehouses

Core Concepts

• Models: definitions, SQL transformations, and materialization types (view, table, incremental, ephemeral)

• Sources: defining and managing sources, source freshness checks

• Seeds: loading and using CSV files as seeds

Models: definitions, SQL transformations, and materialization types (view, table, incremental, ephemeral)

Sources: defining and managing sources, source freshness checks

Seeds: loading and using CSV files as seeds

SQL in dbt

• Using Jinja for templating, variables, macros, and filters

• Writing queries with ref and source functions

• Query optimization and best practices for handling large datasets

Using Jinja for templating, variables, macros, and filters

Writing queries with ref and source functions

Query optimization and best practices for handling large datasets

Testing and Validation

• Built-in tests (unique, not null, accepted values)

• Custom SQL-based tests using Jinja

• Data validation strategies and automated test workflows

Built-in tests (unique, not null, accepted values)

Custom SQL-based tests using Jinja

Data validation strategies and automated test workflows

Documentation

• Generating and maintaining project documentation

• Lineage graphs, YAML-based metadata management, and documentation best practices

Generating and maintaining project documentation

Lineage graphs, YAML-based metadata management, and documentation best practices

Macros and Reusability

• Writing reusable macros and parameterized transformations

• Installing and managing dbt packages like dbt-utils

• Advanced templating techniques with custom filters and control flow

Writing reusable macros and parameterized transformations

Installing and managing dbt packages like dbt-utils

Advanced templating techniques with custom filters and control flow

Incremental Models and Performance

• Creating incremental models and using is_incremental logic

• Partitioning, clustering, and performance tuning best practices

• Debugging and optimizing query execution plans

Creating incremental models and using is_incremental logic

Partitioning, clustering, and performance tuning best practices

Debugging and optimizing query execution plans

Version Control and Collaboration

• Using Git for version control, branching strategies, and environment management

• Collaboration best practices for teams and code reviews

• Integrating dbt with CI/CD pipelines using GitHub Actions, GitLab CI, etc.

Using Git for version control, branching strategies, and environment management

Collaboration best practices for teams and code reviews

Integrating dbt with CI/CD pipelines using GitHub Actions, GitLab CI, etc.

Deployment and Scheduling

• Managing jobs and schedules in dbt Cloud

• Integrating with external orchestrators like Airflow, Prefect, and Dagster

• Environment management for development, staging, and production

Managing jobs and schedules in dbt Cloud

Integrating with external orchestrators like Airflow, Prefect, and Dagster

Environment management for development, staging, and production

Monitoring and Debugging

• Analyzing logs, artifacts, and debugging with dbt run/debug

• Monitoring query performance and tracking model execution times

• Running and debugging tests within pipelines

Analyzing logs, artifacts, and debugging with dbt run/debug

Monitoring query performance and tracking model execution times

Running and debugging tests within pipelines

Advanced Topics

• Custom materializations and cross-database modeling

• Managing dependencies across multiple warehouses

• Leveraging dbt models for data applications and analytics workflows

Custom materializations and cross-database modeling

Managing dependencies across multiple warehouses

Leveraging dbt models for data applications and analytics workflows

These practice exams will help you confidently review all major dbt features, techniques, and best practices — ensuring you are fully prepared to excel in dbt interviews and real-world data transformation projects.