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Dbt Certification 2026 Exam Prep | 120+ Practice Questions & Answers | Snapshots, Incremental Models, Macros, Git & dbt Core

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Prepare for the dbt Labs Certification Exam with this comprehensive 120+ practice questions and answers study guide designed for aspiring Analytics Engineers, Data Engineers, Data Analysts, and Business Intelligence professionals. This exam-focused resource covers the most frequently tested concepts in dbt Core, including hooks, macros, snapshots, exposures, metrics, incremental models, selectors, graph operators, materializations, tests, documentation, packages, variables, seeds, sources, YAML configuration, Git workflows, project configuration, artifacts, performance optimization, state comparison, deferral, and command-line interface (CLI) operations. The content is presented in an easy-to-follow question-and-answer format that helps reinforce both conceptual understanding and practical application of dbt workflows. This study guide closely follows the knowledge domains outlined in the dbt Fundamentals and dbt Developer Certification learning paths. Learners will strengthen their understanding of advanced dbt features such as snapshot strategies (timestamp and check), incremental model configurations, schema changes, metrics, exposures, documentation generation, testing strategies, source freshness, Git version control, package management, project architecture, and performance tuning. It also provides practical command-line examples and real-world scenarios commonly encountered in certification exams and production analytics engineering projects. The material serves as an effective last-minute revision guide, interview preparation resource, and self-assessment tool for professionals working with modern data stacks. Each question is structured to reinforce exam objectives while improving confidence in implementing dbt best practices across cloud data warehouse environments such as Snowflake, BigQuery, Redshift, Databricks, and PostgreSQL. This document is highly relevant for: Candidates preparing for the dbt Labs Developer Certification Analytics Engineers Data Engineers Data Analysts Business Intelligence Developers Data Warehouse Developers Cloud Data Engineers ETL/ELT Developers SQL Developers Data Platform Engineers Graduate students in Data Analytics Computer Science students Information Systems students Business Analytics students Professionals transitioning into Analytics Engineering References dbt Labs. (2024). dbt Documentation. dbt Labs. (2024). dbt Fundamentals Learning Path. dbt Labs. (2024). Analytics Engineering Certification Exam Guide. Kimball, R., & Ross, M. (2013). The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling (3rd ed.). Wiley. Keywords dbt certification, dbt labs certification, dbt developer certification, dbt exam questions, dbt practice questions, dbt interview questions, analytics engineering, dbt core, dbt cloud, incremental models, snapshots, dbt snapshots, macros, Jinja, materializations, exposures, metrics, selectors, graph operators, source freshness, dbt tests, generic tests, singular tests, dbt docs, dbt compile, dbt build, dbt run, dbt seed, dbt snapshot, dbt source freshness, dbt project, dbt_, , , YAML, SQL, Git, version control, data engineering, ELT, modern data stack, Snowflake, BigQuery, Redshift, Databricks, PostgreSQL, analytics engineer, business intelligence, data warehouse, command line, dbt artifacts, performance optimization, state comparison, deferral, package management, schema tests, source configuration, documentation generation

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Dbt Certification (Prep) 2026
Exam Questions and Answers |
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pre-hook - ANSWER ✔✔executed before a model, seed or snapshot

is built.


post-hook - ANSWER ✔✔executed after a model, seed or snapshot

is built


on-run-start - ANSWER ✔✔executed at the start of dbt run, dbt seed

or dbt snapshot


on-run-end - ANSWER ✔✔executed at the end of dbt run, dbt seed

or dbt snapshot

,T|F Operations are a separate resource in a dbt project. - ANSWER

✔✔False. Operations are macros that you can run using the run-

operation command command.


What are the required properties in defining an exposure? - ANSWER

✔✔Name, Type & Owner (email)


What are the different values of 'type' property of an exposure? -

ANSWER ✔✔dashboard, notebook, analysis, ml, application


T|F Exposures appear as orange-y nodes in the DAG - ANSWER

✔✔True.


What will command "dbt run -s +exposure:weekly_jaffle_metrics"

execute? - ANSWER ✔✔This will execute all models upstream of the

exposure.

On the first run of the "dbt snapshot" command, what is the value of

dbt_valid_to for all records? - ANSWER ✔✔Null


What are the different strategies used in creating a snapshot? -

ANSWER ✔✔Timestamp & Check


What is the required config when a 'check' strategy is used in creating a

snapshot? - ANSWER ✔✔check_cols

, When should a check strategy be used when creating a snapshot? -

ANSWER ✔✔The check strategy is useful for tables which do not

have a reliable updated_at column.

What is the required config when a 'timestamp' strategy is used in

creating a snapshot? - ANSWER ✔✔updated_at


What are the different snapshot meta fields? - ANSWER

✔✔dbt_valid_from, dbt_valid_to, dbt_scd_id, dbt_updated_at


What happens when the 'invalidate_hard_deletes' config is set to 'true'

while defining a snapshot? - ANSWER ✔✔Finds hard deleted records

in source, and set dbt_valid_to current time if no longer exists


T|F Seeds can be configured in the .csv file - ANSWER ✔✔False.

Seeds are configured in your dbt_project.yml


T|F Sources are declared in the dbt_project.yml file. - ANSWER

✔✔False. Sources are defined in .yml files nested under a sources: key.


How can the landing page of the generated documentation website be

customized? - ANSWER ✔✔By supplying your own docs block called

__overview__. i.e. {{ % docs _overview_ % }} ....... {{ % enddocs % }}




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