dbt Architect study guide
dbt Architect · Work through your exam, one topic at a time.
The dbt Architect credential is listed on this page because candidates search for it, but the official dbt Architect certification page at getdbt.com returned a "page not found" error when retrieved on 2026-11-05, as did the certification overview page. That means no exam logistics - question count, time limit, passing score, fee, retake policy, validity period, or official domain weights - could be confirmed from the source. The dbt Architect study guide has 5 sections and about 2,428 words.
01dbt Project Architecture and Modeling
The project-architecture domain tests whether you can structure a dbt repository that stays maintainable as models and contributors multiply: layer boundaries, DAG shape, materialization choice, incremental logic, snapshots, and the contract and versioning surface that protects downstream consumers. Declare raw tables as sources in YAML and reference them with source() ; reference every model with ref() .
546 words, about 3 min02Data Platform and Environment Design
This domain tests the environment and platform decisions around a dbt project: how targets, credentials, schemas, warehouses, and platform features such as partitioning, table types, and access policies are configured so development, CI, and production stay isolated but consistent. profiles.yml holds outputs keyed by target name; target: selects one, usually from an environment variable.
506 words, about 3 min03Orchestration, Deployment, and CI/CD
Orchestration questions check that you can schedule, select, and gate dbt runs so production is correct and pull requests are cheap: job design, selectors, state-based selection, deferral, artifacts, and failure handling. dbt build runs models, tests, snapshots, and seeds in DAG order and skips dependents when a test fails.
449 words, about 2 min04Governance, Documentation, and Data Quality
Governance questions test whether consumers can find, trust, and correctly use what you build: documentation coverage, ownership and exposure metadata, test design, contract enforcement, and how quality failures surface to the people on call. persist docs writes model and column descriptions into the warehouse (Snowflake and BigQuery support comments), so BI users see definitions without opening dbt docs.
473 words, about 2 min05Performance, Cost Optimization, and Scalability
Performance questions test whether you can make a dbt project fast and cheap at scale - reducing bytes scanned, matching materializations to access patterns, sizing compute correctly, and avoiding needless rebuilds - without breaking correctness. Most warehouse cost is bytes read.
454 words, about 2 min