dbt Cert
dbt Analytics Engineering Certification
The dbt Analytics Engineering Certification validates expertise in using dbt (data build tool) to transform raw data in cloud data warehouses into clean, tested, and documented analytical datasets. It covers dbt project structure, SQL modeling with Jinja, testing strategies, documentation, macros, packages, and deployment with dbt Cloud and dbt Core. This credential is the definitive certification for analytics engineers working in the modern data stack.
Study Materials in C3RT
dbt Cert Exam Overview
| Detail | Information |
|---|---|
| Full Name | dbt Analytics Engineering Certification |
| Governing Body | dbt Labs |
| Number of Questions | 65 |
| Time Limit | 120 minutes |
| Passing Score | 65% (approx 43/65) |
| Exam Fee | $200 USD |
| Category | IT Certifications |
| C3RT App Available On | iPhone, iPad, and Mac |
| Official Source | dbt Labs official website ↗ |
dbt Cert Content Areas and Domains
Domain weights are approximate and based on the dbt Labs content outline. Always verify at the official source before your exam.
Topics Covered
- ✓ Analytics Engineering Fundamentals, ELT vs ETL, the modern data stack, dbt's role
- ✓ dbt Project Structure, dbt_project.yml, profiles, sources, seeds, snapshots
- ✓ dbt Models, SQL models, materializations (table, view, incremental, ephemeral)
- ✓ Jinja Templating in dbt, ref(), source(), config(), if/for, macros
- ✓ dbt Testing, generic tests (not_null, unique, accepted_values, relationships), custom tests
- ✓ dbt Documentation, model descriptions, column docs, dbt docs generate and serve
- ✓ Incremental Models, is_incremental(), unique_key, merge strategies, late-arriving data
- ✓ dbt Cloud Deployment, jobs, environments, CI/CD integration, dbt Cloud IDE
How C3RT Helps You Pass the dbt Cert
Adaptive Practice
Questions adapt to your weak areas automatically so every study session on the dbt Cert is time well spent.
Diagnostic Mocks
Full-length mock exams timed to the real dbt Cert format with detailed score breakdowns by topic.
Mistake Bank
Every wrong answer is saved for targeted re-drill. The system resurfaces your mistakes until they stick.
Native on iOS & Mac
Built with SwiftUI, not a web wrapper. Instant load, offline support, hardware-speed rendering.
Sample dbt Cert Practice Questions
Q1.You want to configure a dbt test to only run on models that have changed since the last run, in order to optimize testing time in a large project. Which approach is the most appropriate?
- Use the --select state:modified flag with dbt test to run tests only on changed models.Correct
- Manually specify model names in the test command for changed models.
- Configure tests to run on all models to ensure full coverage every time.
- Set the test severity to warning for unchanged models.
The --select state:modified flag in dbt allows tests to run only on models that have changed since the last run, optimizing test time. Option 1 is manual and error-prone. Option 2 runs tests unnecessarily on unchanged models. Option 3 relates to test severity but does not control execution scope.
Q2.Which naming convention for custom data tests best supports clear identification, easy filtering, and automated reporting in a large dbt project?
- Prefix all tests with the model name followed by the test type, e.g., 'orders_not_null_customer_id'Correct
- Use generic test names like 'test1', 'test2' and document their purpose externally
- Name tests based on the author’s initials and creation date, e.g., 'js_20230615_test'
- Avoid naming conventions and rely solely on dbt’s auto-generated test names
Prefixing tests with the model name and test type creates intuitive, descriptive names that facilitate filtering and reporting. Generic names (B) lack context, causing confusion. Author/date-based names (C) don’t describe test purpose. Relying on auto-generated names (D) limits clarity and governance.
Q3.During log file analysis, you detect repeated warnings about a model's configuration 'materialized' value being ignored. What is the most likely cause and best corrective action?
- The model is being overridden by a higher-priority config; verify config precedence and correct the materialization settingCorrect
- The 'materialized' config is deprecated; remove it from the model file
- The model name conflicts with a seed file; rename the model to resolve conflict
- The dbt version is outdated; upgrade dbt to the latest version to fix config parsing
Config precedence rules mean a materialization setting may be overridden by configs in dbt_project.yml or parent models. Reviewing and correcting config precedence resolves the warning. The materialized config is not deprecated. Model-seed conflicts or dbt version issues usually cause different errors.
dbt Cert Frequently Asked Questions
What does dbt Cert stand for?
dbt Cert stands for dbt Analytics Engineering Certification. It is administered by dbt Labs.
Who administers the dbt Cert?
The dbt Analytics Engineering Certification (dbt Cert) is administered by dbt Labs. For official information, visit the dbt Labs website.
How many questions is the dbt Cert?
The dbt Cert consists of 65 questions. Candidates are given 120 minutes to complete the exam.
How many practice questions does C3RT have for the dbt Cert?
The C3RT app includes 5,500 practice questions for the dbt Cert, along with 1,500 flashcards, 300 concept reels, and 400 concept cards.
What is the passing score for the dbt Cert?
The passing score for the dbt Cert is 65% (approx 43/65), as set by dbt Labs. Scoring methodology and passing standards may be updated periodically. Always verify current requirements with the governing body.
How much does the dbt Cert exam cost?
The dbt Cert exam fee is $200 USD. This fee is set by dbt Labs and may vary by testing centre, region, or membership status. Additional fees for registration or rescheduling may apply.
What is dbt and what does an analytics engineer do?
dbt (data build tool) enables analytics engineers to transform raw source data (from a data warehouse like Snowflake, BigQuery, or Databricks) into clean, reliable analytical models using SQL. Analytics engineers sit between data engineers (who load data) and data analysts (who analyze it), building the tested and documented data models that analysts rely on.
What is the ref() function and why is it important?
ref() is dbt's core function for referencing other models in your project, SELECT * FROM {{ ref('stg_orders') }} instead of hardcoding schema names. ref() handles dependency resolution (dbt builds in the correct order), enables consistent schema naming across environments (dev vs prod), and allows dbt to build the DAG of your data pipeline.
What is an incremental model in dbt?
An incremental model only processes new or changed records rather than rebuilding the entire table on each run. Using the is_incremental() macro and a unique_key, dbt generates MERGE or INSERT SQL to update only the changed records. This is critical for large tables where full rebuilds would be prohibitively expensive. The exam tests when incremental models are appropriate and how to handle late-arriving data.
How does dbt handle testing?
dbt has two types of tests: generic tests (not_null, unique, accepted_values, relationships) defined in YAML schema files, and singular tests (custom SQL queries that return failing rows). Tests are run with dbt test and can be applied to models, sources, and seeds. The exam tests how to configure tests in schema.yml files and how to write custom test macros.
C3RT is a native iOS and macOS exam preparation platform covering the dbt Analytics Engineering Certification (dbt Cert), a IT Certifications certification, administered by dbt Labs. C3RT is not affiliated with or endorsed by dbt Labs. Certification names and trademarks are the property of their respective organisations. For official exam registration, eligibility requirements, and content outlines, visit the dbt Labs official website ↗ .