dbt in CI/CD pipelines - Practice Problems & Coding Challenges

Challenge - 5 Problems

💻 Command Output

intermediate

dbt run command output in CI

You run dbt run in a CI pipeline. Which output indicates a successful model build?

dbt

dbt run

A Warning: Some models skipped due to config

B Error: Model 'sales' not found

C Completed successfully: 5 models executed, 0 errors

D SyntaxError: Unexpected token in SQL

💡 Hint

Look for messages indicating no errors and models executed.

A successful dbt run shows the number of models executed with zero errors. Errors or syntax issues indicate failure.

❓ Configuration

intermediate

Configuring dbt test in CI pipeline

Which dbt command configuration is correct to run tests in a CI pipeline?

A dbt compile --run-tests

B dbt run --test

C dbt build --skip-tests

D dbt test --models tag:ci

💡 Hint

Tests are run with the dbt test command.

The correct command to run tests is dbt test. Option D runs tests on models tagged 'ci'.

🔀 Workflow

advanced

dbt CI/CD pipeline step order

What is the correct order of steps in a dbt CI/CD pipeline?

A 1,2,3,4

B 2,1,3,4

C 1,3,2,4

D 3,2,1,4

💡 Hint

Think about loading data first, then building, testing, and documenting.

First load seed data, then build models, test them, and finally generate docs.

❓ Troubleshoot

advanced

dbt test failure in CI pipeline

A dbt test fails in CI with this error: Database Error: relation 'stg_customers' does not exist. What is the most likely cause?

A The model 'stg_customers' was not built before tests ran

B The test SQL syntax is incorrect

C The database connection credentials are wrong

D The dbt version is incompatible with the database

💡 Hint

Check if the model exists before testing.

Tests run on models that must exist. If the model was not built, tests fail with relation not found error.

✅ Best Practice

expert

Best practice for dbt artifacts in CI/CD

Which practice is best for handling dbt artifacts (manifest.json, run_results.json) in a CI/CD pipeline?

A Ignore artifacts to save storage space

B Store artifacts as pipeline artifacts for debugging and documentation

C Delete artifacts immediately after dbt run to avoid clutter

D Only generate artifacts locally, not in CI

💡 Hint

Artifacts help understand pipeline results and failures.

Storing artifacts allows debugging failures and generating docs from CI runs.