Try and practice the latest dbt Labs : dbt-Analytics-Engineering real questions & answers

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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: dbt Models Governance15%- Version control integration
- Project organization and structure
- Naming conventions and standards
Topic 2: Debugging and Error Resolution15%- Identifying modeling errors
- Debugging techniques
- Resolving data quality issues
Topic 3: Managing Data Pipelines15%- Deployment strategies
- CI/CD integration
- Pipeline orchestration
Topic 4: Developing dbt Models20%- Model optimization
  • 1. Model contracts
  • 2. Performance tuning
- Model design and structure
  • 1. Incremental models
  • 2. Ref and source functions
  • 3. Materialization types
Topic 5: Leveraging dbt State5%- State-aware operations
- State management
Topic 6: Creating and Maintaining Documentation10%- Generating documentation
- Documentation standards
- Descriptions and metadata
Topic 7: Implementing dbt Tests10%- Built-in tests
- Custom tests
- Test configuration and execution
Topic 8: External Dependencies10%- Managing snapshots
- Using packages
- External sources integration

dbt Labs dbt Analytics Engineering Certification Sample Questions:

Question #1

You're onboarding a new dbt project to an existing production database. You find tables that might conflict with naming conventions used by dbt. What's a proactive way to handle this?

  • A. Update your dbt models to use aliases that avoid the name collisions.
  • B. Add configuration settings to your database connection profile instructing dbt to use a specific schema prefix.
  • C. Customize the schema property in your dbt_project.yml to namespace dbt-generated objects.
  • D. Manually rename the conflicting tables in the database prior to running any dbt jobs.
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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Question #2

You discover a downstream model, dim products, references an outdated raw dataset, raw_product_archive, intended for historical analysis and not active products. What's the most appropriate corrective action?

  • A. Leave it as-is since it's not actively causing any errors currently.
  • B. Update the dim_products model to reference the active product dataset.
  • C. Add a schema test to dim_products to catch potential errors.
  • D. Delete the raw_product_archive source, so it can't be used accidentally.
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Correct Answer: B  🗳️

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Question #3

You need to run a dbt job that performs a long-running data cleansing operation. The job occasionally times out. Which of the following are potential solutions?

  • A. Use a dbt macro to retry the cleansing operation with a backoff strategy within the model itself-
  • B. Schedule the job to run concurrently with other dbt models to maximize resource usage-
  • C. Increase the timeout setting in your CI/CD tool or dbt job configuration.
  • D. Break the cleansing logic into multiple models, chaining them with dependencies-
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Correct Answer: A,C,D  🗳️

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Question #4

Which of the following scenarios would typically warrant the use of production data in a development environment?

  • A. Development environments should generally avoid using real production data to minimize risks.
  • B. Training new members of your team on writing SQL and basic dbt models.
  • C. Testing changes to a critical business reporting model for accuracy before deployment.
  • D. Debugging a logic error that only manifests itself under the exact conditions of real-world production data.
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Correct Answer: A  🗳️

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Question #5

Which two are true for a dbt retry command?
Choose 2 options.

  • A. It retries the previous command if it is not a syntax error in a model.
  • B. It reads a manifest.json file to identify the models and tests that failed in the last run.
  • C. It reruns all nodes in your previous invocation statement.
  • D. It picks up from the error without running all of the upstream dependencies.
  • E. It reuses selectors from the previous command.
Reveal Solution  Discussion  0

Correct Answer: A,B  🗳️

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