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dbt-Analytics-Engineering pdf
  • Exam Code: dbt-Analytics-Engineering
  • Exam Name: dbt Analytics Engineering Certification Exam
  • Updated: Sep 18, 2026
  • Q & A: 359 Questions and Answers
  • Convenient, easy to study.
    Printable dbt Labs dbt-Analytics-Engineering PDF Format. It is an electronic file format regardless of the operating system platform.
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  • Exam Code: dbt-Analytics-Engineering
  • Exam Name: dbt Analytics Engineering Certification Exam
  • Updated: Sep 18, 2026
  • Q & A: 359 Questions and Answers
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  • Exam Code: dbt-Analytics-Engineering
  • Exam Name: dbt Analytics Engineering Certification Exam
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  • Q & A: 359 Questions and Answers
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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

SectionWeightObjectives
Deployment and Orchestration15%- Environments (dev, staging, prod)
- Jobs and scheduling in dbt Cloud
- Git version control integration
- CI/CD with dbt Cloud
Data Transformation Techniques25%- Common table expressions and subqueries
- Jinja templating
- Refactoring and incremental models
- Macros and packages
Models25%- Sources and references
- Seeds
- Snapshots
- Materializations (table, view, ephemeral, incremental)
- Writing and managing SQL models
Testing and Documentation20%- Custom data tests
- Documentation generation
- Schema tests (unique, not_null, accepted_values, relationships)
- dbt docs and DAG visualization
dbt Fundamentals15%- dbt workflow and best practices
- dbt Core vs dbt Cloud
- dbt project structure

dbt Labs dbt Analytics Engineering Certification Sample Questions:

Question #1

You're setting up a new dbt project on a recently provisioned data warehouse. What actions are essential before your initial development begins?

  • A. Configuring monitoring and alerting to track data warehouse usage and events related to dbt jobs
  • B. Establishing access controls and user roles specific to the dbt project's requirements.
  • C. Defining and implementing a schema and naming conventions for your dbt models within the data warehouse.
  • D. All of the above
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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Question #2

You're working on a dbt project in a development environment where compute resources are limited. You want to prototype a new model quickly without incurring high materialization overhead. Which materialization would be most suitable for this?

  • A. Incremental
  • B. Table
  • C. Ephemeral
  • D. Snapshot
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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Question #3

Several of your users report that dashboards built on top of your dbt models are intermittently displaying stale dat a. Which of the following might be a potential cause?

  • A. There are dependency issues in your DAG affecting execution order.
  • B. Source freshness checks are configured incorrectly, allowing older data to be processed
  • C. All of the above.
  • D. Some models have incorrect materializations, causing them to not update when expected
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Correct Answer: C  🗳️

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Question #4

(Multiple Select)

  • A. Using dbt run -m to select models using a path pattern.
  • B. Defining a custom source in sources.yml that selects tables from the desired subdirectory.
  • C. Setting up tags strategically and using the -s flag when executing dbt commands.
  • D. Configuring separate dbt projects for each subdirectory and running them independently.
Reveal Solution  Discussion  0

Correct Answer: A,C  🗳️

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Question #5

You need to set up a production-like environment for load testing and performance tuning of dbt models. How might you approach the resource allocation and provisioning of this environment?

  • A. Write dbt macros that artificially increase dataset sizes in development for simulating load.
  • B. Create a CI/CD job that dynamically scales a cloned production environment for the test, then scales it back down.
  • C. Run performance tests directly against production during a designated off-peak period.
  • D. Partner with your data infrastructure team to get guidance based on their experience.
Reveal Solution  Discussion  0

Correct Answer: B,D  🗳️

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