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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Analytics Engineering Foundations | - Modern data stack concepts (ELT vs ETL)
|
| Topic 2: Deployment and Orchestration | - Running dbt in production
|
| Topic 3: dbt Core Concepts | - Project structure and configuration
|
| Topic 4: Documentation and Lineage | - Data lineage understanding
|
| Topic 5: Testing and Data Quality | - Built-in and custom tests
|
dbt Labs dbt Analytics Engineering Certification Sample Questions:
Question #1
You create tests to validate counts and timestamps against a baseline dataset. The tests fail unexpectedly. What might be possible root causes?
A. The baseline dataset itself has become corrupted
B. Your tests are too strict and need more generous tolerances.
C. There are differences in data types or time zone handling between the model and baseline.
D. Your upstream ETL process has a logic error, leading to incorrect data.
Question #2
A colleague asks, "Every time I make changes to my dbt_project.yml I have to wait for errors during dbt compile. Is there a better way?" What would you suggest?
A. Thoroughly review dbts documentation for a complete list of valid properties to memorize.
B. Have them write dbt schema tests for their configuration files.
C. Manually validate the dbt_project.yml against a fixed template file after each edit
D. Install a YAML linter within their code editor to catch formatting and basic schema errors.
Question #3
(Multiple Select)
A. Testing performance under a simulated heavy load.
B. Catching subtle errors or discrepancies that may not cause immediate functional issues.
C. Ensuring data synchronization after a migration.
D. Validating changes during development before deploying a new dbt model version.
Question #4
Your stakeholders require robust data visualization and dashboarding capabilities beyond what dbt itself offers. Which of the following would be good pairings with dbt to address this need?
A. Spreadsheet software (like Excel or Google Sheets)
B. Version control systems (e.g., Git)
C. Data observability platforms
D. Modern Bl tools (like Tableau, Looker, Power Bl)
Question #5
You're planning your production dbt deployment strategy. When deciding between completely separate development and deployment environments vs. using different schemas within a single data warehouse, what are I key factors to weigh?
A. Your data warehouse's pricing model and how it charges for resource usage.
B. The desired level of isolation and access control granularity.
C. The need to collaborate between developers with varying skill levels.
D. The complexity of your dbt project and the number of models.
Solutions:
| Question #1 Correct Answer: A,B,C,D | Question #2 Correct Answer: D | Question #3 Correct Answer: B,C,D | Question #4 Correct Answer: D | Question #5 Correct Answer: A,B,C |


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