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dbt Labs dbt-Analytics-Engineering Exam Overview:
| Certification Vendor: | dbt Labs |
|---|---|
| Exam Name: | dbt Analytics Engineering Certification Exam |
| Exam Number: | dbt-Analytics-Engineering |
| Real Exam Qty: | 65 |
| Exam Price: | USD 200 |
| Exam Format: | Multiple select, Multiple choice |
| Related Certifications: | dbt Analytics Engineering Certification |
| Certificate Validity Period: | 2 years |
| Passing Score: | 80% |
| Exam Duration: | 120 minutes |
| Available Languages: | English |
| Sample Questions: | dbt Labs dbt-Analytics-Engineering Sample Questions |
| Exam Way: | Online proctored exam |
| Pre Condition: | No formal prerequisites, but practical experience with dbt and SQL is recommended |
| Official Syllabus URL: | https://www.getdbt.com/certification |
dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Testing and Documentation | 20% | - dbt docs and DAG visualization - Documentation generation - Schema tests (unique, not_null, accepted_values, relationships) - Custom data tests |
| Data Transformation Techniques | 25% | - Macros and packages - Common table expressions and subqueries - Jinja templating - Refactoring and incremental models |
| Deployment and Orchestration | 15% | - Jobs and scheduling in dbt Cloud - Git version control integration - CI/CD with dbt Cloud - Environments (dev, staging, prod) |
| dbt Fundamentals | 15% | - dbt workflow and best practices - dbt Core vs dbt Cloud - dbt project structure |
| Models | 25% | - Sources and references - Materializations (table, view, ephemeral, incremental) - Writing and managing SQL models - Seeds - Snapshots |
dbt Labs dbt Analytics Engineering Certification Sample Questions:
Question 1
You try updating a column description using its corresponding YAML file. After regenerating the docs, the update doesn't appear. Which of the following might be the cause?
A. Your browser is aggressively caching the documentation site.
B. The column description might need additional configuration with a test for it to render in the docs.
C. There could be a syntax error within the updated description that dbt is silently ignoring.
D. Changes in column descriptions only update the database schema, not the project documentation.
Question 2
Your team decides to use separate databases within the data warehouse for development, staging, and production. Which are the potential benefits of this approach?
A. Simplified testing and deployment processes due to clear separation of environments.
B. All of the above.
C. The ability to fine-tune database configurations and permissions specifically for each environment's purpose.
D. Increased isolation of changes between environments, reducing the risk of unintended consequences in production.
Question 3
You introduce a new data source for a dbt model. Production policies require review and approval of the source's schema before it can be used for reporting. How could you streamline this within your dbt project?
A. Create a pre-hook that automates schema validation and sends the output to the designated team.
B. Run a separate dbt project in production exclusively for source validation, providing access to relevant stakeholders.
C. Use dbfs ephemeral models feature to temporarily load the data for the approval process.
D. Write dbt tests that explicitly fail if the new source schema does not match the approved definition.
Question 4
(Multiple Select)
A. A ref function referencing another model is incorrect.
B. You made a typo when defining the column name in your model.
C. An upstream source table schema was updated without updating the dbt model.
D. Your dbt tests are invalid.
Question 5
Several models perform similar filtering and aggregation on a frequently used source table. What's the most effective way to streamline this process and improve maintainability?
A. Create an ephemeral model to prepare the data in a common format, referenced by downstream models.
B. Create a snapshot to capture the pre-processed state of the source table.
C. Replicate the filtering and aggregation logic directly within each model.
D. Create a table materialization to hold the pre-processed data.
Solutions:
| Question 1 Answer: A,C | Question 2 Answer: B | Question 3 Answer: A,B,C | Question 4 Answer: A,B,C | Question 5 Answer: A |
By Lena
