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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Models | 25% | - Sources and references - Seeds - Materializations (table, view, ephemeral, incremental) - Snapshots - Writing and managing SQL models |
| dbt Fundamentals | 15% | - dbt workflow and best practices - dbt project structure - dbt Core vs dbt Cloud |
| Data Transformation Techniques | 25% | - Macros and packages - Jinja templating - Common table expressions and subqueries - Refactoring and incremental models |
| Testing and Documentation | 20% | - Documentation generation - Schema tests (unique, not_null, accepted_values, relationships) - Custom data tests - dbt docs and DAG visualization |
| Deployment and Orchestration | 15% | - CI/CD with dbt Cloud - Git version control integration - Environments (dev, staging, prod) - Jobs and scheduling in dbt Cloud |
dbt Labs dbt Analytics Engineering Certification Sample Questions:
1. While onboarding new data sources to your dbt project, you discover some have unexpectedly high null rates in key columns. How would you integrate tests for this into your dbt workflow?
A) Designate some nulls as acceptable by extending the not_null test with the OR condition.
B) Write tests within the initial staging models that calculate and check the null percentage.
C) Create not_null tests on the raw source tables in a separate dbt project dedicated to source validation.
D) Include a data quality monitoring step in your upstream ETL process before data reaches dbt
2. During development, you frequently used temporary print statements for debugging. Which of the following is the best practice before opening a pull request?
A) Consolidate the print statements into a separate debugging function that can be toggled on and off
B) Leave the print statements for future reference, in case similar debugging is needed-
C) Comment out the print statements but leave them in the code.
D) Remove the print statements entirely from your commits-
3. You have strict data retention policies. Which factors might influence how data is managed differently across your production, development, and raw data environments?
A) Raw data might be archived or deleted following a shorter retention period than production data
B) All of the above.
C) Development data might have less stringent retention requirements, allowing for more frequent deletion-
D) Regulatory requirements could mandate that production data be retained for longer periods.
4. You've made several commits to your feature branch. Before merging into the main branch, you want to consolidate the changes into a single, clear commit. What Git technique would help achieve this?
A) Use git rebase -i (interactive rebase) to squash commits together.
B) Manually copy your files into a new branch, commit, and issue a pull request.
C) git cherry-pick specific commits from your branch and reapply them.
D) Run git rebase main followed by git commit -amend.
5. You're setting up a new dbt project on a recently provisioned data warehouse. What actions are essential before your initial development begins?
A) All of the above
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) Configuring monitoring and alerting to track data warehouse usage and events related to dbt jobs
Solutions:
| Question # 1 Answer: B,D | Question # 2 Answer: D | Question # 3 Answer: B | Question # 4 Answer: A | Question # 5 Answer: A |
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