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| Section | Weight | Objectives |
|---|
| Topic 1: Deployment and Orchestration | 15% | - Environments (dev, staging, prod) - Jobs and scheduling in dbt Cloud - Git version control integration - CI/CD with dbt Cloud
|
| Topic 2: Data Transformation Techniques | 25% | - Common table expressions and subqueries - Refactoring and incremental models - Jinja templating - Macros and packages
|
| Topic 3: Models | 25% | - Materializations (table, view, ephemeral, incremental) - Writing and managing SQL models - Sources and references - Seeds - Snapshots
|
| Topic 4: Testing and Documentation | 20% | - Documentation generation - dbt docs and DAG visualization - Custom data tests - Schema tests (unique, not_null, accepted_values, relationships)
|
| Topic 5: dbt Fundamentals | 15% | - dbt project structure - dbt workflow and best practices - dbt Core vs dbt Cloud
|
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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q41-Q46):
NEW QUESTION # 41
You assume two columns of type 'numeric' will always align in terms of precision and scale (number of decimal places). What's a key way to design your models and tests to be resilient even if this assumption changes?
- A. Always store all numeric values as strings to prevent these issues.
- B. Avoid relying on exact equality when comparing values from these columns.
- C. Write tests to compare the column metadata rather than their contents.
- D. Use explicit casting functions to enforce matching precision during calculations.
Answer: B,D
Explanation:
These strategies allow for flexibility with numeric comparisons, making your models less sensitive to changes in precision. C is a good idea but not a full solution. D is generally bad practice.
NEW QUESTION # 42
You've added new tests to a dbt model but are unsure how to determine the test coverage. What's a practical first step?
- A. Review the compiled SQL files generated by dbt.
- B. Employ a code coverage tool specifically designed for SQL.
- C. Execute dbt test -select failing to identify any currently failing tests.
- D. Examine the output of dbt docs generate and dbt docs serve.
Answer: D
Explanation:
dbt docs provides visual indicators of which models have tests, aiding in identifying gaps in your coverage strategy
NEW QUESTION # 43
You've added new tests but notice they aren't failing even though you've intentionally introduced errors. What's a LIKELY cause?
- A. Your tests have incorrect severity thresholds.
- B. You haven't compiled your project since writing the new tests.
- C. Your dbt project has multiple targets configured.
- D. The tests are defined in seeds.csv files.
Answer: B
Explanation:
Compilation is necessary for new tests to be recognized. Others are less likely, though severity thresholds could be a factor if misconfigured.
NEW QUESTION # 44
You want to display the runtime metrics (e.g., execution time) of each model directly on the DAG node. Which challenges might you face when implementing this?
- A. Retrieving runtime metrics for each historical run might not be natively supported by dbt
- B. All of the above
- C. dbt documentation is primarily static; it might not be able to render dynamic metrics that change with each execution.
- D. Adding too much detailed information directly to the DAG could lead to visual clutter and make it overwhelming.
Answer: B
Explanation:
A: You might need to generate static summaries or find workarounds to display dynamic metrics. B. Collecting runtime metrics across all historical runs might require additional logging or integration with dbt artifacts (manifestjson, run_resultsjson)- C: The DAG visualization needs a balance between information and readability.
NEW QUESTION # 45
You have complex dbt models involving incremental logic based on a timestamp column. To debug a production issue, you want to temporarily force a full rebuild of a specific incremental model. Which dbt command-line options could help?
- A. Write a macro within the model to switch behavior based on an environment variable.
- B. Use dbt artifacts or the compilation context to access the current run's timestamp and override model logic.
- C. Add the -full-refresh flag to the dbt run command.
- D. Manually update the timestamp column in the incremental model's output table.
Answer: B,C
Explanation:
A overrides the incremental setting project-wide. C allows the model to be 'tricked" into a full refresh. B is valid, but more work. D is risky and violates dbts workflow.
NEW QUESTION # 46
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