dbt Labs dbt-Analytics-Engineering Exam Flashcards & dbt-Analytics-Engineering Test Duration

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| Section | Weight | Objectives |
|---|
| Leveraging dbt State | 5% | - State-aware operations - State management
|
| Creating and Maintaining Documentation | 10% | - Descriptions and metadata - Documentation standards - Generating documentation
|
| dbt Models Governance | 15% | - Project organization and structure - Naming conventions and standards - Version control integration
|
| Implementing dbt Tests | 10% | - Custom tests - Built-in tests - Test configuration and execution
|
| Developing dbt Models | 20% | - Model design and structure
- 1. Materialization types
- 2. Ref and source functions
- 3. Incremental models
- Model optimization
- 1. Performance tuning
- 2. Model contracts
|
| Debugging and Error Resolution | 15% | - Resolving data quality issues - Debugging techniques - Identifying modeling errors
|
| Managing Data Pipelines | 15% | - Deployment strategies - Pipeline orchestration - CI/CD integration
|
| External Dependencies | 10% | - Managing snapshots - Using packages - External sources integration
|
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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q302-Q307):
NEW QUESTION # 302
You get a warning during dbt run:WARNlNG: Found 100 rows in 'stg_customers' that will become duplicates in the 'dim_customers' target.
- A. Update the stg_customers source definition to include filtering logic to remove duplicate rows.
- B. It's safe to ignore this warning, as de-duplication is handled automatically by dbt.
- C. Increase the memory allocated for the database to handle the large number of duplicates.
- D. Investigate your dim_customers modeling logic to ensure a unique key is correctly used to prevent duplicates.
Answer: D
Explanation:
This typically indicates a logic flaw in your model creating the target table. Modifying the source or increasing memory won't resolve the core issue.
NEW QUESTION # 303
You've been tasked with improving the discoverability of dbt models within your project. Which of the following changes would be the MOST effective in achieving this goal?
- A. Adding a tags list to all models, even if the tags are somewhat generic.
- B. Using custom macros to dynamically generate model descriptions based on metadata.
- C. Writing longer, more verbose descriptions for each column in your models.
- D. Ensuring models have clear and concise descriptions that accurately reflect their purpose.
Answer: D
Explanation:
While the other options might have some value, clear and concise model descriptions provide the most immediate and direct way for users to understand the purpose and content of a model, aiding in discoverability within the dbt project's documentation.
NEW QUESTION # 304
You introduce a custom macro into your project. This is a snippet of your dbt_project.yml:YAML
- A. You must place the macros directory within the root of your dbt project.
- B. The macro's name should only be 'trim', without the 'string_utils' prefix.
- C. The .sql extension on the file within the macros directory is incorrect.
- D. The macro_paths configuration in your dbt_project.yml might be incorrect or missing.
Answer: D
Explanation:
Check your macro file paths, as an incorrect configuration would prevent dbt from finding the macro. The others are likely not the issue but worth double-checking.
NEW QUESTION # 305
You need to test whether the values of a specific column have changed over time. What might be the best approach to implement this kind of test within your dbt project?
- A. Use a snapshot to preserve a point-in-time reference of the column.
- B. Use a custom 'accepted_values' test together with a dbt seed.
- C. Write a dbt macro to create a temporary table with historical data to compare against.
- D. rely on native version history features in your data warehouse (if available).
Answer: A,C,D
Explanation:
A provides a clean comparison point C enables more complex historical analysis. D leverages built-in warehouse features, if you have them.
NEW QUESTION # 306
You've implemented a macro to standardize descriptions across a set of models. After updating the macro and regenerating docs, the changes don't seem to be reflected. What could be the cause?
- A. Your macro implementation has a subtle logic error.
- B. The macro might only be applied to specific model types.
- C. The documentation generator is caching the old version of the macro.
- D. Changes to macros sometimes require a full re-compilation of all models for the updates to take effect in the documentation.
Answer: A,C,D
Explanation:
A: Errors in macro logic can prevent intended results. B: Similar to other content, macros can be cached, so clearing your browser cache might be needed. C: Due to dependency relationships, macro changes might sometimes require re-compilation of affected models to take full effect in the docs.
NEW QUESTION # 307
......
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