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| Section | Weight | Objectives |
|---|
| External Dependencies | 10% | - External sources integration - Using packages - Managing snapshots
|
| Implementing dbt Tests | 10% | - Built-in tests - Custom tests - Test configuration and execution
|
| Developing dbt Models | 20% | - Model optimization
- 1. Performance tuning
- 2. Model contracts
- Model design and structure
- 1. Materialization types
- 2. Incremental models
- 3. Ref and source functions
|
| Managing Data Pipelines | 15% | - Deployment strategies - Pipeline orchestration - CI/CD integration
|
| Leveraging dbt State | 5% | - State-aware operations - State management
|
| Debugging and Error Resolution | 15% | - Debugging techniques - Resolving data quality issues - Identifying modeling errors
|
| Creating and Maintaining Documentation | 10% | - Documentation standards - Descriptions and metadata - Generating documentation
|
| dbt Models Governance | 15% | - Naming conventions and standards - Project organization and structure - Version control integration
|
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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q271-Q276):
NEW QUESTION # 271
You're setting up CI/CD for your dbt project to automate deployments. Which aspects of your testing strategy might need adaptation for a production-focused CI/CD workflow?
- A. Including smoke tests that run in production to verify core functionality after a deployment.
- B. Emphasizing tests with longer run times for thorough checks before deployment.
- C. Prioritizing tests with strict thresholds to avoid false positives from minor data variations in production.
- D. Disabling all tests that rely on creating new tables, to avoid impacting the production database.
Answer: A,C
Explanation:
B ensures the deployed changes actually work in the real environment. C helps maintain CI/CD flow without unnecessary disruptions. A is counterproductive in a time-sensitive CI/CD context. D would overly limit your testing coverage.
NEW QUESTION # 272
You're testing a model containing financial calculations. Your stakeholders require a high degree of accuracy. Which consideration is most important when designing tests for this scenario?
- A. Using floating-point-specific equality checks for calculated values.
- B. Ensuring tests cover a comprehensive range of input scenarios and edge cases.
- C. Understanding acceptable tolerances and writing tests to check values within a defined range rather than exact matches.
- D. Implementing a large number of generic tests to provide basic coverage.
Answer: B,C
Explanation:
B recognizes that financial calculations often have rounding imprecision, necessitating tolerance-based checks. C is crucial as even minor input variations can propagate into significant errors for financial models. A is generally a bad practice; D focuses on quantity, not the specific quality needed here.
NEW QUESTION # 273
You need to roll out a database migration mid-month. It requires changes to some of your dbt model schemas. How would you manage a smooth transition?
- A. Duplicate models, modifying them for the new schema, and switch over in your job configuration.
- B. Deploy model changes alongside database changes, accepting potential errors for a period.
- C. Use feature branches and environment-aware logic in dbt to allow pre- and post-migration versions to coexist temporarily.
- D. Pause all dbt jobs until after the database migration is fully completed.
Answer: C
Explanation:
C allows for gradual rollout and controlled testing. A' would lead to downtime, B potentially too much downtime, and D increases maintenance overhead.
NEW QUESTION # 274
You need a way to document relationships between raw source tables, dbt models, and downstream Bl dashboards. What type of tool would be best suited for this task?
- A. dbt's built-in documentation (dbt docs)
- B. A data profiling tool
- C. A data catalog
- D. A version control system
Answer: C
Explanation:
While dbt docs is valuable for dbt project specifics, data catalogs specialize in the broader, end-to-end lineage information needed in this scenario.
NEW QUESTION # 275
Several of your users report that dashboards built on top of your dbt models are intermittently displaying stale dat a. Which of the following might be a potential cause?
- A. All of the above.
- B. Source freshness checks are configured incorrectly, allowing older data to be processed
- C. Some models have incorrect materializations, causing them to not update when expected
- D. There are dependency issues in your DAG affecting execution order.
Answer: A
Explanation:
Each of these could lead to stale data Start by examining freshness checks, then materializations, and if those are correct, inspect the DAG for execution bottlenecks.
NEW QUESTION # 276
......
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