Databricks GenAI Engineer AI Governance Quiz

1. What does 'hallucination' mean in generative AI systems?

When a model generates plausible but false information

When a model runs out of memory

When a model is undertraining

When a model produces identical outputs

2. What should be documented for compliance with generative AI governance?

Model training data, methodology, and performance metrics

Only successful model deployments

Nothing, documentation is optional

Only external audit reports

3. In Databricks, what is Delta Lake's role in governance?

Provides ACID transactions and data quality tracking

Eliminates the need for backups

Increases model training speed only

Removes governance requirements

4. What is the role of stakeholder engagement in AI governance?

To ensure diverse perspectives inform AI development

To avoid accountability

To eliminate transparency

To reduce model quality

5. Which practice helps ensure responsible data use in AI systems?

Data minimization and purpose limitation

Using all available data without restriction

Ignoring user consent

Storing data indefinitely

6. In generative AI, what does 'alignment' refer to?

Ensuring model outputs match human values and intentions

Arranging data in rows

Physical alignment of servers

Synchronizing GPU memory

7. What is the purpose of a model governance policy?

To establish standards for model development and deployment

To eliminate the need for testing

To prevent all AI innovation

To maximize model size

8. Which of the following represents a key ethical concern in generative AI?

Copyright and intellectual property issues

Misuse for generating harmful content

Bias in training data leading to unfair outcomes

All of the above

9. What is the importance of version control in AI governance?

To track model changes and enable rollback if needed

To increase model accuracy

To reduce training time

To eliminate the need for testing

10. In Databricks, how can you monitor model performance in production?

Using Databricks Model Monitoring

Manual daily checks only

Ignoring performance after deployment

Using external third-party tools exclusively

11. What is the primary purpose of AI governance in generative AI systems?

To maximize model accuracy and speed

To ensure responsible, ethical, and compliant AI deployment

To reduce infrastructure costs

To increase model parameter count

12. Which of the following is a compliance requirement for generative AI in many regions?

Disclosure of AI use to end users

Data privacy and protection measures

Regular audits and documentation

All of the above

13. What is 'model explainability' in the context of AI governance?

The ability to understand and interpret model decisions

The speed at which a model makes predictions

The number of parameters in a model

The cost of deploying a model

14. Which governance framework is commonly used for responsible AI?

ISO 27001

NIST AI Risk Management Framework

GDPR only

SOC 2 Type II

15. In generative AI, what does 'prompt injection' refer to?

Injecting new training data into models

Malicious input designed to manipulate model behavior

Optimizing model prompts for speed

Compressing prompts for efficiency

16. What is the role of data lineage in AI governance?

To track the origin and transformation of data used in models

To increase model training speed

To reduce storage requirements

To eliminate the need for monitoring

17. Which technique helps detect and reduce bias in generative AI models?

Fairness audits and bias testing

Increasing model complexity

Using larger datasets only

Removing all categorical features

18. What does 'model drift' refer to in AI governance?

The physical movement of data servers

Degradation of model performance over time due to changing data

The version control system for models

The training process optimization

19. In Databricks, what tool helps track and manage ML model governance?

Databricks Workspace

Databricks Model Registry

Databricks Delta Lake

Databricks SQL Warehouse

20. Which of the following is a key component of responsible AI governance?

Model transparency and interpretability

Bias mitigation and fairness monitoring

Compliance with regulatory frameworks

All of the above

Databricks GenAI Engineer AI Governance Quiz

1. What does 'hallucination' mean in generative AI systems?

2.

What first name or nickname would you like us to use?

2. What should be documented for compliance with generative AI governance?

3. In Databricks, what is Delta Lake's role in governance?

4. What is the role of stakeholder engagement in AI governance?

5. Which practice helps ensure responsible data use in AI systems?

6. In generative AI, what does 'alignment' refer to?

7. What is the purpose of a model governance policy?

8. Which of the following represents a key ethical concern in generative AI?

9. What is the importance of version control in AI governance?

10. In Databricks, how can you monitor model performance in production?

11. What is the primary purpose of AI governance in generative AI systems?

12. Which of the following is a compliance requirement for generative AI in many regions?

13. What is 'model explainability' in the context of AI governance?

14. Which governance framework is commonly used for responsible AI?

15. In generative AI, what does 'prompt injection' refer to?

16. What is the role of data lineage in AI governance?

17. Which technique helps detect and reduce bias in generative AI models?

18. What does 'model drift' refer to in AI governance?

19. In Databricks, what tool helps track and manage ML model governance?

20. Which of the following is a key component of responsible AI governance?