Question 1

Which method is recommended for managing risks related to data privacy when deploying ML models in production environments?

Options :

A : Reducing training data size

B : Increasing model complexity

C : Hyperparameter tuning

D : Data anonymization and encryption

Answer: D

Question 2

Which approach best manages the risk of privacy breaches in machine learning systems dealing with sensitive data?

Options :

A : Using more frequent data updates

B : Increasing model complexity

C : Implementing differential privacy mechanisms

D : Employing larger training datasets

Answer: C

Question 3

What strategy should be employed to manage system failures in a high-availability AI architecture?

Options :

A : Implementing automated failover and redundancy mechanisms

B : Minimizing monitoring to focus on development

C : Avoiding the use of backup systems to reduce costs

D : Relying on manual intervention for fault resolution

Answer: A

Question 4

In the context of AI ethics, what is the "black box" problem, and why is it a concern?

Options :

A : The black box problem is the challenge of integrating AI systems with existing legacy systems in an organization.

B : The black box problem occurs when AI models are too complex, leading to inefficient training and inference times.

C : The black box problem is the lack of transparency and interpretability in AI models, making it difficult to understand how decisions are made.

D : The black box problem refers to the difficulty in accessing and using AI technologies due to proprietary constraints.

Answer: C

Question 5

Which AWS service integrates with Amazon SageMaker to provide distributed training capabilities?

Options :

A : Amazon EC2

B : AWS Lambda

C : Amazon Elastic MapReduce

D : AWS Batch

Answer: A

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