AIF-C01 Exam Dumps : AWS Certified AI Practitioner Exam

Amazon S3 is the optimal choice for storing and uploading datasets used for machine learning model validation and training. It offers scalable, durable, and secure storage, making it ideal for holding datasets required by Amazon Bedrock for validation purposes.

Option A (Correct): "Amazon S3": This is the correct answer because Amazon S3 is widely used for storing large datasets that are accessed by machine learning models, including those in Amazon Bedrock.

Option B: "Amazon Elastic Block Store (Amazon EBS)" is incorrect because EBS is a block storage service for use with Amazon EC2, not for directly storing datasets for Amazon Bedrock.

Option C: "Amazon Elastic File System (Amazon EFS)" is incorrect as it is primarily used for file storage with shared access by multiple instances.

Option D: "AWS Snowcone" is incorrect because it is a physical device for offline data transfer, not suitable for directly providing data to Amazon Bedrock.

AWS AI Practitioner References:

Storing and Managing Datasets on AWS for Machine Learning: AWS recommends using S3 for storing and managing datasets required for ML model training and validation.

The correct answer is D because Amazon Bedrock provides access to foundation models (FMs) from various providers for generative AI use cases, including text generation. It supports generating content in a consistent tone, voice, or writing style using prompts or few-shot examples.

From AWS documentation:

"Amazon Bedrock allows you to build and scale generative AI applications using foundation models from AI21 Labs, Anthropic, Cohere, Meta, Mistral, Stability AI, and Amazon. These models can generate text with controlled tone and style for applications like branding, content creation, and copywriting."

Explanation of other options:

A. Amazon Comprehend is for natural language understanding, such as sentiment analysis and entity recognition, not generation.

B. Amazon Personalize is for building recommendation systems, not content generation.

C. Amazon Rekognition is for image and video analysis, not text generation.

Referenced AWS AI/ML Documents and Study Guides:

Amazon Bedrock Developer Guide – Generative AI Use Cases

AWS Certified Machine Learning Specialty Guide – Content Generation with FMs

The correct answer is A, as the most effective way to ensure fairness in AI systems is to audit and diversify training data. AWS Responsible AI documentation stresses that bias originates primarily from unbalanced or incomplete datasets. By including samples from all demographic groups, the model learns patterns that generalize fairly across populations. AWS SageMaker Clarify supports data bias detection and offers fairness metrics like demographic parity and equal opportunity difference. Deeper models (option B) or secrecy (option C) do not mitigate bias and may worsen transparency. Static test datasets (option D) fail to capture evolving data distributions. Therefore, ongoing bias reviews and diverse datasets form the foundation of responsible generative AI for financial use cases.

Referenced AWS AI/ML Documents and Study Guides:

AWS Responsible AI Whitepaper – Fairness and Bias Mitigation

Amazon SageMaker Clarify Documentation – Bias Detection