Amazon AIF-C01 Exam Dumps

Class imbalance in a training dataset can cause ML models to favor overrepresented groups, leading to biased predictions. The AWS AI Practitioner guide and SageMaker Clarify documentation emphasize the need to identify and mitigate class imbalance to ensure fairness and unbiased model outcomes.

D is correct: By measuring class imbalance and adapting the training process (e.g., through oversampling, undersampling, or using class weights), organizations can improve fairness and reduce bias across demographic groups.

A (reducing data size) could worsen bias by removing potentially useful diverse data.

B (consistency with historical results) might reinforce existing biases.

C (separate models) is not scalable and can introduce other fairness issues.

''To reduce bias, examine class imbalance in your training data and use techniques to ensure all groups are fairly represented.''

(Reference: AWS SageMaker Clarify: Mitigating Bias, AWS Responsible AI)

Amazon Bedrock offers an on-demand pricing model that provides flexibility without long-term commitments. This model allows companies to pay only for the resources they use, which is ideal for a limited budget and offers flexibility.

Option A (Correct): 'On-Demand': This is the correct answer because on-demand pricing allows the company to use Amazon Bedrock without any long-term commitments and to manage costs according to their budget.

Option B: 'Model customization' is a feature, not a pricing model.

Option C: 'Provisioned Throughput' involves reserving capacity ahead of time, which might not offer the desired flexibility and could lead to higher costs if the capacity is not fully used.

Option D: 'Spot Instance' is a pricing model for EC2 instances and does not apply to Amazon Bedrock.

AWS AI Practitioner Reference:

AWS Pricing Models for Flexibility: On-demand pricing is a key AWS model for services that require flexibility and no long-term commitment, ensuring cost-effectiveness for projects with variable usage patterns.

Comprehensive and Detailed

When a model performs well on training data but poorly on evaluation/test data, it indicates overfitting.

Overfitting: The model memorizes the training data patterns instead of generalizing.

Underfitting (A) means the model performs poorly on both training and test data.

Bias (C) refers to systemic errors in predictions, not this training/test mismatch.

Prompt engineering (B) applies to generative AI, not general ML training models.

Reference:

AWS ML Glossary -- Overfitting and Underfitting

Comprehensive and Detailed

Summarization is a natural language processing (NLP) task that condenses long documents into concise, meaningful summaries while retaining the key information.

Regression predicts numerical values.

Clustering groups similar items.

Classification assigns data into predefined categories.

Reference:

AWS NLP Use Cases -- Summarization

Comprehensive and Detailed Explanation from AWS AI Documents:

Amazon Bedrock ensures data privacy and security by not sharing customer inputs or outputs with third-party model providers.

The models are accessed via Bedrock's API isolation layer, meaning that model providers do not see your data.

Customer data is not used for training or improving foundation models unless customers explicitly opt in.

From AWS Docs:

''Amazon Bedrock does not share your inputs and outputs with third-party model providers. Your data remains private, and is not used to improve the foundation models.''

This ensures full data privacy, especially for sensitive use cases like confidential documents.

Reference:

AWS Documentation -- Data privacy in Amazon Bedrock