Passed Exam Today AIF-C01

This scenario describes a classic supervised learning problem. Supervised learning involves training a model on a labeled dataset where the correct output (also called the target variable or ground truth) is already known. In this case, the company has a dataset containing features such as age, cholesterol, and exercise habits, and a label indicating whether a patient has heart disease or not. This matches the definition of a binary classification task, which is a type of supervised learning. According to the AWS Machine Learning Specialty Study Guide, supervised learning is most effective when labels are available for all training examples, and the goal is to map input variables to a known output. This method enables the model to learn patterns and predict the presence or absence of heart disease. By contrast, unsupervised learning is used when labels are not available, and reinforcement learning involves agents learning via rewards and punishments, which is not applicable here. Semi-supervised learning is used when only some data is labeled.

Referenced AWS AI/ML Documents and Study Guides:

AWS Certified Machine Learning Specialty Guide – Supervised vs. Unsupervised Learning

Amazon SageMaker Documentation – Classification Models

Comprehensive and Detailed Explanation From Exact AWS AI documents:

Hallucinations occur when an AI model generates incorrect, fabricated, or misleading outputs that appear plausible but are factually wrong.

AWS generative AI guidance identifies hallucinations as:

A common limitation of generative models

A risk when models generate numerical or factual data

A key reason for validation and human review in critical use cases

Why the other options are incorrect:

Safety (B) relates to harmful or restricted content.

Interpretability (C) refers to understanding how a model makes decisions.

Cost (D) concerns operational expenses.

AWS AI document references:

Generative AI Risks and Limitations

Responsible Use of Foundation Models

Model Validation Best Practices

The correct answer is D because low latency and optimized inference speed are critical for real-time applications. For delivering real-time service quotes, the system must respond in milliseconds or a few seconds at most, making latency a primary concern when choosing the model.

From AWS Bedrock documentation:

"When selecting a foundation model for real-time applications, inference speed and latency are key evaluation metrics to ensure responsive user experiences."

Explanation of other options:

A. Model size affects performance and cost but doesn't directly guarantee low latency.

B. Training data quality is important for accuracy, but it doesn’t address real-time performance requirements.

C. GPU availability matters in infrastructure planning, not in model selection for latency optimization.

Referenced AWS AI/ML Documents and Study Guides:

Amazon Bedrock Model Selection Guide – Real-time Use Case Considerations

AWS ML Specialty Guide – Foundation Model Performance Criteria