Free Amazon Web Services AIF-C01 Practice Exam with Questions & Answers | Set: 11

The company is developing ML applications for various use cases, and the task is to select the correct ML paradigm (supervised or unsupervised learning) for each. Supervised learning involves training a model on labeled data to make predictions, while unsupervised learning identifies patterns or structures in unlabeled data. Each use case aligns with one of these paradigms based on its requirements.

Exact Extract from AWS AI Documents:

From the AWS AI Practitioner Learning Path:

"Supervised learning uses labeled data to train models for tasks like classification (e.g., binary or multi-class classification), where the model predicts a category. Unsupervised learning works with unlabeled data for tasks like clustering (e.g., K-means clustering) or dimensionality reduction, identifying patternsor reducing data complexity without predefined labels."

(Source: AWS AI Practitioner Learning Path, Module on Machine Learning Strategies)

Detailed Explanation:

Binary classification: Supervised learningBinary classification involves predicting one of two classes (e.g., yes/no, spam/not spam) using labeled data, making it a supervised learning task. The model learns from examples where the correct class is provided.

Multi-class classification: Supervised learningMulti-class classification extends binary classification to predict one of multiple classes (e.g., categorizing items into several groups). Like binary classification, it requires labeled data, so it falls under supervised learning.

K-means clustering: Unsupervised learningK-means clustering groups data into clusters based on similarity, without requiring labeled data. This is a classic unsupervised learning task, as the algorithm identifies patterns in the data on its own.

Dimensionality reduction: Unsupervised learningDimensionality reduction (e.g., using techniques like PCA) reduces the number of features in a dataset while preserving important information. It does not require labeled data, making it an unsupervised learning task.

Hotspot Selection Analysis:

The hotspot lists four use cases, each with a dropdown containing "Select...," "Supervised learning," and "Unsupervised learning." The correct selections are:

Binary classification: Supervised learning

Multi-class classification: Supervised learning

K-means clustering: Unsupervised learning

Dimensionality reduction: Unsupervised learning

Each paradigm (supervised and unsupervised learning) is used twice, as the question allows for paradigms to be selected one or more times.

To comply with multiple regulatory frameworks, the company must ensure data protection and threat detection. Data protection involves safeguarding sensitive customer information, while threat detection identifies and mitigates security threats to the application.

Option C (Correct): "Data protection": This is correct because data protection is critical for compliance with privacy and security regulations.

Option B (Correct): "Threat detection": This is correct because detecting and mitigating threats is essential to maintaining the security posture required for regulatory compliance.

Option A: "Auto scaling inference endpoints" is incorrect because auto-scaling does not directly relate to regulatory compliance.

Option D: "Cost optimization" is incorrect because it is focused on managing expenses, not compliance.

Option E: "Loosely coupled microservices" is incorrect because this architectural approach does not directly address compliance requirements.

AWS AI Practitioner References:

AWS Compliance Capabilities: AWS offers services and tools, such as data protection and threat detection, to help companies meet regulatory requirements for security and privacy.

The correct answer is A because temperature controls the randomness of a language model's output. A higher temperature increases diversity by making the model more likely to explore less probable tokens, while a lower temperature results in more deterministic and repetitive outputs.

From AWS documentation:

"The temperature parameter in LLMs adjusts the randomness of generated responses. Higher values (e.g., 0.8–1.0) produce more creative and diverse output, while lower values (e.g., 0.1–0.3) make output more focused and repetitive."

Explanation of other options:

B. Batch size is related to training efficiency, not output diversity.

C. Learning rate affects the training convergence rate, not inference-time output variety.

D. Optimizer type is a training configuration that influences how the model learns during training, not diversity during inference.

Referenced AWS AI/ML Documents and Study Guides:

Amazon Bedrock – Parameter Tuning Guide

AWS Machine Learning Specialty Guide – LLM Inference Parameters

Comprehensive and Detailed Explanation From Exact AWS AI documents:

The temperature parameter controls randomness in model outputs.

AWS generative AI guidance explains:

Higher temperature → more randomness and creativity

Lower temperature → more deterministic and predictable outputs

To increase diversity and creativity, the temperature should be increased.

Why the other options are incorrect:

Lower Top K (B) reduces output diversity.

Response length (C) affects size, not creativity.

Prompt length (D) does not directly control randomness.

AWS AI document references:

Inference Parameters for Foundation Models

Controlling Creativity in LLMs

Text Generation Configuration on AWS