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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Applications of Foundation Models | 28% | - Explain integration of foundation models into applications - Identify AWS services for generative AI: Amazon Bedrock, Amazon Titan - Recognize tools for building and deploying generative AI solutions - Describe use cases for text, image, video, and code generation |
| Topic 2: Fundamentals of AI and ML | 20% | - Recognize key concepts: data, models, training, inference, evaluation - Define artificial intelligence (AI), machine learning (ML), and deep learning - Describe common ML workflows and lifecycle - Identify types of ML: supervised, unsupervised, reinforcement learning |
| Topic 3: Guidelines for Responsible AI | 14% | - Identify risks and mitigation strategies for AI systems - Define responsible AI principles: fairness, transparency, privacy, safety - Explain bias detection and reduction - Describe ethical and societal impacts of AI |
| Topic 4: Fundamentals of Generative AI | 24% | - Define generative AI and foundation models - Differentiate between generative AI and traditional ML - Describe concepts: prompts, embeddings, fine-tuning, retrieval-augmented generation (RAG) - Explain capabilities and use cases of generative AI |
| Topic 5: Security, Compliance, and Governance for AI Solutions | 14% | - Compliance requirements and regulations - Security controls for AI data and models - Governance frameworks for AI lifecycle - Data protection and privacy in AI workflows |
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질문 # 72
A company wants to use AI to protect its application from threats. The AI solution needs to check if an IP address is from a suspicious source.
정답：D
설명：
The correct answer is C - Develop an anomaly detection system. According to AWS documentation, anomaly detection models are specifically used to identify unusual or suspicious patterns in data, such as unexpected IP access behavior, unusual network traffic, login anomalies, or deviations from normal usage patterns. Amazon SageMaker and Amazon Lookout for Metrics both support anomaly detection capabilities that detect deviations from learned baselines. AWS highlights that anomaly detection is widely used in cybersecurity, fraud detection, intrusion monitoring, and identifying suspicious IP addresses. Speech recognition (A) and NLP named entity recognition (B) do not classify threat behavior. Fraud forecasting (D) focuses on long-term prediction patterns, not real-time anomaly detection. Since identifying malicious IP sources requires spotting activity outside the normal distribution, anomaly detection is the most accurate and AWS-aligned solution.
Referenced AWS Documentation:
* AWS Machine Learning Specialty Guide - Anomaly Detection Use Cases
* Amazon SageMaker Documentation - Random Cut Forest (RCF) for Anomaly Detection
질문 # 73
A research group wants to test different generative AI models to create research papers. The research group has defined a prompt and needs a method to assess the models' output. The research group wants to use a team of scientists to perform the output assessments.
Which solution will meet these requirements?
정답：B
설명：
The correct answer is C because Amazon Bedrock's model evaluation feature allows users to compare outputs from different foundation models using human evaluation or automatic metrics. It enables the creation of structured evaluations where human reviewers (in this case, scientists) can assess model responses based on custom criteria like relevance, coherence, or accuracy.
From AWS documentation:
"Amazon Bedrock provides model evaluation capabilities that support both automatic and human evaluation. You can define custom evaluation prompts and collect assessments from reviewers to compare foundation model outputs for tasks such as summarization, text generation, and more." This solution is ideal for research workflows requiring domain experts to provide feedback on LLM-generated content.
Explanation of other options:
A . Amazon Personalize is used for building recommendation systems, not for evaluating model output.
B . Amazon Rekognition is used for analyzing images and videos (e.g., moderation, facial recognition), not textual output.
D . Amazon Comprehend provides NLP services like sentiment analysis, but sentiment is not sufficient for full quality evaluation of research paper generation.
Referenced AWS AI/ML Documents and Study Guides:
Amazon Bedrock Developer Guide - Model Evaluation Overview
AWS Generative AI Best Practices
AWS ML Specialty Study Guide - Evaluation and Feedback Loops in LLMs
질문 # 74
An AI practitioner trained a custom model on Amazon Bedrock by using a training dataset that contains confidential dat a. The AI practitioner wants to ensure that the custom model does not generate inference responses based on confidential data.
How should the AI practitioner prevent responses based on confidential data?
정답：C
질문 # 75
A company wants to improve multiple ML models.
Select the correct technique from the following list of use cases. Each technique should be selected one time or not at all. (Select THREE.) Few-shot learning Fine-tuning Retrieval Augmented Generation (RAG) Zero-shot learning
정답：
설명：
질문 # 76
A company wants to create an application by using Amazon Bedrock. The company has a limited budget and prefers flexibility without long-term commitment.
Which Amazon Bedrock pricing model meets these requirements?
정답：B
설명：
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.
질문 # 77
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