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| Section | Weight | Objectives |
|---|---|---|
| Security, Compliance, and Governance for AI Solutions | 14% | - Security controls for AI data and models - Data protection and privacy in AI workflows - Compliance requirements and regulations - Governance frameworks for AI lifecycle |
| Fundamentals of AI and ML | 20% | - Identify types of ML: supervised, unsupervised, reinforcement learning - Describe common ML workflows and lifecycle - Define artificial intelligence (AI), machine learning (ML), and deep learning - Recognize key concepts: data, models, training, inference, evaluation |
| Fundamentals of Generative AI | 24% | - Differentiate between generative AI and traditional ML - Define generative AI and foundation models - Explain capabilities and use cases of generative AI - Describe concepts: prompts, embeddings, fine-tuning, retrieval-augmented generation (RAG) |
| Guidelines for Responsible AI | 14% | - Define responsible AI principles: fairness, transparency, privacy, safety - Describe ethical and societal impacts of AI - Explain bias detection and reduction - Identify risks and mitigation strategies for AI systems |
| Applications of Foundation Models | 28% | - 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 - Explain integration of foundation models into applications |
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NEW QUESTION # 212
A company has trained a custom foundation model (FM). The company wants to evaluate the toxicity of the FM's outputs by using human reviewers. The company has a team of internal reviewers. The company also wants to include external teams of reviewers to scale operations.
Which AWS service or feature will meet these requirements?
Answer: A
Explanation:
Amazon SageMaker Ground Truth is designed to support human evaluation and labeling workflows using both internal teams and external workforces. AWS documentation states that Ground Truth enables organizations to build high-quality labeled datasets and conduct human reviews using private workforces, vendor-managed workforces, or third-party providers.
In this scenario, the company needs to evaluate toxicity in foundation model outputs, which requires nuanced human judgment. AWS highlights that Ground Truth supports tasks such as content moderation, sentiment analysis, and safety evaluation, making it well suited for assessing harmful or toxic content generated by models.
Ground Truth provides managed tooling for task distribution, reviewer instructions, quality control, and auditability. It allows companies to seamlessly scale operations by combining internal reviewers for sensitive data with external reviewers for higher-volume workloads, while maintaining consistent review standards.
The other options do not meet the requirement. Amazon Bedrock Agents orchestrate interactions with foundation models but do not manage human review workflows. Amazon Comprehend Custom focuses on training NLP models, not evaluating FM outputs. Amazon SageMaker JumpStart provides pretrained models and solutions, not human evaluation pipelines.
AWS positions SageMaker Ground Truth as a core service for human-in-the-loop machine learning and responsible AI, making it the correct choice for toxicity evaluation using both internal and external reviewers.
NEW QUESTION # 213
A company wants to classify human genes into 20 categories based on gene characteristics. The company needs an ML algorithm to document how the inner mechanism of the model affects the output.
Which ML algorithm meets these requirements?
Answer: B
Explanation:
Decision trees are an interpretable machine learning algorithm that clearly documents the decision-making process by showing how each input feature affects the output. This transparency is particularly useful when explaining how the model arrives at a certain decision, making it suitable for classifying genes into categories.
Option A (Correct): "Decision trees": This is the correct answer because decision trees provide a clear and interpretable representation of how input features influence the model's output, making it ideal for understanding the inner mechanisms affecting predictions.
Option B: "Linear regression" is incorrect because it is used for regression tasks, not classification.
Option C: "Logistic regression" is incorrect as it does not provide the same level of interpretability in documenting decision-making processes.
Option D: "Neural networks" is incorrect because they are often considered "black boxes" and do not easily explain how they arrive at their outputs.
AWS AI Practitioner Reference:
Interpretable Machine Learning Models on AWS: AWS supports using interpretable models, such as decision trees, for tasks that require clear documentation of how input data affects output decisions.
NEW QUESTION # 214
A company plans to implement a generative AI (GenAI) tool to create content that is based on trending social media topics.
Which option represents a legal risk of creating content by using this tool?
Answer: B
Explanation:
Copyright infringement is the clearest legal risk among the choices because generative AI systems can create text, images, or other material that may implicate intellectual-property rights if generated output is substantially similar to protected material.
AWS responsible AI documentation explicitly recognizes intellectual-property considerations for generative AI. AWS AI Service Cards discuss protection from third-party claims alleging "IP infringement or misappropriation (including copyright claims)" in connection with generated outputs.
This confirms that copyright and other intellectual-property rights represent concrete legal considerations when organizations deploy generative AI for content generation.
In the scenario, the system generates content based on trending social-media topics. Such applications must therefore consider whether generated material improperly reproduces or closely resembles copyrighted articles, photographs, illustrations, marketing material, or other protected works. Organizations should implement governance processes that address acceptable-use requirements, intellectual-property review, content provenance where appropriate, human review for higher-risk uses, and applicable contractual or legal obligations.
Option B is not a meaningful legal risk as written. Increasing diversity or inclusiveness in generated content is generally a design or responsible-AI objective rather than a category of infringement.
Option C, bias in generated content, is a significant responsible-AI concern and can create reputational, fairness, or even regulatory exposure depending on the application. However, the question asks which listed option directly represents a legal risk associated with generated creative content, and copyright infringement is the precise answer.
Option D can be relevant to the deployment or export of certain technologies in particular jurisdictions, but it does not directly arise from the content-creation behavior described in this scenario.
AWS responsible AI guidance treats governance, fairness, transparency, privacy, safety, and intellectual-property concerns as considerations that organizations should evaluate throughout the AI lifecycle.
Therefore, the legally relevant content-generation risk specified by the available choices is A. Copyright infringement claims.
NEW QUESTION # 215
A company is developing an ML application. The application must automatically group similar customers and products based on their characteristics.
Which ML strategy should the company use to meet these requirements?
Answer: A
Explanation:
The company needs to automatically group similar customers and products based on their characteristics, which is a clustering task. Unsupervised learning is the ML strategy for grouping data without labeled outcomes, making it ideal for this requirement.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"Unsupervised learning is used to identify patterns or groupings in data without labeled outcomes. Common applications include clustering, such as grouping similar customers or products based on their characteristics, using algorithms like K-means or hierarchical clustering." (Source: AWS AI Practitioner Learning Path, Module on Machine Learning Strategies) Detailed Option A: Unsupervised learningThis is the correct answer. Unsupervised learning, specifically clustering, is designed to group similar entities (e.g., customers or products) based on their characteristics without requiring labeled data.
Option B: Supervised learningSupervised learning requires labeled data to train a model for prediction or classification, which is not applicable here since the task involves grouping without predefined labels.
Option C: Reinforcement learningReinforcement learning involves training an agent to make decisions through rewards and penalties, not for grouping data. This option is irrelevant.
Option D: Semi-supervised learningSemi-supervised learning uses a mix of labeled and unlabeled data, but the task here does not involve any labeled data, making unsupervised learning more appropriate.
Reference:
AWS AI Practitioner Learning Path: Module on Machine Learning Strategies Amazon SageMaker Developer Guide: Unsupervised Learning Algorithms (https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html) AWS Documentation: Introduction to Unsupervised Learning (https://aws.amazon.com/machine-learning/)
NEW QUESTION # 216
A company is building a generative Al application and is reviewing foundation models (FMs). The company needs to consider multiple FM characteristics.
Select the correct FM characteristic from the following list for each definition. Each FM characteristic should be selected one time. (Select THREE.) Concurrency Context windows Latency
Answer:
Explanation:
NEW QUESTION # 217
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