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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Applications of Foundation Models | 28% | - Recognize tools for building and deploying generative AI solutions - Describe use cases for text, image, video, and code generation - Identify AWS services for generative AI: Amazon Bedrock, Amazon Titan - Explain integration of foundation models into applications |
| Topic 2: 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 |
| Topic 3: Security, Compliance, and Governance for AI Solutions | 14% | - Governance frameworks for AI lifecycle - Compliance requirements and regulations - Data protection and privacy in AI workflows - Security controls for AI data and models |
| Topic 4: Fundamentals of Generative AI | 24% | - Define generative AI and foundation models - Describe concepts: prompts, embeddings, fine-tuning, retrieval-augmented generation (RAG) - Differentiate between generative AI and traditional ML - Explain capabilities and use cases of generative AI |
| Topic 5: Guidelines for Responsible AI | 14% | - Identify risks and mitigation strategies for AI systems - Explain bias detection and reduction - Describe ethical and societal impacts of AI - Define responsible AI principles: fairness, transparency, privacy, safety |
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NEW QUESTION # 131
A law firm wants to build an AI application by using large language models (LLMs). The application will read legal documents and extract key points from the documents.
Which solution meets these requirements?
Answer: C
Explanation:
A summarization chatbot is ideal for extracting key points from legal documents. Large language models (LLMs) can be used to summarize complex texts, such as legal documents, making them more accessible and understandable.
Option C (Correct): "Develop a summarization chatbot": This is the correct answer because a summarization chatbot uses LLMs to condense and extract key information from text, which is precisely the requirement for reading and summarizing legal documents.
Option A: "Build an automatic named entity recognition system" is incorrect because it focuses on identifying specific entities, not summarizing documents.
Option B: "Create a recommendation engine" is incorrect as it is used to suggest products or content, not summarize text.
Option D: "Develop a multi-language translation system" is incorrect because translation is unrelated to summarizing text.
AWS AI Practitioner Reference:
Using LLMs for Text Summarization on AWS: AWS supports developing summarization tools using its AI services, including Amazon Bedrock.
NEW QUESTION # 132
A retail company wants to build an ML model to recommend products to customers. The company wants to build the model based on responsible practices. Which practice should the company apply when collecting data to decrease model bias?
Answer: D
Explanation:
The retail company wants to build an ML model for product recommendations using responsible practices to decrease model bias. Collecting balanced and diverse data ensures the model does not favor specific groups, reducing bias and promoting fairness, a key responsible AI practice.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"To reduce model bias, it is critical to collect balanced and diverse data that represents various demographics and user groups. This practice ensures fairness and prevents the model from disproportionately favoring certain populations." (Source: AWS AI Practitioner Learning Path, Module on Responsible AI) Detailed Explanation:
* Option A: Use data from only customers who match the demography of the company's overall customer base.Limiting data to a specific demographic may reinforce existing biases, failing to address underrepresented groups and increasing bias.
* Option B: Collect data from customers who have a past purchase history.Focusing only on customers with purchase history may exclude new users, potentially introducing bias, and does not address diversity.
* Option C: Ensure that the data is balanced and collected from a diverse group.This is the correct answer. A balanced and diverse dataset reduces bias by ensuring the model learns from a representative sample, aligning with responsible AI practices.
* Option D: Ensure that the data is from a publicly available dataset.Public datasets may not be diverse or representative of the company's customer base and could introduce unrelated biases, failing to address fairness.
References:
AWS AI Practitioner Learning Path: Module on Responsible AI
Amazon SageMaker Developer Guide: Bias and Fairness in ML (https://docs.aws.amazon.com/sagemaker
/latest/dg/clarify-bias.html)
AWS Documentation: Responsible AI Practices (https://aws.amazon.com/machine-learning/responsible-ai/)
NEW QUESTION # 133
A company is developing its first generative AI application and wants to put a responsible AI policy in place before going to production. The company is concerned with explainability and transparency with model selections for the application.
Which techniques or tools address these issues? (Select TWO.)
Answer: B,C
Explanation:
The verified answers are A. Model evaluation and C. AI model service cards . The question is specifically about explainability and transparency with model selections before a generative AI application goes to production. AWS AI Practitioner guidance identifies model selection as part of the foundation model lifecycle and includes evaluation as a required activity in that lifecycle. AWS also lists model-selection factors such as model type, performance requirements, capabilities, constraints, compliance, modality, latency, model complexity, customization, and input/output length. This means model evaluation directly supports responsible selection because it provides measurable evidence that a model meets the application's business, technical, and governance requirements.
AI model service cards are also correct because AWS describes AI Service Cards as responsible AI documentation that improves transparency. AWS states that AI Service Cards provide information about intended use cases, limitations, responsible AI design choices, and performance optimization best practices for AWS AI services and models. AWS also links these cards to responsible AI areas such as fairness, bias, explainability, robustness, governance, transparency, privacy, and security. That directly addresses the company's concern about transparency when choosing models.
Guardrails is not the best answer here. Guardrails are important for safety controls, denied topics, content filtering, and responsible runtime behavior, but the question focuses on explainability and transparency in model selection, not output filtering. Data encryption is incorrect because encryption protects data confidentiality at rest or in transit; it does not explain model behavior or document model limitations.
Automated reasoning is also not the best match. It can support formal validation of specific policy-based logic, but it is not the primary AWS tool or technique for transparent model selection. Therefore, model evaluation and AI model service cards are the two techniques/tools that best address the stated requirement.
NEW QUESTION # 134
A company is building an AI application to summarize books of varying lengths. During testing, the application fails to summarize some books. Why does the application fail to summarize some books?
Answer: C
Explanation:
Comprehensive and Detailed
Foundation models have a context window (max tokens), which limits the size of the input text (prompt + instructions).
If the input (e.g., a very long book) exceeds this limit, the model cannot process it, causing failure.
Temperature (A) and Top P (C) control randomness, not input size.
Fine-tuning (B) is irrelevant to input truncation failures.
Reference:
AWS Documentation - Amazon Bedrock Model Parameters (context size limits)
NEW QUESTION # 135
A company has multiple datasets that contain historical data. The company wants to use ML technologies to process each dataset.
Select the correct ML technology from the following list for each dataset. Select each ML technology one time or not at all. (Select THREE.) Computer vision Natural language processing (NLP) Reinforcement learning Time series forecasting
Answer:
Explanation:
Dataset 1: A dataset that contains text-based customer reviews # Natural language processing (NLP) NLP is designed for analyzing text (sentiment analysis, text classification, etc.).
Dataset 2: A dataset that contains images of animals labeled with their species names # Computer vision Computer vision models classify or detect objects in images.
Dataset 3: A dataset that contains daily sales volumes for products # Time series forecasting Time series forecasting predicts future values based on historical sequential data (like sales, demand, stock prices).
NEW QUESTION # 136
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