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| Section | Weight | Objectives |
|---|---|---|
| Applications of Foundation Models | 28% | - 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 - Identify AWS services for generative AI: Amazon Bedrock, Amazon Titan |
| Fundamentals of AI and ML | 20% | - Recognize key concepts: data, models, training, inference, evaluation - Identify types of ML: supervised, unsupervised, reinforcement learning - Describe common ML workflows and lifecycle - Define artificial intelligence (AI), machine learning (ML), and deep learning |
| Security, Compliance, and Governance for AI Solutions | 14% | - Compliance requirements and regulations - Security controls for AI data and models - Data protection and privacy in AI workflows - Governance frameworks for AI lifecycle |
| Guidelines for Responsible AI | 14% | - Describe ethical and societal impacts of AI - Explain bias detection and reduction - Identify risks and mitigation strategies for AI systems - Define responsible AI principles: fairness, transparency, privacy, safety |
| Fundamentals of Generative AI | 24% | - Define generative AI and foundation models - Explain capabilities and use cases of generative AI - Differentiate between generative AI and traditional ML - Describe concepts: prompts, embeddings, fine-tuning, retrieval-augmented generation (RAG) |
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NEW QUESTION # 238
A company is exploring Amazon Nova models in Amazon Bedrock. The company needs a multimodal model that supports multiple languages.
Answer: D
Explanation:
Comprehensive and Detailed
Amazon Nova Pro is a multimodal foundation model in Amazon Bedrock that supports text, images, and multiple languages.
Nova Lite is optimized for lightweight, faster inference at lower cost.
Nova Canvas is a creative tool for visual design.
Nova Reel is optimized for video-related use cases.
Reference:
AWS Documentation - Amazon Nova Models
NEW QUESTION # 239
A company needs to apply numerical transformations to a set of images to transpose and rotate the images.
Answer: D
Explanation:
The correct answer is B. AWS Lambda can efficiently perform image processing and transformations, such as rotating, resizing, or transposing, in a serverless and event-driven manner. According to AWS documentation, Lambda functions can trigger automatically when new images are uploaded to Amazon S3, perform necessary transformations using libraries like Pillow or OpenCV, and store the processed outputs. This approach minimizes infrastructure management and scales automatically. Deep neural networks (option A) are excessive for simple transformations, while LLMs (option C) and Glue Data Quality (option D) are unrelated-LLMs handle text and Glue is for tabular data validation. Lambda is the AWS-recommended service for lightweight, automated image preprocessing tasks.
Referenced AWS AI/ML Documents and Study Guides:
AWS Lambda Developer Guide - Image Processing Use Cases
AWS ML Specialty Guide - Preprocessing and Automation
NEW QUESTION # 240
A hospital developed an AI system to provide personalized treatment recommendations for patients. The AI system must provide the rationale behind the recommendations and make the insights accessible to doctors and patients.
Answer: A
Explanation:
The correct answer is A - Explainability. According to AWS Responsible AI documentation, explainability refers to an AI system's ability to clearly communicate why it produced a given result. In healthcare, clinical decision support systems must provide traceable, understandable reasoning, especially when generating treatment recommendations. AWS highlights explainability as critical for high-impact domains such as medicine because doctors and patients must trust and understand the basis for AI-driven decisions. Tools like Amazon SageMaker Clarify support feature attribution, helping clinicians understand which patient factors (e.g., age, symptoms, lab values) influenced a recommendation. Privacy and security (B) protect data but do not provide rationale. Fairness (C) ensures equitable treatment across demographics but does not explain decisions. Data governance (D) focuses on handling and controlling data, not model decision transparency. Explainability is the AWS principle that ensures clinical users can interpret, validate, and rely on AI-generated recommendations for patient care.
Referenced AWS Documentation:
AWS Responsible AI Whitepaper - Explainability
Amazon SageMaker Clarify - Feature Attribution and Model Insights
NEW QUESTION # 241
A company is developing a new image classification model by using a dataset of photos. The dataset must follow the AWS principles of responsible AI.
Which characteristics should the dataset have to meet this requirement?
Answer: B
Explanation:
Comprehensive and Detailed Explanation (AWS AI documents):
AWS Responsible AI principles stress the importance of fairness, robustness, and accountability, which begin with the quality of the data used to train models. A responsible dataset should:
Be diverse, representing different populations, environments, and conditions to reduce bias Be sourced from reputable and appropriate sources, ensuring data quality and ethical use Have balanced categories, preventing the model from favoring one class over others and reducing the risk of discriminatory outcomes These characteristics help ensure that the resulting image classification model behaves fairly, performs reliably across groups, and aligns with AWS Responsible AI best practices.
Why the other options are incorrect:
B focuses on dataset size rather than quality, balance, or representativeness.
C increases the risk of bias and poor generalization.
D limits diversity and does not inherently ensure fairness or accountability.
AWS AI Study Guide Reference:
AWS Responsible AI principles: fairness and data quality
AWS guidance on dataset selection and preparation for responsible ML
NEW QUESTION # 242
A financial company uses a generative AI model to assign credit limits to new customers. The company wants to make the decision-making process of the model more transparent to its customers.
Answer: C
Explanation:
* Explainable AI (XAI) techniques such as SHAP (SHapley values) or feature attribution provide transparency by showing which input factors influenced decisions.
* A is not scalable for complex use cases.
* C does not guarantee real interpretability.
* D ignores the regulatory need for explainability.
# Reference:
AWS SageMaker Clarify - Explainable AI
NEW QUESTION # 243
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