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| Section | Weight | Objectives |
|---|---|---|
| Fundamentals of Generative AI | 24% | - Explain capabilities and use cases of generative AI - Describe concepts: prompts, embeddings, fine-tuning, retrieval-augmented generation (RAG) - Define generative AI and foundation models - Differentiate between generative AI and traditional ML |
| Security, Compliance, and Governance for AI Solutions | 14% | - Compliance requirements and regulations - Governance frameworks for AI lifecycle - Data protection and privacy in AI workflows - Security controls for AI data and models |
| 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 |
| Fundamentals of AI and ML | 20% | - Identify types of ML: supervised, unsupervised, reinforcement learning - Describe common ML workflows and lifecycle - Recognize key concepts: data, models, training, inference, evaluation - Define artificial intelligence (AI), machine learning (ML), and deep learning |
| Applications of Foundation Models | 28% | - Describe use cases for text, image, video, and code generation - Recognize tools for building and deploying generative AI solutions - Identify AWS services for generative AI: Amazon Bedrock, Amazon Titan - Explain integration of foundation models into applications |
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NEW QUESTION # 52
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 # 53
A hospital wants to use a generative AI solution with speech-to-text functionality to help improve employee skills in dictating clinical notes.
Answer: A
Explanation:
Comprehensive and Detailed
AWS HealthScribe provides speech-to-text and medical documentation generation, specifically designed for healthcare applications.
Amazon Polly is text-to-speech, not speech-to-text.
Amazon Rekognition is computer vision.
Amazon Q Developer is a generative AI assistant for developers, not healthcare.
Reference:
AWS Documentation - AWS HealthScribe
NEW QUESTION # 54
Which scenario describes a potential risk and limitation of prompt engineering In the context of a generative AI model?
Answer: A
NEW QUESTION # 55
A company is training its employees on how to structure prompts for foundation models.
Select the correct prompt engineering technique from the following list for each prompt template. Each prompt engineering technique should be selected onetime. (SelectTHREE.)
* Chain-of-thought reasoning
* Few-shot learning
* Zero-shot learning
Answer:
Explanation:
NEW QUESTION # 56
A company is deploying an AI-powered loan approval system. The company must comply with data governance regulations for AI.
Which solution will meet these requirements?
Answer: B
Explanation:
Implementing data lifecycle management that tracks and manages AI training data is the only option that directly addresses governance, traceability, compliance, and audit requirements throughout an AI/ML lifecycle.
AWS treats governance as a responsible AI dimension involving the incorporation of appropriate practices throughout the AI supply chain, including providers and deployers. AWS also emphasizes appropriate collection, use, management, security, and traceability of data.
A practical AWS example is Amazon SageMaker AI Lineage Tracking. AWS states that SageMaker ML Lineage Tracking "creates and stores information about the steps of a machine learning (ML) workflow from data preparation to model deployment." It enables organizations to track model and dataset lineage and establish governance and audit standards.
For a regulated loan-approval system, an organization should be capable of determining what training data was used, where it originated, how it was processed, which training job consumed it, which model artifact was generated, and how the resulting model progressed toward deployment. AWS lineage entities can associate training datasets with training jobs and subsequent model artifacts, supporting compliance verification and reproducibility.
Option A undermines governance because changing outputs without auditable records reduces accountability and traceability.
Option C is incorrect because performance optimization cannot supersede mandatory regulatory requirements such as approved data residency, retention, and processing controls.
Option D is also incorrect. Synthetic data can sometimes reduce exposure of real personal information, but using synthetic data does not automatically satisfy AI governance requirements. Organizations still need lifecycle controls, documentation, provenance, validation, security, auditing, and regulatory compliance.
Data governance should cover acquisition, storage, processing, access, lineage, retention, deletion, and permitted uses of data throughout the ML lifecycle.
NEW QUESTION # 57
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