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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Fundamentals of Generative AI | 24% | - Explain capabilities and use cases of generative AI - Define generative AI and foundation models - Differentiate between generative AI and traditional ML - Describe concepts: prompts, embeddings, fine-tuning, retrieval-augmented generation (RAG) |
| Topic 2: 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 |
| Topic 3: Applications of Foundation Models | 28% | - Identify AWS services for generative AI: Amazon Bedrock, Amazon Titan - Explain integration of foundation models into applications - Describe use cases for text, image, video, and code generation - Recognize tools for building and deploying generative AI solutions |
| Topic 4: Security, Compliance, and Governance for AI Solutions | 14% | - Governance frameworks for AI lifecycle - Security controls for AI data and models - Compliance requirements and regulations - Data protection and privacy in AI workflows |
| Topic 5: Guidelines for Responsible AI | 14% | - Explain bias detection and reduction - Identify risks and mitigation strategies for AI systems - Define responsible AI principles: fairness, transparency, privacy, safety - Describe ethical and societal impacts of AI |
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NEW QUESTION # 65
A company wants to create an application to summarize meetings by using meeting audio recordings.
Select and order the correct steps from the following list to create the application. Each step should be selected one time or not at all. (Select and order THREE.)
* Convert meeting audio recordings to meeting text files by using Amazon Polly.
* Convert meeting audio recordings to meeting text files by using Amazon Transcribe.
* Store meeting audio recordings in an Amazon S3 bucket.
* Store meeting audio recordings in an Amazon Elastic Block Store (Amazon EBS) volume.
* Summarize meeting text files by using Amazon Bedrock.
* Summarize meeting text files by using Amazon Lex.
Answer:
Explanation:
Reference:
Amazon Transcribe Developer Guide: Introduction to Amazon Transcribe (https://docs.aws.amazon.com/transcribe/latest/dg/what-is.html) AWS Bedrock User Guide: Text Generation and Summarization (https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html) AWS AI Practitioner Learning Path: Module on Speech-to-Text and Generative AI Amazon S3 User Guide: Storing Data for Processing (https://docs.aws.amazon.com/AmazonS3/latest/userguide/Welcome.html)
NEW QUESTION # 66
An AI company periodically evaluates its systems and processes with the help of independent software vendors (ISVs). The company needs to receive email message notifications when an ISV's compliance reports become available.
Which AWS service can the company use to meet this requirement?
Answer: B
Explanation:
AWS Data Exchange is a service that allows companies to securely exchange data with third parties, such as independent software vendors (ISVs). AWS Data Exchange can be configured to provide notifications, including email notifications, when new datasets or compliance reports become available.
* Option D (Correct): "AWS Data Exchange": This is the correct answer because it enables the company to receive notifications, including email messages, when ISVs' compliance reports are available.
* Option A: "AWS Audit Manager" is incorrect because it focuses on assessing an organization's own compliance, not receiving third-party compliance reports.
* Option B: "AWS Artifact" is incorrect as it provides access to AWS's compliance reports, not ISVs'.
* Option C: "AWS Trusted Advisor" is incorrect as it offers optimization and best practices guidance, not compliance report notifications.
AWS AI Practitioner References:
* AWS Data Exchange Documentation: AWS explains how Data Exchange allows organizations to subscribe to third-party data and receive notifications when updates are available.
NEW QUESTION # 67
A large retail bank wants to develop an ML system to help the risk management team decide on loan allocations for different demographics.
What must the bank do to develop an unbiased ML model?
Answer: B
Explanation:
Class imbalance in a training dataset can cause ML models to favor overrepresented groups, leading to biased predictions. The AWS AI Practitioner guide and SageMaker Clarify documentation emphasize the need to identify and mitigate class imbalance to ensure fairness and unbiased model outcomes.
D is correct: By measuring class imbalance and adapting the training process (e.g., through oversampling, undersampling, or using class weights), organizations can improve fairness and reduce bias across demographic groups.
A (reducing data size) could worsen bias by removing potentially useful diverse data.
B (consistency with historical results) might reinforce existing biases.
C (separate models) is not scalable and can introduce other fairness issues.
"To reduce bias, examine class imbalance in your training data and use techniques to ensure all groups are fairly represented." (Reference: AWS SageMaker Clarify: Mitigating Bias, AWS Responsible AI)
"To reduce bias, examine class imbalance in your training data and use techniques to ensure all groups are fairly represented." (Reference: AWS SageMaker Clarify: Mitigating Bias, AWS Responsible AI)
NEW QUESTION # 68
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
Answer:
Explanation:
AWS References:
Amazon Bedrock - Retrieval Augmented Generation (RAG)
AWS Generative AI Guide - Zero-shot & Few-shot learning
NEW QUESTION # 69
Which AWS service makes foundation models (FMs) available to help users build and scale generative AI applications?
Answer: A
Explanation:
The correct answer is Amazon Bedrock, AWS's fully managed service for building and scaling generative AI applications using foundation models (FMs). Bedrock gives developers access to models from leading providers such as Anthropic (Claude), Meta (Llama), Mistral, Cohere, and Amazon Titan. Users can invoke these models via API without managing infrastructure or model training. According to AWS documentation, Bedrock supports tasks such as text generation, summarization, question answering, image generation, and RAG workflows with minimal setup. It supports both on-demand and provisioned throughput modes and integrates with features like Guardrails, Knowledge Bases, and Agents for secure, enterprise-grade applications. Amazon Q Developer is a generative AI tool for developers, but it doesn't host or scale models.
Amazon Kendra is an intelligent search engine, and Amazon Comprehend is used for NLP tasks like entity extraction-not foundation model access.
Referenced AWS AI/ML Documents and Study Guides:
Amazon Bedrock Developer Guide - Foundation Models and Use Cases
AWS Certified ML Specialty Guide - Generative AI on AWS
NEW QUESTION # 70
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