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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Fundamentals of Generative AI | 24% | - 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) - Define generative AI and foundation models |
| Topic 2: Guidelines for Responsible AI | 14% | - Identify risks and mitigation strategies for AI systems - Describe ethical and societal impacts of AI - Define responsible AI principles: fairness, transparency, privacy, safety - Explain bias detection and reduction |
| Topic 3: Security, Compliance, and Governance for AI Solutions | 14% | - Data protection and privacy in AI workflows - Compliance requirements and regulations - Security controls for AI data and models - Governance frameworks for AI lifecycle |
| Topic 4: Fundamentals of AI and ML | 20% | - Describe common ML workflows and lifecycle - Define artificial intelligence (AI), machine learning (ML), and deep learning - Identify types of ML: supervised, unsupervised, reinforcement learning - Recognize key concepts: data, models, training, inference, evaluation |
| Topic 5: Applications of Foundation Models | 28% | - Recognize tools for building and deploying generative AI solutions - Identify AWS services for generative AI: Amazon Bedrock, Amazon Titan - Describe use cases for text, image, video, and code generation - Explain integration of foundation models into applications |
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NEW QUESTION # 157
An AI practitioner wants to generate more diverse and more creative outputs from a large language model (LLM).
How should the AI practitioner adjust the inference parameter?
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
The temperature parameter controls randomness in model outputs.
AWS generative AI guidance explains:
Higher temperature → more randomness and creativity
Lower temperature → more deterministic and predictable outputs
To increase diversity and creativity, the temperature should be increased.
Why the other options are incorrect:
Lower Top K (B) reduces output diversity.
Response length (C) affects size, not creativity.
Prompt length (D) does not directly control randomness.
AWS AI document references:
Inference Parameters for Foundation Models
Controlling Creativity in LLMs
Text Generation Configuration on AWS
NEW QUESTION # 158
A company is building a mobile app for users who have a visual impairment. The app must be able to hear what users say and provide voice responses.
Which solution will meet these requirements?
Answer: D
Explanation:
The mobile app for users with visual impairment needs to hear user speech and provide voice responses, requiring speech-to-text (speech recognition) and text-to-speech capabilities. Deep learning neural networks are widely used for speech recognition tasks, as they can effectively process and transcribe spoken language.
AWS services like Amazon Transcribe, which uses deep learning for speech recognition, can fulfill this requirement by converting user speech to text, and Amazon Polly can generate voice responses.
Exact Extract from AWS AI Documents:
From the AWS Documentation on Amazon Transcribe:
"Amazon Transcribe uses deep learning neural networks to perform automatic speech recognition (ASR), converting spoken language into text with high accuracy. This is ideal for applications requiring voice input, such as accessibility features for visually impaired users." (Source: Amazon Transcribe Developer Guide, Introduction to Amazon Transcribe) Detailed Explanation:
* Option A: Use a deep learning neural network to perform speech recognition.This is the correct answer. Deep learning neural networks are the foundation of modern speech recognition systems, as used in AWS services like Amazon Transcribe. They enable the app to hear and transcribe user speech, and a service like Amazon Polly can handle voice responses, meeting the requirements.
* Option B: Build ML models to search for patterns in numeric data.This option is irrelevant, as the task involves processing speech (audio data) and generating voice responses, not analyzing numeric data patterns.
* Option C: Use generative AI summarization to generate human-like text.Generative AI summarization focuses on summarizing text, not processing speech orgenerating voice responses. This option does not address the core requirement of speech recognition.
* Option D: Build custom models for image classification and recognition.Image classification and recognition are unrelated to processing speech or generating voice responses, making this option incorrect for an app focused on audio interaction.
References:
Amazon Transcribe Developer Guide: Introduction to Amazon Transcribe (https://docs.aws.amazon.com
/transcribe/latest/dg/what-is.html)
Amazon Polly Developer Guide: Text-to-Speech Overview (https://docs.aws.amazon.com/polly/latest/dg
/what-is.html)
AWS AI Practitioner Learning Path: Module on Speech Recognition and Synthesis
NEW QUESTION # 159
A company is using a pre-trained large language model (LLM) to extract information from documents. The company noticed that a newer LLM from a different provider is available on Amazon Bedrock. The company wants to transition to the new LLM on Amazon Bedrock.
What does the company need to do to transition to the new LLM?
Answer: C
Explanation:
Transitioning to a new large language model (LLM) on Amazon Bedrock typically involves minimal changes when the new model is pre-trained and available as a foundation model. Since the company is moving from one pre-trained LLM to another, the primary task is to ensure compatibility between the new model's input requirements and the existing application. Adjusting the prompt template is often necessary because different LLMs may have varying prompt formats, tokenization methods, or response behaviors, even for similar tasks like document extraction.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"When switching between foundation models in Amazon Bedrock, you may need to adjust the prompt template to align with the new model's expected input format and optimize its performance for your use case. Prompt engineering is critical to ensure the model understands the task and generates accurate outputs." (Source: AWS Bedrock User Guide, Prompt Engineering for Foundation Models) Detailed Option A: Create a new labeled dataset.Creating a new labeled dataset is unnecessary when transitioning to a new pre-trained LLM, as pre-trained models are already trained on large datasets. This option would only be relevant if the company were training a custom model from scratch, which is not the case here.
Option B: Perform feature engineering.Feature engineering is typically associated with traditional machine learning models, not pre-trained LLMs. LLMs process raw text inputs, and transitioning to a new LLM does not require restructuring input features. This option is incorrect.
Option C: Adjust the prompt template.This is the correct approach. Different LLMs may interpret prompts differently due to variations in training data, tokenization, or model architecture. Adjusting the prompt template ensures the new LLM understands the task (e.g., document extraction) and produces the desired output format. AWS documentation emphasizes prompt engineering as a key step when adopting a new foundation model.
Option D: Fine-tune the LLM.Fine-tuning is not required for transitioning to a new pre-trained LLM unless the company needs to customize the model for a highly specific task. Since the question does not indicate a need for customization beyond document extraction (a common LLM capability), fine-tuning is unnecessary.
Reference:
AWS Bedrock User Guide: Prompt Engineering for Foundation Models (https://docs.aws.amazon.com/bedrock/latest/userguide/prompt-engineering.html) AWS AI Practitioner Learning Path: Module on Working with Foundation Models in Amazon Bedrock Amazon Bedrock Developer Guide: Transitioning Between Models (https://docs.aws.amazon.com/bedrock/latest/devguide/)
NEW QUESTION # 160
A company is using the Generative AI Security Scoping Matrix to assess security responsibilities for its solutions. The company has identified four different solution scopes based on the matrix.
Which solution scope gives the company the MOST ownership of security responsibilities?
Answer: D
NEW QUESTION # 161
A company has a team of AI practitioners that builds and maintains AI applications in an AWS account. The company must keep records of the actions that each AI practitioner takes in the AWS account for audit purposes.
Which AWS service will meet these requirements?
Answer: B
Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
AWS CloudTrail records API calls and user activity across AWS services, including:
* Who performed an action
* What action was taken
* When and from where the action occurred
AWS governance guidance identifies CloudTrail as the primary service for audit logging and accountability.
Why the other options are incorrect:
* AWS Config (B) tracks configuration changes, not user actions.
* Audit Manager (C) assesses compliance but does not record individual actions.
* Trusted Advisor (D) provides best-practice recommendations.
AWS AI document references:
* AWS CloudTrail Overview
* Auditing User Activity on AWS
* Governance and Compliance for AI Workloads
NEW QUESTION # 162
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