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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 |
| Topic 2: Fundamentals of AI and ML | 20% | - Identify types of ML: supervised, unsupervised, reinforcement learning - Recognize key concepts: data, models, training, inference, evaluation - Define artificial intelligence (AI), machine learning (ML), and deep learning - Describe common ML workflows and lifecycle |
| Topic 3: Applications of Foundation Models | 28% | - Identify AWS services for generative AI: Amazon Bedrock, Amazon Titan - Recognize tools for building and deploying generative AI solutions - Explain integration of foundation models into applications - Describe use cases for text, image, video, and code generation |
| Topic 4: 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 |
| Topic 5: Security, Compliance, and Governance for AI Solutions | 14% | - Security controls for AI data and models - Data protection and privacy in AI workflows - Governance frameworks for AI lifecycle - Compliance requirements and regulations |
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NEW QUESTION # 150
A company wants to use generative AI to increase developer productivity and software development. The company wants to use Amazon Q Developer.
What can Amazon Q Developer do to help the company meet these requirements?
Answer: B
Explanation:
Amazon Q Developer is a tool designed to assist developers in increasing productivity by generating code snippets, managing reference tracking, and handling open-source license tracking. These features help developers by automating parts of the software development process.
Option A (Correct): "Create software snippets, reference tracking, and open-source license tracking": This is the correct answer because these are key features that help developers streamline and automate tasks, thus improving productivity.
Option B: "Run an application without provisioning or managing servers" is incorrect as it refers to AWS Lambda or AWS Fargate, not Amazon Q Developer.
Option C: "Enable voice commands for coding and providing natural language search" is incorrect because this is not a function of Amazon Q Developer.
Option D: "Convert audio files to text documents by using ML models" is incorrect as this refers to Amazon Transcribe, not Amazon Q Developer.
AWS AI Practitioner Reference:
Amazon Q Developer Features: AWS documentation outlines how Amazon Q Developer supports developers by offering features that reduce manual effort and improve efficiency.
NEW QUESTION # 151
Which strategy will determine if a foundation model (FM) effectively meets business objectives?
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
Meeting business objectives requires evaluating whether the model:
* Solves the intended business problem
* Produces actionable and relevant outputs
* Aligns with functional and operational requirements
AWS model selection guidance emphasizes use-case alignment as the primary criterion for business success.
Why the other options are incorrect:
* Benchmarks (A) measure technical performance, not business value.
* Architecture (B) is an implementation detail.
* Resource usage (D) affects cost, not objective fulfillment.
AWS AI document references:
* Selecting Foundation Models for Business Use
* Aligning AI Models with Business Outcomes
* AI Evaluation Beyond Accuracy
NEW QUESTION # 152
A company uses Amazon Bedrock to implement a generative AI assistant on a website. The AI assistant helps customers with product recommendations and purchasing decisions. The company wants to measure the direct impact of the AI assistant on sales performance.
Answer: C
Explanation:
Comprehensive and Detailed
The most direct business KPI for sales performance is conversion rate (percentage of users who purchase after AI assistant interaction).
Number of interactions (B) shows engagement, not sales impact.
Sentiment analysis (C) shows customer satisfaction but not revenue impact.
NLU accuracy (D) is a technical metric, not a business outcome.
Reference:
AWS Generative AI Use Cases - Measuring Business Value
NEW QUESTION # 153
A company wants to collaborate with several research institutes to develop an AI model. The company needs standardized documentation of model version tracking and a record of model development.
Which solution meets these requirements?
Answer: C
Explanation:
Amazon SageMaker Model Cards provide a standardized way to document and track model information, including versions and performance. According to AWS documentation:
Explanation:
Amazon SageMaker Model Cards provide a standardized way to document and track model information, including versions and performance. According to AWS documentation:
"SageMaker Model Cards provide a single source of truth for model information including intended use, training details, evaluation metrics, and ethical considerations to support governance and collaboration."
NEW QUESTION # 154
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:
Explanation:
AWS References:
Amazon Bedrock - Retrieval Augmented Generation (RAG)
AWS Generative AI Guide - Zero-shot & Few-shot learning
NEW QUESTION # 155
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