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| Section | Objectives |
|---|---|
| Topic 1: Responsible AI and Security | - AI ethics and responsible use - Security, privacy, and governance in AI systems - Bias, fairness, and explainability |
| Topic 2: Fundamentals of Generative AI | - AWS generative AI services overview (e.g., Amazon Bedrock) - Foundation models and prompt engineering basics - Large language models (LLMs) concepts |
| Topic 3: Fundamentals of Artificial Intelligence and Machine Learning | - Common ML workflows and lifecycle - Data fundamentals for AI/ML - Core AI and ML concepts |
| Topic 4: Applications of Foundation Models | - AI-powered assistants and automation - Use cases for generative AI in business - Content generation and summarization |
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NEW QUESTION # 153
Which scenario represents a practical use case for generative AI?
Answer: A
Explanation:
Generative AI is a type of AI that creates new content, such as text, images, or audio, often mimicking human-like outputs. A practical use case for generative AI is employing a chatbot to provide human-like responses to customer queries in real time, as it leverages the ability of large language models (LLMs) to generate natural language responses dynamically.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Generative AI enables applications like chatbots to produce human-like text responses in real time, enhancing customer support by providing natural and contextually relevant answers to user queries." (Source: AWS Bedrock User Guide, Introduction to Generative AI) Detailed Option A: Using an ML model to forecast product demandForecasting product demand typically involves predictive analytics using supervised learning (e.g., regression models), not generative AI, which focuses on creating new content.
Option B: Employing a chatbot to provide human-like responses to customer queries in real timeThis is the correct answer. Generative AI, particularly LLMs, is commonly used to power chatbots that generate human-like responses, making this a practical use case.
Option C: Using an analytics dashboard to track website traffic and user behaviorAn analytics dashboard involves data visualization and analysis, not generative AI, which is about creating new content.
Option D: Implementing a rule-based recommendation engine to suggest products to customersA rule-based recommendation engine relies on predefined rules, not generative AI. Generative AI could be used for more dynamic recommendations, but this scenario does not describe such a case.
Reference:
AWS Bedrock User Guide: Introduction to Generative AI (https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html) AWS AI Practitioner Learning Path: Module on Generative AI Applications AWS Documentation: Generative AI Use Cases (https://aws.amazon.com/generative-ai/)
NEW QUESTION # 154
An AI practitioner has prepared a dataset for training models in Amazon SageMaker AI. The AI practitioner wants to share the dataset within the company so that future employees can discover and reuse the dataset.
Which solution will meet these requirements?
Answer: D
Explanation:
Comprehensive and Detailed Explanation (AWS AI documents):
AWS recommends Amazon SageMaker Feature Store as a centralized, managed repository for storing, discovering, and reusing features and datasets across teams and projects. Feature Store enables:
* Feature discovery through searchable metadata
* Reusability of curated datasets and features by future practitioners
* Consistency between training and inference datasets
* Governance and lineage, supporting responsible AI practices
Why the other options are incorrect:
* A is intended for retrieval-augmented generation, not dataset sharing.
* B does not provide discoverability, governance, or long-term reuse.
* D is designed for sharing data with external subscribers, not internal ML teams.
AWS AI Study Guide References:
* Amazon SageMaker Feature Store overview
* AWS best practices for feature management and reuse
NEW QUESTION # 155
Which option is a use case for generative AI models?
Answer: A
Explanation:
Generative AI models are used to create new content based on existing data. One common use case is generating photorealistic images from text descriptions, which is particularly useful in digital marketing, where visual content is key to engaging potential customers.
Option B (Correct): "Creating photorealistic images from text descriptions for digital marketing": This is the correct answer because generative AI models, like those offered by Amazon Bedrock, can create images based on text descriptions, making them highly valuable for generating marketing materials.
Option A: "Improving network security by using intrusion detection systems" is incorrect because this is a use case for traditional machine learning models, not generative AI.
Option C: "Enhancing database performance by using optimized indexing" is incorrect as it is unrelated to generative AI.
Option D: "Analyzing financial data to forecast stock market trends" is incorrect because it typically involves predictive modeling rather than generative AI.
AWS AI Practitioner Reference:
Use Cases for Generative AI Models on AWS: AWS highlights the use of generative AI for creative content generation, including image creation, text generation, and more, which is suited for digital marketing applications.
NEW QUESTION # 156
A digital devices company wants to predict customer demand for memory hardware. The company does not have coding experience or knowledge of ML algorithms and needs to develop a data-driven predictive model. The company needs to perform analysis on internal data and external data.
Which solution will meet these requirements?
Answer: C
Explanation:
Amazon SageMaker Canvas is a visual, no-code machine learning interface that allows users to build machine learning models without having any coding experience or knowledge of machine learning algorithms. It enables users to analyze internal and external data, and make predictions using a guided interface.
Option D (Correct): "Import the data into Amazon SageMaker Canvas. Build ML models and demand forecast predictions by selecting the values in the data from SageMaker Canvas": This is the correct answer because SageMaker Canvas is designed for users without coding experience, providing a visual interface to build predictive models with ease.
Option A: "Store the data in Amazon S3 and use SageMaker built-in algorithms" is incorrect because it requires coding knowledge to interact with SageMaker's built-in algorithms.
Option B: "Import the data into Amazon SageMaker Data Wrangler" is incorrect. Data Wrangler is primarily for data preparation and not directly focused on creating ML models without coding.
Option C: "Use Amazon Personalize Trending-Now recipe" is incorrect as Amazon Personalize is for building recommendation systems, not for general demand forecasting.
AWS AI Practitioner Reference:
Amazon SageMaker Canvas Overview: AWS documentation emphasizes Canvas as a no-code solution for building machine learning models, suitable for business analysts and users with no coding experience.
NEW QUESTION # 157
A hospital is developing an AI system to assist doctors in diagnosing diseases based on patient records and medical images. To comply with regulations, the sensitive patient data must not leave the country the data is located in. Which data governance strategy will ensure compliance and protect patient privacy?
Answer: D
Explanation:
* Data residency ensures data is stored and processed within specific geographic or jurisdictional boundaries, meeting compliance requirements like HIPAA or GDPR.
* Data quality refers to accuracy and consistency of data.
* Data discoverability is about cataloging and searching datasets.
* Data enrichment enhances datasets with additional external data.
# Reference:
AWS Data Residency Guide
NEW QUESTION # 158
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