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| Section | Objectives |
|---|---|
| Applications of Foundation Models | - Use cases for generative AI in business - Content generation and summarization - AI-powered assistants and automation |
| Responsible AI and Security | - Bias, fairness, and explainability - Security, privacy, and governance in AI systems - AI ethics and responsible use |
| Fundamentals of Artificial Intelligence and Machine Learning | - Core AI and ML concepts - Data fundamentals for AI/ML - Common ML workflows and lifecycle |
| Fundamentals of Generative AI | - AWS generative AI services overview (e.g., Amazon Bedrock) - Large language models (LLMs) concepts - Foundation models and prompt engineering basics |
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NEW QUESTION # 354
A company has multiple datasets that contain historical data. The company wants to use ML technologies to process each dataset.
Select the correct ML technology from the following list for each dataset. Select each ML technology one time or not at all. (Select THREE.) Computer vision Natural language processing (NLP) Reinforcement learning Time series forecasting
Answer:
Explanation:
Explanation:
Dataset 1: A dataset that contains text-based customer reviews # Natural language processing (NLP) NLP is designed for analyzing text (sentiment analysis, text classification, etc.).
Dataset 2: A dataset that contains images of animals labeled with their species names # Computer vision Computer vision models classify or detect objects in images.
Dataset 3: A dataset that contains daily sales volumes for products # Time series forecasting Time series forecasting predicts future values based on historical sequential data (like sales, demand, stock prices).
NEW QUESTION # 355
A company wants to fine-tune an ML model that is hosted on Amazon Bedrock. The company wants to use its own sensitive data that is stored in private databases in a VPC. The data needs to stay within the company's private network.
Which solution will meet these requirements?
Answer: A
Explanation:
The company wants to fine-tune an ML model on Amazon Bedrock using sensitive data stored in private databases within a VPC, ensuring the data remains within its private network. AWS PrivateLink provides a secure, private connection between a VPC and AWS services like Amazon Bedrock, allowing data to stay within the company's network without traversing the public internet. This meets the requirement for maintaining data privacy during fine-tuning.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"AWS PrivateLink enables you to securely connect your VPC to Amazon Bedrock without exposing data to the public internet. This is particularly useful for fine-tuning models with sensitive data, as it ensures that data remains within your private network." (Source: AWS Bedrock User Guide, Security and Networking) Detailed Option A: Restrict access to Amazon Bedrock by using an AWS Identity and Access Management (IAM) service role.While IAM service roles control access to Amazon Bedrock, they do not address the requirement of keeping data within the private network during data transfer. This option is insufficient.
Option B: Restrict access to Amazon Bedrock by using an AWS Identity and Access Management (IAM) resource policy.IAM resource policies define permissions for Bedrock resources but do not ensure that data stays within the private network. This option is incorrect.
Option C: Use AWS PrivateLink to connect the VPC and Amazon Bedrock.This is the correct answer. AWS PrivateLink creates a secure, private connection between the VPC and Amazon Bedrock, ensuring that sensitive data does not leave the private network during fine-tuning, as required.
Option D: Use AWS Key Management Service (AWS KMS) keys to encrypt the data.While AWS KMS can encrypt data, encryption alone does not guarantee that data remains within the private network during transfer. This option does not fully meet the requirement.
Reference:
AWS Bedrock User Guide: Security and Networking (https://docs.aws.amazon.com/bedrock/latest/userguide/security.html) AWS Documentation: AWS PrivateLink (https://aws.amazon.com/privatelink/) AWS AI Practitioner Learning Path: Module on Security and Networking for AI/ML Services
NEW QUESTION # 356
A company is using few-shot prompting on a base model that is hosted on Amazon Bedrock. The model currently uses 10 examples in the prompt. The model is invoked once daily and is performing well. The company wants to lower the monthly cost.
Which solution will meet these requirements?
Answer: D
NEW QUESTION # 357
A company is building a generative AI application with a foundation model (FM). The application needs to automatically generate marketing emails. The company wants the application's output text to be creative and short in length.
Which configuration of inference parameters will meet these requirements?
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
Inference parameters control output behavior:
* Higher temperature increases creativity and randomness
* Shorter response length limits output size
AWS prompt and inference guidance recommends increasing temperature for creative tasks and reducing response length for concise outputs.
Why the other options are incorrect:
* A reduces creativity.
* B produces long outputs.
* D produces deterministic and lengthy responses.
AWS AI document references:
* Inference Parameters for Foundation Models
* Controlling Creativity and Length in Text Generation
* Prompt Engineering Best Practices
NEW QUESTION # 358
A manufacturing company has an application that ingests consumer complaints from publicly available sources. The application uses complex hard-coded logic to process the complaints. The company wants to scale this logic across markets and product lines.
Which advantage do generative AI models offer for this scenario?
Answer: D
Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
Generative AI models offer adaptability, meaning they can generalize across:
* Different markets
* New product lines
* Variations in language and complaint structure
Unlike hard-coded logic, generative models can adapt to new patterns and inputs without requiring extensive rule rewrites, making them ideal for scaling text-based processing applications.
Why the other options are incorrect:
* Predictability (A) is typically lower in generative models.
* Less sensitivity (C) is incorrect; generative models are sensitive to input variations.
* Explainability (D) is generally limited in large generative models.
AWS AI document references:
* Generative AI Benefits and Trade-offs
* Modernizing Text Processing with Foundation Models
* Scaling NLP Solutions on AWS
NEW QUESTION # 359
......
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