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| Section | Objectives |
|---|---|
| Topic 1: Applications of Foundation Models | - Content generation and summarization - AI-powered assistants and automation - Use cases for generative AI in business |
| Topic 2: Responsible AI and Security | - AI ethics and responsible use - Security, privacy, and governance in AI systems - Bias, fairness, and explainability |
| Topic 3: 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 4: Fundamentals of Artificial Intelligence and Machine Learning | - Common ML workflows and lifecycle - Data fundamentals for AI/ML - Core AI and ML concepts |
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NEW QUESTION # 389
A company wants to improve the accuracy of the responses from a generative AI application. The application uses a foundation model (FM) on Amazon Bedrock.
Which solution meets these requirements MOST cost-effectively?
Answer: C
Explanation:
The company wants to improve the accuracy of a generative AI application using a foundation model (FM) on Amazon Bedrock in the most cost-effective way. Prompt engineering involves optimizing the input prompts to guide the FM to produce more accurate responses without modifying the model itself. This approach is cost-effective because it does not require additional computational resources or training, unlike fine-tuning or retraining.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Prompt engineering is a cost-effective technique to improve the performance of foundation models. By crafting precise and context-rich prompts, users can guide the model to generate more accurate and relevant responses without the need for fine-tuning or retraining." (Source: AWS Bedrock User Guide, Prompt Engineering for Foundation Models) Detailed Option A: Fine-tune the FM.Fine-tuning involves retraining the FM on a custom dataset, which requirescomputational resources, time, and cost (e.g., for Amazon Bedrock fine-tuning jobs). It is not the most cost-effective solution.
Option B: Retrain the FM.Retraining an FM from scratch is highly resource-intensive and expensive, as it requires large datasets and significant compute power. This is not cost-effective.
Option C: Train a new FM.Training a new FM is the most expensive option, as it involves building a model from the ground up, requiring extensive data, compute resources, and expertise. This is not cost-effective.
Option D: Use prompt engineering.This is the correct answer. Prompt engineering adjusts the input prompts to improve the FM's responses without incurring additional compute costs, making it the most cost-effective solution for improving accuracy on Amazon Bedrock.
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 Generative AI Optimization Amazon Bedrock Developer Guide: Cost Optimization for Generative AI (https://aws.amazon.com/bedrock/)
NEW QUESTION # 390
Select the correct AI term from the following list for each statement. Each AI term should be selected one time. (Select THREE.)
* AI
* Deep learning
* ML
Answer:
Explanation:
NEW QUESTION # 391
A company wants to fine-tune a foundation model (FM) by using AWS services. The company needs to ensure that its data stays private, safe, and secure in the source AWS Region where the data is stored.
Which combination of steps will meet these requirements MOST cost-effectively? (Select TWO.)
Answer: B,E
Explanation:
The most cost-effective combination is to perform the managed foundation-model customization through Amazon Bedrock and secure connectivity by using Amazon VPC and AWS PrivateLink.
AWS documentation states that when a Bedrock model-customization job is submitted, Amazon Bedrock accesses the designated training and validation data to fine-tune the selected foundation model. The customization operation is available programmatically through the Amazon Bedrock API.
For network isolation, AWS states: "Using a VPC protects your data" and recommends creating an interface endpoint with AWS PrivateLink so the data does not need to be available over the public internet.
AWS PrivateLink establishes private connectivity between resources in the VPC and Amazon Bedrock without routing application traffic through the public internet. For model customization, AWS supports supplying a VPC configuration containing appropriate subnets and security groups to protect access to training data.
AWS also documents that Bedrock fine-tuning data is used only for the customization process and is not used to train the underlying base model for unrelated customers. Training and validation data provided for fine-tuning is not retained by Bedrock after the job completes.
Option A would introduce unnecessary on-premises infrastructure and hardware costs. Fine-tuning supported Bedrock foundation models does not require deploying the entire service through AWS Outposts.
Option D is invalid because customers do not host the managed Amazon Bedrock service API on premises.
Option E provides monitoring and observability, but CloudWatch logging does not establish private network connectivity or isolate model-customization data.
Thus, Amazon Bedrock supplies the managed fine-tuning capability while VPC connectivity and AWS PrivateLink provide the private network path. This combination minimizes infrastructure management while satisfying the stated security requirement.
Therefore, the correct answers are B and C.
NEW QUESTION # 392
A company is building a generative AI (GenAI) application. The company wants to implement mechanisms to monitor and direct AI system behavior.
Which responsible AI dimension is the company applying?
Answer: B
Explanation:
The verified answer is C. Controllability. AWS defines responsible AI through several dimensions, including fairness, explainability, privacy and security, safety, controllability, veracity and robustness, governance, and transparency. The exact wording in the question is the clue: the company wants mechanisms to monitor and direct AI system behavior. AWS describes controllability as having mechanisms to monitor and steer AI system behavior. That maps directly to the requirement.
Fairness is incorrect because fairness focuses on considering impacts on different groups of stakeholders. It is about avoiding unjust outcomes, biased treatment, or disproportionate harm across populations. The question does not describe demographic representation, biased outputs, or unequal model behavior.
Explainability is incorrect because explainability focuses on understanding and evaluating system outputs. It helps stakeholders understand why the model generated a result or how model behavior can be interpreted. The question is not asking about understanding a decision; it is asking about controlling and directing system behavior.
Safety is incorrect because safety focuses on reducing harmful system output and misuse. Safety is related, but the wording "monitor and direct AI system behavior" is specifically the AWS definition of controllability, not safety. Safety might include preventing toxic, harmful, or dangerous outputs, but controllability is the broader dimension about steering the system according to intended behavior.
Therefore, the responsible AI dimension being applied is controllability, because the company wants active mechanisms to guide, monitor, and steer how the generative AI application behaves in operation.
NEW QUESTION # 393
A medical company is customizing a foundation model (FM) for diagnostic purposes. The company needs the model to be transparent and explainable to meet regulatory requirements.
Which solution will meet these requirements?
Answer: A
NEW QUESTION # 394
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