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NEW QUESTION # 387
An AI practitioner is writing software code. The AI practitioner wants to quickly develop a test case and create documentation for the code.
Answer: C
Explanation:
* Amazon Q Developer is an AI-powered coding assistant integrated into IDEs (e.g., VS Code, JetBrains). It can:
* Generate unit tests.
* Create documentation.
* Suggest code completions.
* This is the fastest and most effective solution for this scenario.
# Reference:
Amazon Q Developer - AWS Documentation
NEW QUESTION # 388
A company wants more customized responses to its generative AI models' prompts.
Select the correct customization methodology from the following list for each use case. Each use case should be selected one time. (Select THREE.)
* Continued pre-training
* Data augmentation
* Model fine-tuning
Answer:
Explanation:
Model fine-tuning adapts a pre-trained model to a specific domain or task using labeled data. This is the preferred approach when you want highly customized behavior for a particular application or subject area.
(Reference: Amazon Bedrock Fine-Tuning)
Data augmentation is used to artificially increase the size of a labeled dataset, usually by transforming or generating variations of the original data. This helps improve model generalization when labeled data is limited.
(Reference: AWS Data Preparation Techniques, AWS AI Practitioner Guide) Continued pre-training (also called domain-adaptive pre-training) further trains a foundation model on large amounts of unlabeled data from a specific domain, improving the model's language understanding or generation for that domain.
(Reference: Amazon Bedrock Customization Options)
NEW QUESTION # 389
A company uses a foundation model (FM) on Amazon Bedrock to generate meeting summaries and insights from discussion transcripts. However, productivity has not improved.
Which solution will help determine if the FM meets company business objectives?
Answer: C
Explanation:
The verified answer is A. Compare pre-deployment and post-deployment metrics such as time saved in documentation, number of actionable tasks created, and employee adoption rates. The question is not asking whether the FM produces technically accurate summaries. It is asking whether the FM meets company business objectives, and the stated objective is productivity improvement. AWS AI Practitioner guidance identifies business objective alignment metrics for AI applications, including task completion rate, user satisfaction, and cost per interaction. AWS also identifies business value metrics for generative AI applications such as ROI, efficiency, conversion rate, accuracy, and customer lifetime value. These are business-impact measurements, not just model-quality measurements.
Option A is best because it compares business outcomes before and after deployment. If the tool is intended to improve productivity, the company should measure actual productivity signals: reduced documentation time, more actionable tasks created, higher employee adoption, and other operational improvements. AWS Prescriptive Guidance separates generative AI monitoring into application health, business and user- interaction health, and model quality health. It states that business and user-interaction health evaluates whether the application meets business objectives by tracking adoption, customer satisfaction, productivity improvements, cost savings, and task automation efficiency.
Option B is incomplete because precision, recall, and BLEU are technical evaluation metrics. They can help assess output quality but do not prove productivity improved. Option C may improve the system, but adding RAG does not determine whether the current FM meets business objectives. Option D is also incomplete because employee sentiment alone does not measure actual productivity. Therefore, the correct solution is to compare pre-deployment and post-deployment business metrics.
NEW QUESTION # 390
Which THREE of the following principles of responsible AI are most critical to this scenario? (Choose 3)
* Explainability
* Fairness
* Privacy and security
* Robustness
* Safety
Answer:
Explanation:
This question maps responsible AI principles to specific AI system practices as defined in AWS Responsible AI Guidelines and Amazon Bedrock Responsible AI documentation.
Scenario 1:
Encrypt the application data, and isolate the application on a private network.
# Principle: Privacy and Security
From AWS documentation:
" Protecting user data through encryption, secure network isolation, and access control aligns with the Responsible AI principle of privacy and security. AWS recommends securing all data used by AI systems, both in transit and at rest, to maintain trust and regulatory compliance. " Scenario 2:
Evaluate how different population groups will be impacted.
# Principle: Fairness
From AWS documentation:
" The fairness principle ensures that AI models do not discriminate or generate biased outcomes across population groups. Fairness assessment involves evaluating performance metrics across demographic segments and mitigating any bias detected. " Scenario 3:
Test the application with unexpected data to ensure the application will work in unique situations.
# Principle: Robustness
From AWS documentation:
" Robustness refers to an AI system's ability to maintain reliable performance under varied, noisy, or unexpected input conditions. Testing for robustness helps ensure the model generalizes well and behaves safely in edge cases. " Referenced AWS AI/ML Documents and Study Guides:
AWS Responsible AI Practices Whitepaper - Core Principles of Responsible AI Amazon Bedrock Documentation - Responsible AI and Safety Controls AWS Certified Machine Learning Specialty Guide - AI Governance and Model Evaluation
NEW QUESTION # 391
An education provider is building a question and answer application that uses a generative AI model to explain complex concepts. The education provider wants to automatically change the style of the model response depending on who is asking the question. The education provider will give the model the age range of the user who has asked the question.
Which solution meets these requirements with the LEAST implementation effort?
Answer: C
Explanation:
Adding a role description to the prompt context is a straightforward way to instruct the generative AI model to adjust its response style based on the user's age range. This method requires minimal implementation effort as it does not involve additional training or complex logic.
* Option B (Correct): "Add a role description to the prompt context that instructs the model of the age range that the response should target": This is the correct answer because it involves the least implementation effort while effectively guiding the model to tailor responses according to the age range.
* Option A: "Fine-tune the model by using additional training data" is incorrect because it requires significant effort in gathering data and retraining the model.
* Option C: "Use chain-of-thought reasoning" is incorrect as it involves complex reasoning that may not directly address the need to adjust response style based on age.
* Option D: "Summarize the response text depending on the age of the user" is incorrect because it involves additional processing steps after generating the initial response, increasing complexity.
AWS AI Practitioner References:
* Prompt Engineering Techniques on AWS: AWS recommends using prompt context effectively to guide generative models in providing tailored responses based on specific user attributes.
NEW QUESTION # 392
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