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Amazon AWS Certified AI Practitioner Sample Questions (Q199-Q204):

NEW QUESTION # 199
A medical company wants to develop an AI application that can access structured patient records, extract relevant information, and generate concise summaries.
Which solution will meet these requirements?

Answer: D


NEW QUESTION # 200
A company has implemented a generative AI solution to create personalized exercise routines for premium subscription users. The company offers free basic subscriptions and paid premium subscriptions. The company wants to evaluate the AI solution's return on investment over time.

Answer: B

Explanation:
The correct answer is C, as conversion rate (how many users upgrade to premium) and retention rate (how many stay subscribed) are primary indicators of a generative AI system's business impact and ROI.
According to AWS documentation, evaluating AI performance should go beyond technical accuracy to include business metrics that show real-world value. For a premium service, higher conversion implies successful personalization that attracts free users, while strong retention reflects sustained engagement and user satisfaction. Metrics like ARPU or query reduction are secondary indicators. AWS emphasizes outcome- based measurement to ensure AI initiatives contribute measurable ROI tied to customer adoption, loyalty, and profitability. Therefore, tracking conversion and retention gives the clearest long-term measure of financial success from generative AI.
Referenced AWS AI/ML Documents and Study Guides:
AWS Machine Learning Specialty Guide - ML Business Metrics
AWS AI Adoption Framework - Value Realization and ROI Tracking


NEW QUESTION # 201
A retail company is tagging its product inventory. A tag is automatically assigned to each product based on the product description. The company created one product category by using a large language model (LLM) on Amazon Bedrock in few-shot learning mode.
The company collected a labeled dataset and wants to scale the solution to all product categories.
Which solution meets these requirements?

Answer: A

Explanation:
When you have a labeled dataset and need to scale a generative AI solution for more complex or diverse product categories, fine-tuning the foundation model with your dataset is the best approach for consistent, accurate tagging.
D is correct:
"Fine-tuning a foundation model with your labeled data allows the model to generalize to new categories and improve tagging accuracy for your inventory." (Reference: Amazon Bedrock Fine-Tuning, AWS Generative AI) A (zero-shot) and B (prompt templates) do not leverage the labeled data or scale as accurately.
C (continued pre-training) uses unlabeled data, not labeled.


NEW QUESTION # 202
A company wants to develop ML applications to improve business operations and efficiency.
Select the correct ML paradigm from the following list for each use case. Each ML paradigm should be selected one or more times. (Select FOUR.)
* Supervised learning
* Unsupervised learning

Answer:

Explanation:

.
Reference:
AWS AI Practitioner Learning Path: Module on Machine Learning Strategies Amazon SageMaker Developer Guide: Supervised and Unsupervised Learning (https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html) AWS Documentation: Introduction to Machine Learning Paradigms (https://aws.amazon.com/machine-learning/)


NEW QUESTION # 203
Which statement presents an advantage of using Retrieval Augmented Generation (RAG) for natural language processing (NLP) tasks?

Answer: B

Explanation:
* Retrieval-Augmented Generation (RAG) integrates external knowledge sources (databases, vector stores, document repositories) with LLMs, enabling them to generate contextually accurate and up-to- date responses without retraining.
* B is incorrect: RAG does not speed up training; it improves inference results.
* C is incorrect: speech recognition is not an RAG use case.
* D is incorrect: computer vision augmentation is unrelated to RAG.
# Reference:
AWS Documentation - Knowledge Bases for RAG in Amazon Bedrock


NEW QUESTION # 204
......

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