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Amazon AWS Certified AI Practitioner Sample Questions (Q87-Q92):

NEW QUESTION # 87
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?

A. Generate simple metrics, reports, and examples by using Amazon SageMaker Clarify.
B. Gather more data. Use Amazon Rekognition to add custom labels to the data.
C. Encrypt and secure training data by using Amazon Macie.
D. Configure the security and compliance by using Amazon Inspector.

Answer: A

Explanation:
Amazon SageMaker Clarify provides transparency and explainability for machine learning models by generating metrics, reports, and examples that help to understand model predictions. For a medical company that needs a foundation model to be transparent and explainable to meet regulatory requirements, SageMaker Clarify is the most suitable solution.
* Amazon SageMaker Clarify:
* It helps in identifying potential bias in the data and model, and also explains model behavior by generating feature attributions, providing insights into which features are most influential in the model's predictions.
* These capabilities are critical in medical applications where regulatory compliance often mandates transparency and explainability to ensure that decisions made by the model can be trusted and audited.
* Why Option B is Correct:
* Transparency and Explainability: SageMaker Clarify is explicitly designed to provide insights into machine learning models' decision-making processes, helping meet regulatory requirements by explaining why a model made a particular prediction.
* Compliance with Regulations: The tool is suitable for use in sensitive domains, such as healthcare, where there is a need for explainable AI.
* Why Other Options are Incorrect:
* A. Amazon Inspector: Focuses on security assessments, not on explainability or model transparency.
* C. Amazon Macie: Provides data security by identifying and protecting sensitive data, but does not help in making models explainable.
* D. Amazon Rekognition: Used for image and video analysis, not relevant to making models explainable.
Thus, B is the correct answer for meeting transparency and explainability requirements for the foundation model

NEW QUESTION # 88
A company is using Amazon SageMaker Studio notebooks to build and train ML models. The company stores the data in an Amazon S3 bucket. The company needs to manage the flow of data from Amazon S3 to SageMaker Studio notebooks.
Which solution will meet this requirement?

A. Configure SageMaker to use S3 Glacier Deep Archive.
B. Configure SageMaker to use a VPC with an S3 endpoint.
C. Use Amazon Inspector to monitor SageMaker Studio.
D. Use Amazon Macie to monitor SageMaker Studio.

Answer: B

Explanation:
To manage the flow of data from Amazon S3 to SageMaker Studio notebooks securely, using a VPC with an S3 endpoint is the best solution.
* Amazon SageMaker and S3 Integration:
* Configuring SageMaker to use a Virtual Private Cloud (VPC) with an S3 endpoint allows the data flow between Amazon S3 and SageMaker Studio notebooks to occur over a private network.
* This setup ensures that traffic between SageMaker and S3 does not traverse the public internet, enhancing security and performance.
* Why Option C is Correct:
* Secure Data Transfer: Ensures secure, private connectivity between SageMaker and S3, reducing exposure to potential security risks.
* Direct Access to S3: Using an S3 endpoint in a VPC allows direct access to data in S3 without leaving the AWS network.
* Why Other Options are Incorrect:
* A. Amazon Inspector: Focuses on identifying security vulnerabilities, not managing data flow.
* B. Amazon Macie: Monitors for sensitive data but does not manage data flow between S3 and SageMaker.
* D. S3 Glacier Deep Archive: Is a storage class for archiving data, not for managing active data flow.

NEW QUESTION # 89
A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment analysis. The company wants to classify the sentiment of text passages as positive or negative.
Which prompt engineering strategy meets these requirements?

A. Provide the new text passage with a few examples of unrelated tasks, such as text summarization or question answering.
B. Provide a detailed explanation of sentiment analysis and how LLMs work in the prompt.
C. Provide the new text passage to be classified without any additional context or examples.
D. Provide examples of text passages with corresponding positive or negative labels in the prompt followed by the new text passage to be classified.

Answer: D

Explanation:
Providing examples of text passages with corresponding positive or negative labels in the prompt followed by the new text passage to be classified is the correct prompt engineering strategy for using a large language model (LLM) on Amazon Bedrock for sentiment analysis.
* Example-Driven Prompts:
* This strategy, known as few-shot learning, involves giving the model examples of input-output pairs (e.g., text passages with their sentiment labels) to help it understand the task context.
* It allows the model to learn from these examples and apply the learned pattern to classify new text passages correctly.
* Why Option A is Correct:
* Guides the Model: Providing labeled examples teaches the model how to perform sentiment analysis effectively, increasing accuracy.
* Contextual Relevance: Aligns the model's responses to the specific task of classifying sentiment.
* Why Other Options are Incorrect:
* B. Detailed explanation of sentiment analysis: Unnecessary for the model's operation; it requires examples, not explanations.
* C. New text passage without context: Provides no guidance or learning context for the model.
* D. Unrelated task examples: Would confuse the model and lead to inaccurate results.

NEW QUESTION # 90
A customer service team is developing an application to analyze customer feedback and automatically classify the feedback into different categories. The categories include product quality, customer service, and delivery experience.
Which AI concept does this scenario present?

A. Recommendation systems
B. Natural language processing (NLP)
C. Fraud detection
D. Computer vision

Answer: B

Explanation:
The scenario involves analyzing customer feedback and automatically classifying it into categories such as product quality, customer service, and delivery experience. This task requires processing and understanding textual data, which is a core application of natural language processing (NLP). NLP encompasses techniques for analyzing, interpreting, and generating human language, including tasks like text classification, sentiment analysis, and topic modeling, all of which are relevant to this use case.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"Natural Language Processing (NLP) enables machines to understand and process human language. Common NLP tasks include text classification, sentiment analysis, named entity recognition, and topic modeling.
Services like Amazon Comprehend can be used to classify text into predefined categories based on content." (Source: AWS AI Practitioner Learning Path, Module on AI and ML Concepts) Detailed Explanation:
Option A: Computer visionComputer vision involves processing and analyzing visual data, such as images or videos. Since the scenario deals with textual customer feedback, computer vision is not applicable.
Option B: Natural language processing (NLP)This is the correct answer. The task of classifying customer feedback into categories requires understanding and processing text, which is an NLP task. AWS services like Amazon Comprehend are specifically designed for such text classification tasks.
Option C: Recommendation systemsRecommendation systems suggest items or content based on user preferences or behavior. The scenario does not involve recommending products or services but rather classifying feedback, so this option is incorrect.
Option D: Fraud detectionFraud detection involves identifying anomalous or fraudulent activities, typically in financial or transactional data. The scenario focuses on text classification, not anomaly detection, making this option irrelevant.
References:
AWS AI Practitioner Learning Path: Module on AI and ML Concepts
Amazon Comprehend Developer Guide: Text Classification (https://docs.aws.amazon.com/comprehend/latest
/dg/how-classification.html)
AWS Documentation: Introduction to NLP (https://aws.amazon.com/what-is/natural-language-processing/)

NEW QUESTION # 91
A company wants to create a chatbot that answers questions about human resources policies. The company is using a large language model (LLM) and has a large digital documentation base.
Which technique should the company use to optimize the generated responses?

A. Use few-shot prompting.
B. Set the temperature to 1.
C. Decrease the token size.
D. Use Retrieval Augmented Generation (RAG).

Answer: D

Explanation:
The company is building a chatbot using an LLM to answer questions about HR policies, with access to a large digital documentation base. Retrieval Augmented Generation (RAG) optimizes the LLM's responses by retrieving relevant information from the documentation base and using it to generate accurate, contextually grounded answers, reducing hallucinations and improving response quality.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Retrieval Augmented Generation (RAG) enhances the performance of large language models by retrieving relevant information from external knowledge bases, such as documentation or databases, and incorporating it into the generation process. This technique ensures responses are accurate and grounded in the provided data, making it ideal for applications like policy chatbots." (Source: AWS Bedrock User Guide, Retrieval Augmented Generation) Detailed Explanation:
* Option A: Use Retrieval Augmented Generation (RAG).This is the correct answer. RAG leverages the documentation base to provide the LLM with relevant HR policy information, optimizing the chatbot's responses for accuracy and relevance.
* Option B: Use few-shot prompting.Few-shot prompting provides a few examples in the prompt to guide the LLM, but it is less effective than RAG for large documentation bases, as it cannot dynamically retrieve specific policy details.
* Option C: Set the temperature to 1.Setting the temperature to 1 controls the randomness of the LLM' s output but does not optimize responses using external documentation. This option is unrelated to the documentation base.
* Option D: Decrease the token size.Decreasing token size (likely referring to limiting input/output tokens) may reduce response length but does not optimize the quality of responses using the documentation base.
References:
AWS Bedrock User Guide: Retrieval Augmented Generation (https://docs.aws.amazon.com/bedrock/latest
/userguide/rag.html)
AWS AI Practitioner Learning Path: Module on Generative AI Optimization Amazon Bedrock Developer Guide: Building Policy Chatbots (https://aws.amazon.com/bedrock/)

NEW QUESTION # 92
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Jack Reed Jack Reed

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