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Amazon AWS Certified AI Practitioner Sample Questions (Q284-Q289):

NEW QUESTION # 284
An AI practitioner is using an Amazon Bedrock base model to summarize session chats from the customer service department. The AI practitioner wants to store invocation logs to monitor model input and output data.
Which strategy should the AI practitioner use?

A. Enable invocation logging in Amazon Bedrock.
B. Configure model invocation logging in Amazon EventBridge.
C. Configure AWS CloudTrail as the logs destination for the model.
D. Configure AWS Audit Manager as the logs destination for the model.

Answer: A

Explanation:
Amazon Bedrock provides an option to enable invocation logging to capture and store the input and output data of the models used. This is essential for monitoring and auditing purposes, particularly when handling customer data.
* Option B (Correct): "Enable invocation logging in Amazon Bedrock": This is the correct answer as it directly enables the logging of all model invocations, ensuring transparency and traceability.
* Option A: "Configure AWS CloudTrail" is incorrect because CloudTrail logs API calls but does not provide specific logging for model inputs and outputs.
* Option C: "Configure AWS Audit Manager" is incorrect as Audit Manager is used for compliance reporting, not specific invocation logging for AI models.
* Option D: "Configure model invocation logging in Amazon EventBridge" is incorrect as EventBridge is for event-driven architectures, not specifically designed for logging AI model inputs and outputs.
AWS AI Practitioner References:
* Amazon Bedrock Logging Capabilities: AWS emphasizes using built-in logging features in Bedrock to maintain data integrity and transparency in model operations.

NEW QUESTION # 285
A company has built a chatbot that can respond to natural language questions with images. The company wants to ensure that the chatbot does not return inappropriate or unwanted images.
Which solution will meet these requirements?

A. Retrain the model with a general public dataset.
B. Implement moderation APIs.
C. Perform model validation.
D. Automate user feedback integration.

Answer: B

Explanation:
Moderation APIs, such as Amazon Rekognition's Content Moderation API, can help filter and block inappropriate or unwanted images from being returned by a chatbot. These APIs are specifically designed to detect and manage undesirable content in images.
* Option A (Correct): "Implement moderation APIs": This is the correct answer because moderation APIs are designed to identify and filter inappropriate content, ensuring the chatbot does not return unwanted images.
* Option B: "Retrain the model with a general public dataset" is incorrect because retraining does not directly prevent inappropriate content from being returned.
* Option C: "Perform model validation" is incorrect as it ensures model correctness, not content moderation.
* Option D: "Automate user feedback integration" is incorrect because user feedback does not prevent inappropriate images in real-time.
AWS AI Practitioner References:
* AWS Content Moderation Services: AWS provides moderation APIs for filtering unwanted content from applications.

NEW QUESTION # 286
Which component of Amazon Bedrock Studio can help secure the content that AI systems generate?

A. Function calling
B. Access controls
C. Guardrails
D. Knowledge bases

Answer: C

Explanation:
Amazon Bedrock Studio provides tools to build and manage generative AI applications, and the company needs a component to secure the content generated by AI systems. Guardrails in Amazon Bedrock are designed to ensure safe and responsible AI outputs by filtering harmful or inappropriate content, making them the key component for securing generated content.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Guardrails in Amazon Bedrock provide mechanisms to secure the content generated by AI systems by filtering out harmful or inappropriate outputs, such as hate speech, violence, or misinformation, ensuring responsible AI usage." (Source: AWS Bedrock User Guide, Guardrails for Responsible AI) Detailed Option A: Access controlsAccess controls manage who can use or interact with the AI system but do not directly secure the content generated by the system.
Option B: Function callingFunction calling enables AI models to interact with external tools or APIs, but it is not related to securing generated content.
Option C: GuardrailsThis is the correct answer. Guardrails in Amazon Bedrock secure generated content by filtering out harmful or inappropriate material, ensuring safe outputs.
Option D: Knowledge basesKnowledge bases provide data for AI models to generate responses but do not inherently secure the content that is generated.
Reference:
AWS Bedrock User Guide: Guardrails for Responsible AI (https://docs.aws.amazon.com/bedrock/latest/userguide/guardrails.html) AWS AI Practitioner Learning Path: Module on Responsible AI and Model Safety Amazon Bedrock Developer Guide: Securing AI Outputs (https://aws.amazon.com/bedrock/)

NEW QUESTION # 287
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?

A. Use Amazon Personalize to analyze patient engagement patterns. Integrate the output with a general purpose text summarization tool.
B. Implement Amazon Kendra to provide a searchable index for medical records. Use a template-based system to format summaries.
C. Use Amazon Textract to convert scanned documents into digital text. Design a keyword extraction system to generate summaries.
D. Use Amazon Comprehend Medical to extract relevant medical entities and relationships. Apply rule- based logic to structure and format summaries.

Answer: D

Explanation:
Amazon Comprehend Medical is designed for processing medical records and extracting key clinical entities, useful for summaries. Per the AWS Comprehend Medical documentation:
"Amazon Comprehend Medical enables extraction of relevant medical information from unstructured clinical text such as medications, conditions, and relationships, making it ideal for summarization tasks."

NEW QUESTION # 288
A company is making a chatbot. The chatbot uses Amazon Lex and Amazon OpenSearch Service. The chatbot uses the company's private data to answer questions. The company needs to convert the data into a vector representation before storing the data in a database.

A. Text embeddings model
B. Instruction following model
C. Text completion model
D. Image generation model

Answer: A

NEW QUESTION # 289
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