Free AIF-C01 Exam Braindumps
AWS Certified AI Practitioner (page: 5)

Updated On: 19-Oct-2026

A company is building a generative AI application to help customers make travel reservations. The application will process customer requests and invoke the appropriate API calls to complete reservation transactions.
Which Amazon Bedrock resource will meet these requirements?

  1. Agents
  2. Intelligent prompt routing
  3. Knowledge Bases
  4. Guardrails

Answer(s): A

Explanation:

Agents in Amazon Bedrock are designed to interpret user requests, orchestrate workflows, and automatically invoke the appropriate APIs and services to complete tasks such as reservation transactions in a generative AI
application.



A medical company deployed a disease detection model on Amazon Bedrock. To comply with privacy policies,
the company wants to prevent the model from including personal patient information in its responses. The company also wants to receive notification when policy violations occur.
Which solution meets these requirements?

  1. Use Amazon Macie to scan the model's output for sensitive data and set up alerts for potential violations.
  2. Configure AWS CloudTrail to monitor the model's responses and create alerts for any detected personal information.
  3. Use Guardrails for Amazon Bedrock to filter content. Set up Amazon CloudWatch alarms for notification of policy violations.
  4. Implement Amazon SageMaker Model Monitor to detect data drift and receive alerts when model quality degrades.

Answer(s): C

Explanation:

Guardrails for Amazon Bedrock can be used to filter content and ensure that personal patient information is not included in model responses. Setting up Amazon CloudWatch alarms allows the company to receive notifications when policy violations occur. The other options are not specifically designed for filtering model output and monitoring policy compliance.



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?

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

Answer(s): A

Explanation:

Amazon Comprehend Medical is specifically designed to extract structured medical information (such as medication, condition, test results) from unstructured text. By applying rule-based logic afterward, relevant data can be formatted into concise summaries, meeting both the extraction and summarization needs.



A company is preparing for a compliance review. The company needs an AWS service that automatically collects information from the company's AWS accounts and generates reports for reviewers.
Which AWS service meets these requirements?

  1. AWS Artifact
  2. AWS Audit Manager
  3. Amazon Macie
  4. Amazon Inspector

Answer(s): B

Explanation:

AWS Audit Manager automatically collects evidence from AWS accounts, maps that evidence to compliance controls and frameworks, and generates audit-ready reports for reviewers.



What is tokenization used for in natural language processing (NLP)?

  1. To encrypt text data
  2. To compress text files
  3. To break text into smaller units for processing
  4. To translate text between languages

Answer(s): C

Explanation:

Tokenization is the process of dividing text into smaller units, such as words, subwords, or characters, which can then be analyzed or processed by NLP models. It is a foundational step in preparing text data for machine learning.



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