AIF-C01 VALID TEST FORUM | PDF AIF-C01 DOWNLOAD

AIF-C01 Valid Test Forum | PDF AIF-C01 Download

AIF-C01 Valid Test Forum | PDF AIF-C01 Download

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DumpsValid is aware of your busy routine; therefore, it has made the AWS Certified AI Practitioner AIF-C01 dumps format to facilitate you to prepare for the AWS Certified AI Practitioner AIF-C01 exam. We adhere strictly to the syllabus set by Amazon AIF-C01 Certification Exam. What will make your AIF-C01 test preparation easy is its compatibility with all devices such as PCs, tablets, laptops, and androids.

Amazon AIF-C01 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Fundamentals of Generative AI: This domain explores the basics of generative AI, focusing on techniques for creating new content from learned patterns, including text and image generation. It targets professionals interested in understanding generative models, such as developers and researchers in AI.
Topic 2
  • Guidelines for Responsible AI: This domain highlights the ethical considerations and best practices for deploying AI solutions responsibly, including ensuring fairness and transparency. It is aimed at AI practitioners, including data scientists and compliance officers, who are involved in the development and deployment of AI systems and need to adhere to ethical standards.
Topic 3
  • Fundamentals of AI and ML: This domain covers the fundamental concepts of artificial intelligence (AI) and machine learning (ML), including core algorithms and principles. It is aimed at individuals new to AI and ML, such as entry-level data scientists and IT professionals.
Topic 4
  • Security, Compliance, and Governance for AI Solutions: This domain covers the security measures, compliance requirements, and governance practices essential for managing AI solutions. It targets security professionals, compliance officers, and IT managers responsible for safeguarding AI systems, ensuring regulatory compliance, and implementing effective governance frameworks.
Topic 5
  • Applications of Foundation Models: This domain examines how foundation models, like large language models, are used in practical applications. It is designed for those who need to understand the real-world implementation of these models, including solution architects and data engineers who work with AI technologies to solve complex problems.

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Amazon AWS Certified AI Practitioner Sample Questions (Q68-Q73):

NEW QUESTION # 68
A company is implementing the Amazon Titan foundation model (FM) by using Amazon Bedrock. The company needs to supplement the model by using relevant data from the company's private data sources.
Which solution will meet this requirement?

  • A. Enable model invocation logging
  • B. Use a different FM
  • C. Choose a lower temperature value
  • D. Create an Amazon Bedrock knowledge base

Answer: D

Explanation:
Creating an Amazon Bedrock knowledge base allows the integration of external or private data sources with a foundation model (FM) like Amazon Titan. This integration helps supplement the model with relevant data from the company's private data sources to enhance its responses.
* Option C (Correct): "Create an Amazon Bedrock knowledge base": This is the correct answer as it enables the company to incorporate private data into the FM to improve its effectiveness.
* Option A: "Use a different FM" is incorrect because it does not address the need to supplement the current model with private data.
* Option B: "Choose a lower temperature value" is incorrect as it affects output randomness, not the integration of private data.
* Option D: "Enable model invocation logging" is incorrect because logging does not help in supplementing the model with additional data.
AWS AI Practitioner References:
* Amazon Bedrock and Knowledge Integration: AWS explains how creating a knowledge base allows Amazon Bedrock to use external data sources to improve the FM's relevance and accuracy.


NEW QUESTION # 69
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. Implement Amazon Kendra to provide a searchable index for medical records. Use a template-based system to format summaries.
  • B. Use Amazon Textract to convert scanned documents into digital text. Design a keyword extraction system to generate summaries.
  • C. Use Amazon Comprehend Medical to extract relevant medical entities and relationships. Apply rule-based logic to structure and format summaries.
  • D. Use Amazon Personalize to analyze patient engagement patterns. Integrate the output with a general purpose text summarization tool.

Answer: C


NEW QUESTION # 70
A company needs to monitor the performance of its ML systems by using a highly scalable AWS service.
Which AWS service meets these requirements?

  • A. Amazon CloudWatch
  • B. AWS CloudTrail
  • C. AWS Config
  • D. AWS Trusted Advisor

Answer: A


NEW QUESTION # 71
A company wants to use a pre-trained generative AI model to generate content for its marketing campaigns. The company needs to ensure that the generated content aligns with the company's brand voice and messaging requirements.
Which solution meets these requirements?

  • A. Create effective prompts that provide clear instructions and context to guide the model's generation.
  • B. Select a large, diverse dataset to pre-train a new generative model.
  • C. Increase the model's complexity by adding more layers to the model's architecture.
  • D. Optimize the model's architecture and hyperparameters to improve the model's overall performance.

Answer: A


NEW QUESTION # 72
A company is using an Amazon Bedrock base model to summarize documents for an internal use case. The company trained a custom model to improve the summarization quality.
Which action must the company take to use the custom model through Amazon Bedrock?

  • A. Grant access to the custom model in Amazon Bedrock.
  • B. Purchase Provisioned Throughput for the custom model.
  • C. Deploy the custom model in an Amazon SageMaker endpoint for real-time inference.
  • D. Register the model with the Amazon SageMaker Model Registry.

Answer: C

Explanation:
To use a custom model that has been trained to improve summarization quality, the company must deploy the model on an Amazon SageMaker endpoint. This allows the model to be used for real-time inference through Amazon Bedrock or other AWS services. By deploying the model in SageMaker, the custom model can be accessed programmatically via API calls, enabling integration with Amazon Bedrock.
Option B (Correct): "Deploy the custom model in an Amazon SageMaker endpoint for real-time inference": This is the correct answer because deploying the model on SageMaker enables it to serve real-time predictions and be integrated with Amazon Bedrock.
Option A: "Purchase Provisioned Throughput for the custom model" is incorrect because provisioned throughput is related to database or storage services, not model deployment.
Option C: "Register the model with the Amazon SageMaker Model Registry" is incorrect because while the model registry helps with model management, it does not make the model accessible for real-time inference.
Option D: "Grant access to the custom model in Amazon Bedrock" is incorrect because Bedrock does not directly manage custom model access; it relies on deployed endpoints like those in SageMaker.
AWS AI Practitioner Reference:
Amazon SageMaker Endpoints: AWS recommends deploying models to SageMaker endpoints to use them for real-time inference in various applications.


NEW QUESTION # 73
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