AIF-C01 PDF DUMPS & LATEST AIF-C01 EXAM COST

AIF-C01 Pdf Dumps & Latest AIF-C01 Exam Cost

AIF-C01 Pdf Dumps & Latest AIF-C01 Exam Cost

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Tags: AIF-C01 Pdf Dumps, Latest AIF-C01 Exam Cost, Valid Braindumps AIF-C01 Pdf, AIF-C01 Latest Exam Labs, AIF-C01 Free Practice

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Amazon AIF-C01 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 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
  • 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.
Topic 5
  • 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.

Amazon AWS Certified AI Practitioner Sample Questions (Q33-Q38):

NEW QUESTION # 33
Which option is a benefit of ongoing pre-training when fine-tuning a foundation model (FM)?

  • A. Optimizes model inference time
  • B. Helps decrease the model's complexity
  • C. Improves model performance over time
  • D. Decreases the training time requirement

Answer: C


NEW QUESTION # 34
A law firm wants to build an AI application by using large language models (LLMs). The application will read legal documents and extract key points from the documents.
Which solution meets these requirements?

  • A. Build an automatic named entity recognition system.
  • B. Develop a summarization chatbot.
  • C. Develop a multi-language translation system.
  • D. Create a recommendation engine.

Answer: B

Explanation:
A summarization chatbot is ideal for extracting key points from legal documents. Large language models (LLMs) can be used to summarize complex texts, such as legal documents, making them more accessible and understandable.
* Option C (Correct): "Develop a summarization chatbot": This is the correct answer because a summarization chatbot uses LLMs to condense and extract key information from text, which is precisely the requirement for reading and summarizing legal documents.
* Option A: "Build an automatic named entity recognition system" is incorrect because it focuses on identifying specific entities, not summarizing documents.
* Option B: "Create a recommendation engine" is incorrect as it is used to suggest products or content, not summarize text.
* Option D: "Develop a multi-language translation system" is incorrect because translation is unrelated to summarizing text.
AWS AI Practitioner References:
* Using LLMs for Text Summarization on AWS: AWS supports developing summarization tools using its AI services, including Amazon Bedrock.


NEW QUESTION # 35
A company wants to build an ML model by using Amazon SageMaker. The company needs to share and manage variables for model development across multiple teams.
Which SageMaker feature meets these requirements?

  • A. Amazon SageMaker Model Cards
  • B. Amazon SageMaker Data Wrangler
  • C. Amazon SageMaker Feature Store
  • D. Amazon SageMaker Clarify

Answer: C

Explanation:
Amazon SageMaker Feature Store is the correct solution for sharing and managing variables (features) across multiple teams during model development.
* Amazon SageMaker Feature Store:
* A fully managed repository for storing, sharing, and managing machine learning features across different teams and models.
* It enables collaboration and reuse of features, ensuring consistent data usage and reducing redundancy.
* Why Option A is Correct:
* Centralized Feature Management: Provides a central repository for managing features, making it easier to share them across teams.
* Collaboration and Reusability: Improves efficiency by allowing teams to reuse existing features instead of creating them from scratch.
* Why Other Options are Incorrect:
* B. SageMaker Data Wrangler: Helps with data preparation and analysis but does not provide a centralized feature store.
* C. SageMaker Clarify: Used for bias detection and explainability, not for managing variables across teams.
* D. SageMaker Model Cards: Provide model documentation, not feature management.


NEW QUESTION # 36
A company wants to build an interactive application for children that generates new stories based on classic stories. The company wants to use Amazon Bedrock and needs to ensure that the results and topics are appropriate for children.
Which AWS service or feature will meet these requirements?

  • A. Amazon Rekognition
  • B. Guardrails for Amazon Bedrock
  • C. Agents for Amazon Bedrock
  • D. Amazon Bedrock playgrounds

Answer: B

Explanation:
Amazon Bedrock is a service that provides foundational models for building generative AI applications.
When creating an application for children, it is crucial to ensure that the generated content is appropriate for the target audience. "Guardrails" in Amazon Bedrock provide mechanisms to control the outputs and topics of generated content to align with desired safety standards and appropriateness levels.
* Option C (Correct): "Guardrails for Amazon Bedrock": This is the correct answer because guardrails are specifically designed to help users enforce content moderation, filtering, and safety checks on the outputs generated by models in Amazon Bedrock. For a children's application, guardrails ensure that all content generated is suitable and appropriate for the intended audience.
* Option A: "Amazon Rekognition" is incorrect. Amazon Rekognition is an image and video analysis service that can detect inappropriate content in images or videos, but it does not handle text or story generation.
* Option B: "Amazon Bedrock playgrounds" is incorrect because playgrounds are environments for experimenting and testing model outputs, but they do not inherently provide safeguards to ensure content appropriateness for specific audiences, such as children.
* Option D: "Agents for Amazon Bedrock" is incorrect. Agents in Amazon Bedrock facilitate building AI applications with more interactive capabilities, but they do not provide specific guardrails for ensuring content appropriateness for children.
AWS AI Practitioner References:
* Guardrails in Amazon Bedrock: Designed to help implement controls that ensure generated content is safe and suitable for specific use cases or audiences, such as children, by moderating and filtering inappropriate or undesired content.
* Building Safe AI Applications: AWS provides guidance on implementing ethical AI practices, including using guardrails to protect against generating inappropriate or biased content.


NEW QUESTION # 37
An AI practitioner has a database of animal photos. The AI practitioner wants to automatically identify and categorize the animals in the photos without manual human effort.
Which strategy meets these requirements?

  • A. Object detection
  • B. Inpainting
  • C. Anomaly detection
  • D. Named entity recognition

Answer: A


NEW QUESTION # 38
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