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【General】 AIF-C01 Pass4sure | AIF-C01 Exam Practice

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Amazon AIF-C01 Exam Syllabus Topics:
TopicDetails
Topic 1
  • 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 2
  • 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 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
  • 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 5
  • 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.

Amazon AWS Certified AI Practitioner Sample Questions (Q326-Q331):NEW QUESTION # 326
A company wants to assess the costs that are associated with using a large language model (LLM) to generate inferences. The company wants to use Amazon Bedrock to build generative AI applications.
Which factor will drive the inference costs?
  • A. Number of tokens consumed
  • B. Total training time
  • C. Temperature value
  • D. Amount of data used to train the LLM
Answer: A

NEW QUESTION # 327
Which AWS service makes foundation models (FMs) available to help users build and scale generative AI applications?
  • A. Amazon Q Developer
  • B. Amazon Bedrock
  • C. Amazon Comprehend
  • D. Amazon Kendra
Answer: B
Explanation:
The correct answer is Amazon Bedrock, AWS's fully managed service for building and scaling generative AI applications using foundation models (FMs). Bedrock gives developers access to models from leading providers such as Anthropic (Claude), Meta (Llama), Mistral, Cohere, and Amazon Titan. Users can invoke these models via API without managing infrastructure or model training. According to AWS documentation, Bedrock supports tasks such as text generation, summarization, question answering, image generation, and RAG workflows with minimal setup. It supports both on-demand and provisioned throughput modes and integrates with features like Guardrails, Knowledge Bases, and Agents for secure, enterprise-grade applications. Amazon Q Developer is a generative AI tool for developers, but it doesn't host or scale models.
Amazon Kendra is an intelligent search engine, and Amazon Comprehend is used for NLP tasks like entity extraction-not foundation model access.
Referenced AWS AI/ML Documents and Study Guides:
Amazon Bedrock Developer Guide - Foundation Models and Use Cases
AWS Certified ML Specialty Guide - Generative AI on AWS

NEW QUESTION # 328
A company has built an image classification model to predict plant diseases from photos of plant leaves. The company wants to evaluate how many images the model classified correctly.
Which evaluation metric should the company use to measure the model's performance?
  • A. Root mean squared error (RMSE)
  • B. R-squared score
  • C. Accuracy
  • D. Learning rate
Answer: C
Explanation:
Accuracy is the most appropriate metric to measure the performance of an image classification model. It indicates the percentage of correctly classified images out of the total number of images. In the context of classifying plant diseases from images, accuracy will help the company determine how well the model is performing by showing how many images were correctly classified.
Option B (Correct): "Accuracy": This is the correct answer because accuracy measures the proportion of correct predictions made by the model, which is suitable for evaluating the performance of a classification model.
Option A: "R-squared score" is incorrect as it is used for regression analysis, not classification tasks.
Option C: "Root mean squared error (RMSE)" is incorrect because it is also used for regression tasks to measure prediction errors, not for classification accuracy.
Option D: "Learning rate" is incorrect as it is a hyperparameter for training, not a performance metric.
AWS AI Practitioner Reference:
Evaluating Machine Learning Models on AWS: AWS documentation emphasizes the use of appropriate metrics, like accuracy, for classification tasks.

NEW QUESTION # 329
A company wants to create a chatbot to answer employee questions about company policies. Company policies are updated frequently. The chatbot must reflect the changes in near real time. The company wants to choose a large language model (LLM).
  • A. Create a Retrieval Augmented Generation (RAG) workflow by using Amazon Bedrock Knowledge Bases.
  • B. Select a foundation model (FM) from Amazon Bedrock to build an application.
  • C. Use Amazon Q Business to build a custom Q App.
  • D. Fine-tune an LLM on the company policy text by using Amazon SageMaker.
Answer: A
Explanation:
The correct answer is C because Retrieval-Augmented Generation (RAG) allows a large language model to provide responses based on up-to-date content from external data sources without the need to fine-tune the model.
According to the AWS Bedrock Developer Guide:
"Amazon Bedrock Knowledge Bases enables developers to augment foundation models (FMs) with company- specific data that is updated in real time or near real time. By separating retrieval from the model itself, RAG- based approaches avoid the need for frequent retraining or fine-tuning." This means a company can use a knowledge base with Amazon Bedrock to dynamically fetch the latest company policy information and feed it to the LLM in the prompt. This approach is ideal for use cases where the content (like policies) changes frequently, and latency for updates must be minimal.
Explanation of other options:
A). Fine-tuning an LLM with SageMaker is not optimal for frequently updated data. Fine-tuning involves retraining and redeploying the model, which is time-consuming and not suited for real-time updates. As stated in the SageMaker documentation:
"Fine-tuning is best used for use cases where the data changes infrequently and where highly specific model behavior is required."
B). Selecting a foundation model alone does not fulfill the real-time requirement. The FM's base knowledge is static unless augmented through additional methods like RAG.
D). Amazon Q Business is intended for workplace productivity and enterprise use but is more opinionated in structure and doesn't provide the same flexibility as a custom RAG workflow for building a tailored chatbot application. While it supports some real-time data sync features, it's not purpose-built for LLM-based chat systems with dynamic data feeds like Knowledge Bases in Bedrock.
Therefore, the most appropriate and scalable solution aligned with AWS recommendations is C.
Referenced AWS AI/ML Documents and Study Guides:
Amazon Bedrock Developer Guide - Knowledge Bases and RAG (2024 Edition) AWS Certified Machine Learning Specialty Study Guide - Generative AI Section AWS Documentation: Choosing Between Fine-Tuning and RAG for LLM Applications Amazon SageMaker Documentation - Model Tuning and Deployment Best Practices (2024)

NEW QUESTION # 330
A company uses a foundation model (FM) from Amazon Bedrock for an AI search tool. The company wants to fine-tune the model to be more accurate by using the company's data.
Which strategy will successfully fine-tune the model?
  • A. Provide labeled data with the prompt field and the completion field.
  • B. Purchase Provisioned Throughput for Amazon Bedrock.
  • C. Prepare the training dataset by creating a .txt file that contains multiple lines in .csv format.
  • D. Train the model on journals and textbooks.
Answer: A
Explanation:
Providing labeled data with both a prompt field and a completion field is the correct strategy for fine-tuning a foundation model (FM) on Amazon Bedrock.
* Fine-Tuning Strategy:
* To fine-tune a model, labeled data that pairs input prompts with the correct outputs (completions) is necessary.
* This allows the model to learn the desired behavior or response style based on the provided examples.
* Why Option A is Correct:
* Proper Training Format: The prompt-completion pairs provide the necessary format for training the model to produce accurate outputs.
* Customization: Ensures that the model is fine-tuned to the specific requirements of the company' s data and desired outputs.
* Why Other Options are Incorrect:
* B. Prepare a .txt file in .csv format: This does not align with the specific need for labeled data with prompts and completions.
* C. Purchase Provisioned Throughput: Relates to read/write capacity in databases, not to model fine-tuning.
* D. Train on journals and textbooks: Lacks the specific format and labeling required for fine- tuning.

NEW QUESTION # 331
......
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