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AIF-C01熱門考古題 - AIF-C01最新題庫

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AIF-C01熱門考古題 - AIF-C01最新題庫

Posted at yesterday 08:36      View：3 | Replies：0        Print      Only Author   [Copy Link] 1#
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Amazon AIF-C01 考試大綱：
主題簡介
主題 1
  • 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.
主題 2
  • 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.
主題 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.
主題 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.
主題 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.

最新的 AWS Certified AI AIF-C01 免費考試真題 (Q30-Q35):問題 #30
A company is building a customer service chatbot. The company wants the chatbot to improve its responses by learning from past interactions and online resources.
Which AI learning strategy provides this self-improvement capability?
  • A. Reinforcement learning with rewards for positive customer feedback
  • B. Supervised learning with a continuously updated FAQ database
  • C. Unsupervised learning to find clusters of similar customer inquiries
  • D. Supervised learning with a manually curated dataset of good responses and bad responses
答案：A

問題 #31
A company wants to develop ML applications to improve business operations and efficiency.
Select the correct ML paradigm from the following list for each use case. Each ML paradigm should be selected one or more times. (Select FOUR.)
* Supervised learning
* Unsupervised learning

答案：
解題說明：

Explanation:

The company is developing ML applications for various use cases, and the task is to select the correct ML paradigm (supervised or unsupervised learning) for each. Supervised learning involves training a model on labeled data to make predictions, while unsupervised learning identifies patterns or structures in unlabeled data. Each use case aligns with one of these paradigms based on its requirements.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"Supervised learning uses labeled data to train models for tasks like classification (e.g., binary or multi-class classification), where the model predicts a category. Unsupervised learning works with unlabeled data for tasks like clustering (e.g., K-means clustering) or dimensionality reduction, identifying patternsor reducing data complexity without predefined labels." (Source: AWS AI Practitioner Learning Path, Module on Machine Learning Strategies) Detailed Explanation:
Binary classification: Supervised learningBinary classification involves predicting one of two classes (e.g., yes
/no, spam/not spam) using labeled data, making it a supervised learning task. The model learns from examples where the correct class is provided.
Multi-class classification: Supervised learningMulti-class classification extends binary classification to predict one of multiple classes (e.g., categorizing items into several groups). Like binary classification, it requires labeled data, so it falls under supervised learning.
K-means clustering: Unsupervised learningK-means clustering groups data into clusters based on similarity, without requiring labeled data. This is a classic unsupervised learning task, as the algorithm identifies patterns in the data on its own.
Dimensionality reduction: Unsupervised learningDimensionality reduction (e.g., using techniques like PCA) reduces the number of features in a dataset while preserving important information. It does not require labeled data, making it an unsupervised learning task.
Hotspot Selection Analysis:
The hotspot lists four use cases, each with a dropdown containing "Select...," "Supervised learning," and
"Unsupervised learning." The correct selections are:
Binary classification: Supervised learning
Multi-class classification: Supervised learning
K-means clustering: Unsupervised learning
Dimensionality reduction: Unsupervised learning
Each paradigm (supervised and unsupervised learning) is used twice, as the question allows for paradigms to be selected one or more times.
References:
AWS AI Practitioner Learning Path: Module on Machine Learning Strategies Amazon SageMaker Developer Guide: Supervised and Unsupervised Learning (https://docs.aws.amazon.com
/sagemaker/latest/dg/algos.html)
AWS Documentation: Introduction to Machine Learning Paradigms (https://aws.amazon.com/machine- learning/)

問題 #32
A company needs to apply numerical transformations to a set of images to transpose and rotate the images.
  • A. Use AWS Glue Data Quality to make corrections to each image.
  • B. Create a deep neural network by using the images as input.
  • C. Use an Amazon Bedrock large language model (LLM) with a high temperature.
  • D. Create an AWS Lambda function to perform the transformations.
答案：D

問題 #33
What is the purpose of vector embeddings in a large language model (LLM)?
  • A. Providing the count of every word in the input
  • B. Grouping a set of characters to be treated as a single unit
  • C. Splitting text into manageable pieces of data
  • D. Providing the ability to mathematically compare texts
答案：D

問題 #34
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. Train the model on journals and textbooks.
  • C. Prepare the training dataset by creating a .txt file that contains multiple lines in .csv format.
  • D. Purchase Provisioned Throughput for Amazon Bedrock.
答案：A
解題說明：
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.

問題 #35
......
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