AIF-C01 REAL EXAM, LATEST AIF-C01 EXAM TEST

AIF-C01 Real Exam, Latest AIF-C01 Exam Test

AIF-C01 Real Exam, Latest AIF-C01 Exam Test

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Amazon AWS Certified AI Practitioner Sample Questions (Q83-Q88):

NEW QUESTION # 83
Which phase of the ML lifecycle determines compliance and regulatory requirements?

  • A. Data collection
  • B. Business goal identification
  • C. Feature engineering
  • D. Model training

Answer: B

Explanation:
The business goal identification phase of the ML lifecycle involves defining the objectives of the project and understanding the requirements, including compliance and regulatory considerations. This phase ensures the ML solution aligns with legal and organizational standards before proceeding to technical stages like data collection or model training.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"The business goal identification phase involves defining the problem to be solved, identifying success metrics, and determining compliance and regulatory requirements to ensure the ML solution adheres to legal and organizational standards." (Source: AWS AI Practitioner Learning Path, Module on Machine Learning Lifecycle) Detailed Explanation:
* Option A: Feature engineeringFeature engineering involves creating or selecting features for model training, which occurs after compliance requirements are identified. It does not address regulatory concerns.
* Option B: Model trainingModel training focuses on building the ML model using data, not on determining compliance or regulatory requirements.
* Option C: Data collectionData collection involves gathering data for training, but compliance and regulatory requirements (e.g., data privacy laws) are defined earlier in the business goal identification phase.
* Option D: Business goal identificationThis is the correct answer. This phase ensures that compliance and regulatory requirements are considered at the outset, shaping the entire ML project.
References:
AWS AI Practitioner Learning Path: Module on Machine Learning Lifecycle Amazon SageMaker Developer Guide: ML Workflow (https://docs.aws.amazon.com/sagemaker/latest/dg
/how-it-works-mlconcepts.html)
AWS Well-Architected Framework: Machine Learning Lens (https://docs.aws.amazon.com/wellarchitected
/latest/machine-learning-lens/)


NEW QUESTION # 84
A company wants to use large language models (LLMs) with Amazon Bedrock to develop a chat interface for the company's product manuals. The manuals are stored as PDF files.
Which solution meets these requirements MOST cost-effectively?

  • A. Upload PDF documents to an Amazon Bedrock knowledge base. Use the knowledge base to provide context when users submit prompts to Amazon Bedrock.
  • B. Use all the PDF documents to fine-tune a model with Amazon Bedrock. Use the fine-tuned model to process user prompts.
  • C. Use prompt engineering to add one PDF file as context to the user prompt when the prompt is submitted to Amazon Bedrock.
  • D. Use prompt engineering to add all the PDF files as context to the user prompt when the prompt is submitted to Amazon Bedrock.

Answer: C

Explanation:
Using Amazon Bedrock with large language models (LLMs) allows for efficient utilization of AI to answer queries based on context provided in product manuals. To achieve this cost-effectively, the company should avoid unnecessary use of resources.
Option A (Correct): "Use prompt engineering to add one PDF file as context to the user prompt when the prompt is submitted to Amazon Bedrock": This is the most cost-effective solution. By using prompt engineering, only the relevant content from one PDF file is added as context to each query. This approach minimizes the amount of data processed, which helps in reducing costs associated with LLMs' computational requirements.
Option B: "Use prompt engineering to add all the PDF files as context to the user prompt when the prompt is submitted to Amazon Bedrock" is incorrect. Including all PDF files would increase costs significantly due to the large context size processed by the model.
Option C: "Use all the PDF documents to fine-tune a model with Amazon Bedrock" is incorrect. Fine-tuning a model is more expensive than using prompt engineering, especially if done for multiple documents.
Option D: "Upload PDF documents to an Amazon Bedrock knowledge base" is incorrect because Amazon Bedrock does not have a built-in knowledge base feature for directly managing and querying PDF documents.
AWS AI Practitioner Reference:
Prompt Engineering for Cost-Effective AI: AWS emphasizes the importance of using prompt engineering to minimize costs when interacting with LLMs. By carefully selecting relevant context, users can reduce the amount of data processed and save on expenses.


NEW QUESTION # 85
An airline company wants to build a conversational AI assistant to answer customer questions about flight schedules, booking, and payments. The company wants to use large language models (LLMs) and a knowledge base to create a text-based chatbot interface.
Which solution will meet these requirements with the LEAST development effort?

  • A. Develop a Retrieval Augmented Generation (RAG) agent by using Amazon Bedrock.
  • B. Train models on Amazon SageMaker Autopilot.
  • C. Create a Python application by using Amazon Q Developer.
  • D. Fine-tune models on Amazon SageMaker Jumpstart.

Answer: A

Explanation:
The airline company aims to build a conversational AI assistant using large language models (LLMs) and a knowledge base to create a text-based chatbot with minimal development effort. Retrieval Augmented Generation (RAG) on Amazon Bedrock is an ideal solution because it combines LLMs with a knowledge base to provide accurate, contextually relevant responses without requiring extensive model training or custom development. RAG retrieves relevant information from a knowledge base and uses an LLM to generate responses, simplifying the development process.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Retrieval Augmented Generation (RAG) in Amazon Bedrock enables developers to build conversational AI applications by combining foundation models with external knowledge bases. This approach minimizes development effort by leveraging pre-trained models and integrating them with data sources, such as FAQs or databases, to provide accurate and contextually relevant responses." (Source: AWS Bedrock User Guide, Retrieval Augmented Generation) Detailed Explanation:
Option A: Train models on Amazon SageMaker Autopilot.SageMaker Autopilot is designed for automated machine learning (AutoML) tasks like classification or regression, not for building conversational AI with LLMs and knowledge bases. It requires significant data preparation and is not optimized for chatbot development, making it less suitable.
Option B: Develop a Retrieval Augmented Generation (RAG) agent by using Amazon Bedrock.This is the correct answer. RAG on Amazon Bedrock allows the company to use pre-trained LLMs and integrate them with a knowledge base (e.g., flight schedules or FAQs) to build a chatbot with minimal effort. It avoids the need for extensive training or coding, aligning with the requirement for least development effort.
Option C: Create a Python application by using Amazon Q Developer.While Amazon Q Developer can assist with code generation, building a chatbot from scratch in Python requires significant development effort, including integrating LLMs and a knowledge base manually, which is more complex than using RAG on Bedrock.
Option D: Fine-tune models on Amazon SageMaker Jumpstart.Fine-tuning models on SageMaker Jumpstart requires preparing training data and customizing LLMs, which involves more effort than using a pre-built RAG solution on Bedrock. This option is not the least effort-intensive.
References:
AWS Bedrock User Guide: Retrieval Augmented Generation (https://docs.aws.amazon.com/bedrock/latest
/userguide/rag.html)
AWS AI Practitioner Learning Path: Module on Generative AI and Conversational AI Amazon Bedrock Developer Guide: Building Conversational AI (https://aws.amazon.com/bedrock/)


NEW QUESTION # 86
An AI company periodically evaluates its systems and processes with the help of independent software vendors (ISVs). The company needs to receive email message notifications when an ISV's compliance reports become available.
Which AWS service can the company use to meet this requirement?

  • A. AWS Artifact
  • B. AWS Data Exchange
  • C. AWS Audit Manager
  • D. AWS Trusted Advisor

Answer: A


NEW QUESTION # 87
A company needs to build its own large language model (LLM) based on only the company's private dat a. The company is concerned about the environmental effect of the training process.
Which Amazon EC2 instance type has the LEAST environmental effect when training LLMs?

  • A. Amazon EC2 Trn series
  • B. Amazon EC2 P series
  • C. Amazon EC2 C series
  • D. Amazon EC2 G series

Answer: A

Explanation:
The Amazon EC2 Trn series (Trainium) instances are designed for high-performance, cost-effective machine learning training while being energy-efficient. AWS Trainium-powered instances are optimized for deep learning models and have been developed to minimize environmental impact by maximizing energy efficiency.
Option D (Correct): "Amazon EC2 Trn series": This is the correct answer because the Trn series is purpose-built for training deep learning models with lower energy consumption, which aligns with the company's concern about environmental effects.
Option A: "Amazon EC2 C series" is incorrect because it is intended for compute-intensive tasks but not specifically optimized for ML training with environmental considerations.
Option B: "Amazon EC2 G series" (Graphics Processing Unit instances) is optimized for graphics-intensive applications but does not focus on minimizing environmental impact for training.
Option C: "Amazon EC2 P series" is designed for ML training but does not offer the same level of energy efficiency as the Trn series.
AWS AI Practitioner Reference:
AWS Trainium Overview: AWS promotes Trainium instances as their most energy-efficient and cost-effective solution for ML model training.


NEW QUESTION # 88
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