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[General] Reliable AIF-C01 Test Sample - Reliable Exam AIF-C01 Pass4sure

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【General】 Reliable AIF-C01 Test Sample - Reliable Exam AIF-C01 Pass4sure

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

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Amazon AWS Certified AI Practitioner Sample Questions (Q170-Q175):NEW QUESTION # 170
A company is using a pre-trained large language model (LLM) to extract information from documents. The company noticed that a newer LLM from a different provider is available on Amazon Bedrock. The company wants to transition to the new LLM on Amazon Bedrock.
What does the company need to do to transition to the new LLM?
  • A. Adjust the prompt template.
  • B. Create a new labeled dataset
  • C. Fine-tune the LLM.
  • D. Perform feature engineering.
Answer: A
Explanation:
Transitioning to a new large language model (LLM) on Amazon Bedrock typically involves minimal changes when the new model is pre-trained and available as a foundation model. Since the company is moving from one pre-trained LLM to another, the primary task is to ensure compatibility between the new model's input requirements and the existing application. Adjusting the prompt template is often necessary because different LLMs may have varying prompt formats, tokenization methods, or response behaviors, even for similar tasks like document extraction.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"When switching between foundation models in Amazon Bedrock, you may need to adjust the prompt template to align with the new model's expected input format and optimize its performance for your use case.
Prompt engineering is critical to ensure the model understands the task and generates accurate outputs." (Source: AWS Bedrock User Guide, Prompt Engineering for Foundation Models) Detailed Explanation:
Option A: Create a new labeled dataset.Creating a new labeled dataset is unnecessary when transitioning to a new pre-trained LLM, as pre-trained models are already trained on large datasets. This option would only be relevant if the company were training a custom model from scratch, which is not the case here.
Option B: Perform feature engineering.Feature engineering is typically associated with traditional machine learning models, not pre-trained LLMs. LLMs process raw text inputs, and transitioning to a new LLM does not require restructuring input features. This option is incorrect.
Option C: Adjust the prompt template.This is the correct approach. Different LLMs may interpret prompts differently due to variations in training data, tokenization, or model architecture. Adjusting the prompt template ensures the new LLM understands the task (e.g., document extraction) and produces the desired output format. AWS documentation emphasizes prompt engineering as a key step when adopting a new foundation model.
Option D: Fine-tune the LLM.Fine-tuning is not required for transitioning to a new pre-trained LLM unless the company needs to customize the model for a highly specific task. Since the question does not indicate a need for customization beyond document extraction (a common LLM capability), fine-tuning is unnecessary.
References:
AWS Bedrock User Guide: Prompt Engineering for Foundation Models (https://docs.aws.amazon.com
/bedrock/latest/userguide/prompt-engineering.html)
AWS AI Practitioner Learning Path: Module on Working with Foundation Models in Amazon Bedrock Amazon Bedrock Developer Guide: Transitioning Between Models (https://docs.aws.amazon.com/bedrock
/latest/devguide/)

NEW QUESTION # 171
A company needs to use Amazon SageMaker AI for model training and inference. The company must comply with regulatory requirements to run SageMaker jobs in an isolated environment without internet access.
Which solution will meet these requirements?
  • A. Run SageMaker training and inference by using SageMaker Experiments.
  • B. Encrypt the data at rest by using encryption for SageMaker geospatial capabilities.
  • C. Run SageMaker training and inference by using network isolation.
  • D. Associate appropriate AWS Identity and Access Management (IAM) roles with the SageMaker jobs.
Answer: C
Explanation:
Network isolation is a key security feature for SageMaker. It ensures that training and inference jobs run in a VPC and are not accessible from the internet. Per the official SageMaker documentation:
Explanation:
Network isolation is a key security feature for SageMaker. It ensures that training and inference jobs run in a VPC and are not accessible from the internet. Per the official SageMaker documentation:
"When you enable network isolation, your model can't make any outbound network calls. This is useful for security and regulatory compliance when working with sensitive data."

NEW QUESTION # 172
A company has a team of AI practitioners that builds and maintains AI applications in an AWS account. The company must keep records of the actions that each AI practitioner takes in the AWS account for audit purposes.
Which AWS service will meet these requirements?
  • A. AWS Config
  • B. AWS Trusted Advisor
  • C. AWS Audit Manager
  • D. AWS CloudTrail
Answer: D
Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
AWS CloudTrail records API calls and user activity across AWS services, including:
* Who performed an action
* What action was taken
* When and from where the action occurred
AWS governance guidance identifies CloudTrail as the primary service for audit logging and accountability.
Why the other options are incorrect:
* AWS Config (B) tracks configuration changes, not user actions.
* Audit Manager (C) assesses compliance but does not record individual actions.
* Trusted Advisor (D) provides best-practice recommendations.
AWS AI document references:
* AWS CloudTrail Overview
* Auditing User Activity on AWS
* Governance and Compliance for AI Workloads

NEW QUESTION # 173
A company needs an automated solution to group its customers into multiple categories. The company does not want to manually define the categories. Which ML technique should the company use?
  • A. Clustering
  • B. Logistic regression
  • C. Linear regression
  • D. Classification
Answer: A
Explanation:
Comprehensive and Detailed Explanation from AWS AI Documents:
Classification requires predefined labels (categories). The company explicitly does not want to define categories.
Regression (linear or logistic) predicts numerical values or probabilities, not groups.
Clustering is an unsupervised learning technique that groups similar data points together based on their features without needing labeled categories.
AWS defines clustering as:
"Clustering is an unsupervised machine learning algorithm that automatically groups data points into clusters based on their similarities." This makes Clustering the correct choice for segmenting customers into groups without predefined labels.
Reference:
AWS ML Glossary - Clustering

NEW QUESTION # 174
Which AWS feature records details about ML instance data for governance and reporting?
  • A. Amazon SageMaker Debugger
  • B. Amazon SageMaker JumpStart
  • C. Amazon SageMaker Model Monitor
  • D. Amazon SageMaker Model Cards
Answer: D
Explanation:
Amazon SageMaker Model Cards provide a centralized and standardized repository for documenting machine learning models. They capture key details such as the model's intended use, training and evaluation datasets, performance metrics, ethical considerations, and other relevant information. This documentation facilitates governance and reporting by ensuring that all stakeholders have access to consistent and comprehensive information about each model. While Amazon SageMaker Debugger is used for real-time debugging and monitoring during training, and Amazon SageMaker Model Monitor tracks deployed models for data and prediction quality, neither offers the comprehensive documentation capabilities of Model Cards. Amazon SageMaker JumpStart provides pre-built models and solutions but does not focus on governance documentation.

NEW QUESTION # 175
......
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