Sample AIF-C01 Questions, Guide AIF-C01 Torrent

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Amazon AIF-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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	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 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.

>> Sample AIF-C01 Questions <<

Latest Updated Amazon Sample AIF-C01 Questions - Guide AWS Certified AI Practitioner Torrent

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Amazon AWS Certified AI Practitioner Sample Questions (Q25-Q30):

NEW QUESTION # 25
A company is using an Amazon Bedrock base model to summarize documents for an internal use case. The company trained a custom model to improve the summarization quality.
Which action must the company take to use the custom model through Amazon Bedrock?

A. Register the model with the Amazon SageMaker Model Registry.
B. Deploy the custom model in an Amazon SageMaker endpoint for real-time inference.
C. Purchase Provisioned Throughput for the custom model.
D. Grant access to the custom model in Amazon Bedrock.

Answer: B

Explanation:
To use a custom model that has been trained to improve summarization quality, the company must deploy the model on an Amazon SageMaker endpoint. This allows the model to be used for real-time inference through Amazon Bedrock or other AWS services. By deploying the model in SageMaker, the custom model can be accessed programmatically via API calls, enabling integration with Amazon Bedrock.
Option B (Correct): "Deploy the custom model in an Amazon SageMaker endpoint for real-time inference": This is the correct answer because deploying the model on SageMaker enables it to serve real-time predictions and be integrated with Amazon Bedrock.
Option A: "Purchase Provisioned Throughput for the custom model" is incorrect because provisioned throughput is related to database or storage services, not model deployment.
Option C: "Register the model with the Amazon SageMaker Model Registry" is incorrect because while the model registry helps with model management, it does not make the model accessible for real-time inference.
Option D: "Grant access to the custom model in Amazon Bedrock" is incorrect because Bedrock does not directly manage custom model access; it relies on deployed endpoints like those in SageMaker.
AWS AI Practitioner Reference:
Amazon SageMaker Endpoints: AWS recommends deploying models to SageMaker endpoints to use them for real-time inference in various applications.

NEW QUESTION # 26
An accounting firm wants to implement a large language model (LLM) to automate document processing.
The firm must proceed responsibly to avoid potential harms.
What should the firm do when developing and deploying the LLM? (Select TWO.)

A. Avoid overfitting on the training data.
B. Include fairness metrics for model evaluation.
C. Modify the training data to mitigate bias.
D. Apply prompt engineering techniques.
E. Adjust the temperature parameter of the model.

Answer: B,C

Explanation:
To implement a large language model (LLM) responsibly, the firm should focus on fairness and mitigating bias, which are critical for ethical AI deployment.
* A. Include Fairness Metrics for Model Evaluation:
* Fairness metrics help ensure that the model's predictions are unbiased and do not unfairly discriminate against any group.
* These metrics can measure disparities in model outcomes across different demographic groups, ensuring responsible AI practices.
* C. Modify the Training Data to Mitigate Bias:
* Adjusting training data to be more representative and balanced can help reduce bias in the model's predictions.
* Mitigating bias at the data level ensures that the model learns from a diverse and fair dataset, reducing potential harms in deployment.
* Why Other Options are Incorrect:
* B. Adjust the temperature parameter of the model: Controls randomness in outputs but does not directly address fairness or bias.
* D. Avoid overfitting on the training data: Important for model generalization but not directly related to responsible AI practices regarding fairness and bias.
* E. Apply prompt engineering techniques: Useful for improving model outputs but not specifically for mitigating bias or ensuring fairness.

NEW QUESTION # 27
An ecommerce company wants to improve search engine recommendations by customizing the results for each user of the company's ecommerce platform. Which AWS service meets these requirements?

A. Amazon Personalize
B. Amazon Transcribe
C. Amazon Rekognition
D. Amazon Kendra

Answer: A

Explanation:
The ecommerce company wants to improve search engine recommendations by customizing results for each user. Amazon Personalize is a machine learning service that enables personalized recommendations, tailoring search results or product suggestions based on individual user behavior and preferences, making it the best fit for this requirement.
Exact Extract from AWS AI Documents:
From the Amazon Personalize Developer Guide:
"Amazon Personalize enables developers to build applications with personalized recommendations, such as customized search results or product suggestions, by analyzing user behavior and preferences to deliver tailored experiences." (Source: Amazon Personalize Developer Guide, Introduction to Amazon Personalize) Detailed Explanation:
* Option A: Amazon PersonalizeThis is the correct answer. Amazon Personalize specializes in creating personalized recommendations, ideal for customizing search results for each user on an ecommerce platform.
* Option B: Amazon KendraAmazon Kendra is an intelligent search service for enterprise data, focusing on retrieving relevant documents or answers, not on personalizing search results for individual users.
* Option C: Amazon RekognitionAmazon Rekognition is for image and video analysis, such as object detection or facial recognition, and is unrelated to search engine recommendations.
* Option D: Amazon TranscribeAmazon Transcribe converts speech to text, which is not relevant for improving search engine recommendations.
References:
Amazon Personalize Developer Guide: Introduction to Amazon Personalize (https://docs.aws.amazon.com
/personalize/latest/dg/what-is-personalize.html)
AWS AI Practitioner Learning Path: Module on Recommendation Systems
AWS Documentation: Personalization with Amazon Personalize (https://aws.amazon.com/personalize/)

NEW QUESTION # 28
A medical company wants to develop an AI application that can access structured patient records, extract relevant information, and generate concise summaries.
Which solution will meet these requirements?

A. Use Amazon Textract to convert scanned documents into digital text. Design a keyword extraction system to generate summaries.
B. Use Amazon Comprehend Medical to extract relevant medical entities and relationships. Apply rule-based logic to structure and format summaries.
C. Implement Amazon Kendra to provide a searchable index for medical records. Use a template-based system to format summaries.
D. Use Amazon Personalize to analyze patient engagement patterns. Integrate the output with a general purpose text summarization tool.

Answer: B

NEW QUESTION # 29
An AI practitioner wants to use a foundation model (FM) to design a search application. The search application must handle queries that have text and images.
Which type of FM should the AI practitioner use to power the search application?

A. Multi-modal embedding model
B. Image generation model
C. Multi-modal generation model
D. Text embedding model

Answer: A

NEW QUESTION # 30
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SAMPLE AIF-C01 QUESTIONS, GUIDE AIF-C01 TORRENT

Sample AIF-C01 Questions, Guide AIF-C01 Torrent