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Amazon AIF-C01 Exam Syllabus Topics:

Topic
Details

Topic 1

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 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 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

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.

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Amazon AWS Certified AI Practitioner Sample Questions (Q23-Q28):

NEW QUESTION # 23
A company has developed an ML model for image classification. The company wants to deploy the model to production so that a web application can use the model.
The company needs to implement a solution to host the model and serve predictions without managing any of the underlying infrastructure.
Which solution will meet these requirements?

A. Use Amazon API Gateway to host the model and serve predictions.
B. Use Amazon SageMaker Serverless Inference to deploy the model.
C. Use Amazon CloudFront to deploy the model.
D. Use AWS Batch to host the model and serve predictions.

Answer: B

Explanation:
Amazon SageMaker Serverless Inference is the correct solution for deploying an ML model to production in a way that allows a web application to use the model without the need to manage the underlying infrastructure.
* Amazon SageMaker Serverless Inference provides a fully managed environment for deploying machine learning models. It automatically provisions, scales, and manages the infrastructure required to host the model, removing the need for the company to manage servers or other underlying infrastructure.
* Why Option A is Correct:
* No Infrastructure Management: SageMaker Serverless Inference handles the infrastructure management for deploying and serving ML models. The company can simply provide the model and specify the required compute capacity, and SageMaker will handle the rest.
* Cost-Effectiveness: The serverless inference option is ideal for applications with intermittent or unpredictable traffic, as the company only pays for the compute time consumed while handling requests.
* Integration with Web Applications: This solution allows the model to be easily accessed by web applications via RESTful APIs, making it an ideal choice for hosting the model and serving predictions.
* Why Other Options are Incorrect:
* B. Use Amazon CloudFront to deploy the model: CloudFront is a content delivery network (CDN) service for distributing content, not for deploying ML models or serving predictions.
* C. Use Amazon API Gateway to host the model and serve predictions: API Gateway is used for creating, deploying, and managing APIs, but it does not provide the infrastructure or the required environment to host and run ML models.
* D. Use AWS Batch to host the model and serve predictions: AWS Batch is designed for running batch computing workloads and is not optimized for real-time inference or hosting machine learning models.
Thus, A is the correct answer, as it aligns with the requirement of deploying an ML model without managing any underlying infrastructure.

NEW QUESTION # 24
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

Answer:

Explanation:

Reference:
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/)

NEW QUESTION # 25
Which AWS service or feature can help an AI development team quickly deploy and consume a foundation model (FM) within the team's VPC?

A. PartyRock, an Amazon Bedrock Playground
B. Amazon Personalize
C. Amazon SageMaker endpoints
D. Amazon SageMaker JumpStart

Answer: C

NEW QUESTION # 26
A research company implemented a chatbot by using a foundation model (FM) from Amazon Bedrock. The chatbot searches for answers to questions from a large database of research papers.
After multiple prompt engineering attempts, the company notices that the FM is performing poorly because of the complex scientific terms in the research papers.
How can the company improve the performance of the chatbot?

A. Clean the research paper data to remove complex scientific terms.
B. Change the FM inference parameters.
C. Use domain adaptation fine-tuning to adapt the FM to complex scientific terms.
D. Use few-shot prompting to define how the FM can answer the questions.

Answer: C

Explanation:
Domain adaptation fine-tuning involves training a foundation model (FM) further using a specific dataset that includes domain-specific terminology and content, such as scientific terms in research papers. This process allows the model to better understand and handle complex terminology, improving its performance on specialized tasks.
Option B (Correct): "Use domain adaptation fine-tuning to adapt the FM to complex scientific terms": This is the correct answer because fine-tuning the model on domain-specific data helps it learn and adapt to the specific language and terms used in the research papers, resulting in better performance.
Option A: "Use few-shot prompting to define how the FM can answer the questions" is incorrect because while few-shot prompting can help in certain scenarios, it is less effective than fine-tuning for handling complex domain-specific terms.
Option C: "Change the FM inference parameters" is incorrect because adjusting inference parameters will not resolve the issue of the model's lack of understanding of complex scientific terminology.
Option D: "Clean the research paper data to remove complex scientific terms" is incorrect because removing the complex terms would result in the loss of important information and context, which is not a viable solution.
AWS AI Practitioner Reference:
Domain Adaptation in Amazon Bedrock: AWS recommends fine-tuning models with domain-specific data to improve their performance on specialized tasks involving unique terminology.

NEW QUESTION # 27
A company deployed an AI/ML solution to help customer service agents respond to frequently asked questions. The questions can change over time. The company wants to give customer service agents the ability to ask questions and receive automatically generated answers to common customer questions. Which strategy will meet these requirements MOST cost-effectively?

A. Fine-tune the model regularly.
B. Train the model by using context data.
C. Use Retrieval Augmented Generation (RAG) with prompt engineering techniques.
D. Pre-train and benchmark the model by using context data.

Answer: C

Explanation:
RAG combines large pre-trained models with retrieval mechanisms to fetch relevant context from a knowledge base. This approach is cost-effective as it eliminates the need for frequent model retraining while ensuring responses are contextually accurate and up to date. Reference: AWS RAG Techniques.

NEW QUESTION # 28
......

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