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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	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 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	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 5	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.

Amazon AWS Certified AI Practitioner Sample Questions (Q24-Q29):

NEW QUESTION # 24
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. Amazon Personalize
B. Amazon SageMaker JumpStart
C. Amazon SageMaker endpoints
D. PartyRock, an Amazon Bedrock Playground

Answer: C

NEW QUESTION # 25
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. Use domain adaptation fine-tuning to adapt the FM to complex scientific terms.
B. Change the FM inference parameters.
C. Use few-shot prompting to define how the FM can answer the questions.
D. Clean the research paper data to remove complex scientific terms.

Answer: A

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 References:
* 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 # 26
A company is using a pre-trained large language model (LLM) to build a chatbot for product recommendations. The company needs the LLM outputs to be short and written in a specific language.
Which solution will align the LLM response quality with the company's expectations?

A. Increase the temperature.
B. Increase the Top K value.
C. Adjust the prompt.
D. Choose an LLM of a different size.

Answer: C

Explanation:
Adjusting the prompt is the correct solution to align the LLM outputs with the company's expectations for short, specific language responses.
Adjust the Prompt:
Modifying the prompt can guide the LLM to produce outputs that are shorter and tailored to the desired language.
A well-crafted prompt can provide specific instructions to the model, such as "Answer in a short sentence in Spanish." Why Option A is Correct:
Control Over Output: Adjusting the prompt allows for direct control over the style, length, and language of the LLM outputs.
Flexibility: Prompt engineering is a flexible approach to refining the model's behavior without modifying the model itself.
Why Other Options are Incorrect:
B: Choose an LLM of a different size: The model size does not directly impact the response length or language.
C: Increase the temperature: Increases randomness in responses but does not ensure brevity or specific language.
D: Increase the Top K value: Affects diversity in model output but does not align directly with response length or language specificity.

NEW QUESTION # 27
A company wants to use language models to create an application for inference on edge devices. The inference must have the lowest latency possible.
Which solution will meet these requirements?

A. Deploy optimized small language models (SLMs) on edge devices.
B. Incorporate a centralized small language model (SLM) API for asynchronous communication with edge devices.
C. Deploy optimized large language models (LLMs) on edge devices.
D. Incorporate a centralized large language model (LLM) API for asynchronous communication with edge devices.

Answer: A

Explanation:
To achieve the lowest latency possible for inference on edge devices, deploying optimized small language models (SLMs) is the most effective solution. SLMs require fewer resources and have faster inference times, making them ideal for deployment on edge devices where processing power and memory are limited.
* Option A (Correct): "Deploy optimized small language models (SLMs) on edge devices": This is the correct answer because SLMs provide fast inference with low latency, which is crucial for edge deployments.
* Option B: "Deploy optimized large language models (LLMs) on edge devices" is incorrect because LLMs are resource-intensive and may not perform well on edge devices due to their size and computational demands.
* Option C: "Incorporate a centralized small language model (SLM) API for asynchronous communication with edge devices" is incorrect because it introduces network latency due to the need for communication with a centralized server.
* Option D: "Incorporate a centralized large language model (LLM) API for asynchronous communication with edge devices" is incorrect for the same reason, with even greater latency due to the larger model size.
AWS AI Practitioner References:
* Optimizing AI Models for Edge Devices on AWS: AWS recommends using small, optimized models for edge deployments to ensure minimal latency and efficient performance.

NEW QUESTION # 28
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 SageMaker Serverless Inference to deploy the model.
B. Use AWS Batch to host the model and serve predictions.
C. Use Amazon API Gateway to host the model and serve predictions.
D. Use Amazon CloudFront to deploy the model.

Answer: A

NEW QUESTION # 29
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AIF-C01 PRACTICE ONLINE | AIF-C01 LATEST DUMPS FILES

AIF-C01 Practice Online | AIF-C01 Latest Dumps Files