AMAZON AIF-C01 LEARNING MODE & INTERACTIVE AIF-C01 COURSE

Amazon AIF-C01 Learning Mode & Interactive AIF-C01 Course

Amazon AIF-C01 Learning Mode & Interactive AIF-C01 Course

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Amazon AIF-C01 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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
  • 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 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
  • 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.

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

NEW QUESTION # 23
A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment analysis. The company needs the LLM to produce more consistent responses to the same input prompt.
Which adjustment to an inference parameter should the company make to meet these requirements?

  • A. Increase the maximum generation length
  • B. Increase the temperature value
  • C. Decrease the length of output tokens
  • D. Decrease the temperature value

Answer: D

Explanation:
The temperature parameter in a large language model (LLM) controls the randomness of the model's output.
A lower temperature value makes the output more deterministic and consistent, meaning that the model is less likely to produce different results for the same input prompt.
* Option A (Correct): "Decrease the temperature value": This is the correct answer because lowering the temperature reduces the randomness of the responses, leading to more consistent outputs for the same input.
* Option B: "Increase the temperature value" is incorrect because it would make the output more random and less consistent.
* Option C: "Decrease the length of output tokens" is incorrect as it does not directly affect the consistency of the responses.
* Option D: "Increase the maximum generation length" is incorrect because this adjustment affects the output length, not the consistency of the model's responses.
AWS AI Practitioner References:
* Understanding Temperature in Generative AI Models: AWS documentation explains that adjusting the temperature parameter affects the model's output randomness, with lower values providing more consistent outputs.


NEW QUESTION # 24
Which strategy evaluates the accuracy of a foundation model (FM) that is used in image classification tasks?

  • A. Count the number of layers in the neural network.
  • B. Assess the color accuracy of images processed by the model.
  • C. Measure the model's accuracy against a predefined benchmark dataset.
  • D. Calculate the total cost of resources used by the model.

Answer: C

Explanation:
Measuring the model's accuracy against a predefined benchmark dataset is the correct strategy to evaluate the accuracy of a foundation model (FM) used in image classification tasks.
* Model Accuracy Evaluation:
* In image classification, the accuracy of a model is typically evaluated by comparing the predicted labels with the true labels in a benchmark dataset that is representative of the real-world data the model will encounter.
* This approach provides a quantifiable measure of how well the model performs on known data and is a standard practice in machine learning.
* Why Option B is Correct:
* Benchmarking Accuracy: Using a predefined dataset allows for consistent and reliable evaluation of model performance.
* Standard Practice: It is a widely accepted method for assessing the effectiveness of image classification models.
* Why Other Options are Incorrect:
* A. Total cost of resources: Does not measure model accuracy but rather the cost of operation.
* C. Number of layers in the neural network: Does not directly correlate with the accuracy or performance of the model.
* D. Color accuracy of images processed by the model: Is unrelated to the model's classification accuracy.


NEW QUESTION # 25
A company wants to make a chatbot to help customers. The chatbot will help solve technical problems without human intervention. The company chose a foundation model (FM) for the chatbot. The chatbot needs to produce responses that adhere to company tone.
Which solution meets these requirements?

  • A. Use batch inferencing to process detailed responses.
  • B. Define a higher number for the temperature parameter.
  • C. Set a low limit on the number of tokens the FM can produce.
  • D. Experiment and refine the prompt until the FM produces the desired responses.

Answer: D


NEW QUESTION # 26
A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment analysis. The company wants to classify the sentiment of text passages as positive or negative.
Which prompt engineering strategy meets these requirements?

  • A. Provide a detailed explanation of sentiment analysis and how LLMs work in the prompt.
  • B. Provide examples of text passages with corresponding positive or negative labels in the prompt followed by the new text passage to be classified.
  • C. Provide the new text passage to be classified without any additional context or examples.
  • D. Provide the new text passage with a few examples of unrelated tasks, such as text summarization or question answering.

Answer: B


NEW QUESTION # 27
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. Change the FM inference parameters.
  • B. Use few-shot prompting to define how the FM can answer the questions.
  • C. Clean the research paper data to remove complex scientific terms.
  • D. Use domain adaptation fine-tuning to adapt the FM to complex scientific terms.

Answer: D

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 # 28
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