AIF-C01 RELIABLE TEST FORUM | STUDY AIF-C01 GROUP

AIF-C01 Reliable Test Forum | Study AIF-C01 Group

AIF-C01 Reliable Test Forum | Study AIF-C01 Group

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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 (Q57-Q62):

NEW QUESTION # 57
An AI practitioner is building a model to generate images of humans in various professions. The AI practitioner discovered that the input data is biased and that specific attributes affect the image generation and create bias in the model.
Which technique will solve the problem?

  • A. Retrieval Augmented Generation (RAG)
  • B. Data augmentation for imbalanced classes
  • C. Model monitoring for class distribution
  • D. Watermark detection for images

Answer: B

Explanation:
Data augmentation for imbalanced classes is the correct technique to address bias in input data affecting image generation.
* Data Augmentation for Imbalanced Classes:
* Involves generating new data samples by modifying existing ones, such as flipping, rotating, or cropping images, to balance the representation of different classes.
* Helps mitigate bias by ensuring that the training data is more representative of diverse characteristics and scenarios.
* Why Option A is Correct:
* Balances Data Distribution: Addresses class imbalance by augmenting underrepresented classes, which reduces bias in the model.
* Improves Model Fairness: Ensures that the model is exposed to a more diverse set of training examples, promoting fairness in image generation.
* Why Other Options are Incorrect:
* B. Model monitoring for class distribution: Helps identify bias but does not actively correct it.
* C. Retrieval Augmented Generation (RAG): Involves combining retrieval and generation but is unrelated to mitigating bias in image generation.
* D. Watermark detection for images: Detects watermarks in images, not a technique for addressing bias.


NEW QUESTION # 58
Which strategy evaluates the accuracy of a foundation model (FM) that is used in image classification tasks?

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

Answer: B

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 # 59
A company wants to display the total sales for its top-selling products across various retail locations in the past 12 months.
Which AWS solution should the company use to automate the generation of graphs?

  • A. Amazon Q Developer
  • B. Amazon Q in Amazon EC2
  • C. Amazon Q in Amazon QuickSight
  • D. Amazon Q in AWS Chatbot

Answer: C


NEW QUESTION # 60
A company has terabytes of data in a database that the company can use for business analysis. The company wants to build an AI-based application that can build a SQL query from input text that employees provide. The employees have minimal experience with technology.
Which solution meets these requirements?

  • A. WaveNet
  • B. Support vector machine
  • C. Generative pre-trained transformers (GPT)
  • D. Residual neural network

Answer: C


NEW QUESTION # 61
A company wants to build an interactive application for children that generates new stories based on classic stories. The company wants to use Amazon Bedrock and needs to ensure that the results and topics are appropriate for children.
Which AWS service or feature will meet these requirements?

  • A. Amazon Bedrock playgrounds
  • B. Guardrails for Amazon Bedrock
  • C. Amazon Rekognition
  • D. Agents for Amazon Bedrock

Answer: B

Explanation:
Amazon Bedrock is a service that provides foundational models for building generative AI applications.
When creating an application for children, it is crucial to ensure that the generated content is appropriate for the target audience. "Guardrails" in Amazon Bedrock provide mechanisms to control the outputs and topics of generated content to align with desired safety standards and appropriateness levels.
* Option C (Correct): "Guardrails for Amazon Bedrock": This is the correct answer because guardrails are specifically designed to help users enforce content moderation, filtering, and safety checks on the outputs generated by models in Amazon Bedrock. For a children's application, guardrails ensure that all content generated is suitable and appropriate for the intended audience.
* Option A: "Amazon Rekognition" is incorrect. Amazon Rekognition is an image and video analysis service that can detect inappropriate content in images or videos, but it does not handle text or story generation.
* Option B: "Amazon Bedrock playgrounds" is incorrect because playgrounds are environments for experimenting and testing model outputs, but they do not inherently provide safeguards to ensure content appropriateness for specific audiences, such as children.
* Option D: "Agents for Amazon Bedrock" is incorrect. Agents in Amazon Bedrock facilitate building AI applications with more interactive capabilities, but they do not provide specific guardrails for ensuring content appropriateness for children.
AWS AI Practitioner References:
* Guardrails in Amazon Bedrock: Designed to help implement controls that ensure generated content is safe and suitable for specific use cases or audiences, such as children, by moderating and filtering inappropriate or undesired content.
* Building Safe AI Applications: AWS provides guidance on implementing ethical AI practices, including using guardrails to protect against generating inappropriate or biased content.


NEW QUESTION # 62
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