AIF-C01 VALID EXAM ANSWERS & AIF-C01 PRACTICE ENGINE & AIF-C01 TRAINING PDF

AIF-C01 valid exam answers & AIF-C01 practice engine & AIF-C01 training pdf

AIF-C01 valid exam answers & AIF-C01 practice engine & AIF-C01 training pdf

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Amazon AIF-C01 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • 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 4
  • 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 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.

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Amazon AWS Certified AI Practitioner Sample Questions (Q58-Q63):

NEW QUESTION # 58
An AI practitioner is using a large language model (LLM) to create content for marketing campaigns. The generated content sounds plausible and factual but is incorrect.
Which problem is the LLM having?

  • A. Overfitting
  • B. Data leakage
  • C. Underfitting
  • D. Hallucination

Answer: D


NEW QUESTION # 59
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. Incorporate a centralized large language model (LLM) API for asynchronous communication with edge devices.
  • B. Incorporate a centralized small language model (SLM) API for asynchronous communication with edge devices.
  • C. Deploy optimized small language models (SLMs) on edge devices.
  • D. Deploy optimized large language models (LLMs) on edge devices.

Answer: C


NEW QUESTION # 60
Which term describes the numerical representations of real-world objects and concepts that AI and natural language processing (NLP) models use to improve understanding of textual information?

  • A. Binaries
  • B. Embeddings
  • C. Tokens
  • D. Models

Answer: B


NEW QUESTION # 61
An AI company periodically evaluates its systems and processes with the help of independent software vendors (ISVs). The company needs to receive email message notifications when an ISV's compliance reports become available.
Which AWS service can the company use to meet this requirement?

  • A. AWS Trusted Advisor
  • B. AWS Audit Manager
  • C. AWS Data Exchange
  • D. AWS Artifact

Answer: D


NEW QUESTION # 62
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. Measure the model's accuracy against a predefined benchmark dataset.
  • C. Calculate the total cost of resources used by the model.
  • D. Assess the color accuracy of images processed 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 # 63
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