TRAINING AMAZON AIF-C01 SOLUTIONS, AIF-C01 RELIABLE TEST TUTORIAL

Training Amazon AIF-C01 Solutions, AIF-C01 Reliable Test Tutorial

Training Amazon AIF-C01 Solutions, AIF-C01 Reliable Test Tutorial

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Amazon AIF-C01 Exam Syllabus Topics:

TopicDetails
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
  • 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 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 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 5
  • 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.

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Amazon AWS Certified AI Practitioner Sample Questions (Q18-Q23):

NEW QUESTION # 18
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 examples of text passages with corresponding positive or negative labels in the prompt followed by the new text passage to be classified.
  • B. Provide the new text passage with a few examples of unrelated tasks, such as text summarization or question answering.
  • C. Provide a detailed explanation of sentiment analysis and how LLMs work in the prompt.
  • D. Provide the new text passage to be classified without any additional context or examples.

Answer: A


NEW QUESTION # 19
Which option is a benefit of using Amazon SageMaker Model Cards to document AI models?

  • A. Standardizing information about a model's purpose, performance, and limitations.
  • B. Physically storing models for archival purposes.
  • C. Reducing the overall computational requirements of a model.
  • D. Providing a visually appealing summary of a model's capabilities.

Answer: A

Explanation:
Amazon SageMaker Model Cards provide a standardized way to document important details about an AI model, such as its purpose, performance, intended usage, and known limitations. This enables transparency and compliance while fostering better communication between stakeholders. It does not store models physically or optimize computational requirements. References: AWS SageMaker Model Cards Documentation.


NEW QUESTION # 20
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. Choose an LLM of a different size.
  • B. Increase the Top K value.
  • C. Adjust the prompt.
  • D. Increase the temperature.

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 # 21
A company built an AI-powered resume screening system. The company used a large dataset to train the model. The dataset contained resumes that were not representative of all demographics. Which core dimension of responsible AI does this scenario present?

  • A. Explainability.
  • B. Privacy and security.
  • C. Fairness.
  • D. Transparency.

Answer: C

Explanation:
Fairness refers to the absence of bias in AI models. Using non-representative datasets leads to biased predictions, affecting specific demographics unfairly. Explainability, privacy, and transparency are important but not directly related to this scenario. References: AWS Responsible AI Framework.


NEW QUESTION # 22
A company is using an Amazon Bedrock base model to summarize documents for an internal use case. The company trained a custom model to improve the summarization quality.
Which action must the company take to use the custom model through Amazon Bedrock?

  • A. Deploy the custom model in an Amazon SageMaker endpoint for real-time inference.
  • B. Register the model with the Amazon SageMaker Model Registry.
  • C. Purchase Provisioned Throughput for the custom model.
  • D. Grant access to the custom model in Amazon Bedrock.

Answer: A


NEW QUESTION # 23
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