AIF-C01 RELIABLE REAL TEST, LATEST AIF-C01 TEST TESTKING

AIF-C01 Reliable Real Test, Latest AIF-C01 Test Testking

AIF-C01 Reliable Real Test, Latest AIF-C01 Test Testking

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Tags: AIF-C01 Reliable Real Test, Latest AIF-C01 Test Testking, New AIF-C01 Test Blueprint, AIF-C01 Reliable Test Labs, Sample AIF-C01 Questions

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Amazon AIF-C01 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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
  • 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 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
  • 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.

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AIF-C01 Learning Materials: AWS Certified AI Practitioner - AIF-C01 Actual Lab Questions

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Amazon AWS Certified AI Practitioner Sample Questions (Q60-Q65):

NEW QUESTION # 60
An AI practitioner has built a deep learning model to classify the types of materials in images. The AI practitioner now wants to measure the model performance.
Which metric will help the AI practitioner evaluate the performance of the model?

  • A. Confusion matrix
  • B. Mean squared error (MSE)
  • C. R2 score
  • D. Correlation matrix

Answer: A


NEW QUESTION # 61
A company has installed a security camer
a. The company uses an ML model to evaluate the security camera footage for potential thefts. The company has discovered that the model disproportionately flags people who are members of a specific ethnic group.
Which type of bias is affecting the model output?

  • A. Measurement bias
  • B. Confirmation bias
  • C. Observer bias
  • D. Sampling bias

Answer: D


NEW QUESTION # 62
An AI practitioner trained a custom model on Amazon Bedrock by using a training dataset that contains confidential data. The AI practitioner wants to ensure that the custom model does not generate inference responses based on confidential data.
How should the AI practitioner prevent responses based on confidential data?

  • A. Delete the custom model. Remove the confidential data from the training dataset. Retrain the custom model.
  • B. Mask the confidential data in the inference responses by using dynamic data masking.
  • C. Encrypt the confidential data in the custom model by using AWS Key Management Service (AWS KMS).
  • D. Encrypt the confidential data in the inference responses by using Amazon SageMaker.

Answer: A

Explanation:
When a model is trained on a dataset containing confidential or sensitive data, the model may inadvertently learn patterns from this data, which could then be reflected in its inference responses. To ensure that a model does not generate responses based on confidential data, the most effective approach is to remove the confidential data from the training dataset and then retrain the model.
Explanation of Each Option:
* Option A (Correct): "Delete the custom model. Remove the confidential data from the training dataset.
Retrain the custom model."This option is correct because it directly addresses the core issue: the model has been trained on confidential data. The only way to ensure that the model does not produce inferences based on this data is to remove the confidential information from the training dataset and then retrain the model from scratch. Simply deleting the model and retraining it ensures that no confidential data is learned or retained by the model. This approach follows the best practices recommended by AWS for handling sensitive data when using machine learning services like Amazon Bedrock.
* Option B: "Mask the confidential data in the inference responses by using dynamic data masking."This option is incorrect because dynamic data masking is typically used to mask or obfuscate sensitive data in a database. It does not address the core problem of the model being trained on confidential data.
Masking data in inference responses does not prevent the model from using confidential data it learned during training.
* Option C: "Encrypt the confidential data in the inference responses by using Amazon SageMaker."This option is incorrect because encrypting the inference responses does not prevent the model from generating outputs based on confidential data. Encryption only secures the data at rest or in transit but does not affect the model's underlying knowledge or training process.
* Option D: "Encrypt the confidential data in the custom model by using AWS Key Management Service (AWS KMS)."This option is incorrect as well because encrypting the data within the model does not prevent the model from generating responses based on the confidential data it learned during training.
AWS KMS can encrypt data, but it does not modify the learning that the model has already performed.
AWS AI Practitioner References:
* Data Handling Best Practices in AWS Machine Learning: AWS advises practitioners to carefully handle training data, especially when it involves sensitive or confidential information. This includes preprocessing steps like data anonymization or removal of sensitive data before using it to train machine learning models.
* Amazon Bedrock and Model Training Security: Amazon Bedrock provides foundational models and customization capabilities, but any training involving sensitive data should follow best practices, such as removing or anonymizing confidential data to prevent unintended data leakage.


NEW QUESTION # 63
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 Data Exchange
  • C. AWS Artifact
  • D. AWS Audit Manager

Answer: C


NEW QUESTION # 64
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. Hallucination
  • B. Data leakage
  • C. Underfitting
  • D. Overfitting

Answer: A


NEW QUESTION # 65
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