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Amazon AIF-C01 Exam Syllabus Topics:

Topic	Details
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	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	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	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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Amazon AWS Certified AI Practitioner Sample Questions (Q38-Q43):

NEW QUESTION # 38
An accounting firm wants to implement a large language model (LLM) to automate document processing. The firm must proceed responsibly to avoid potential harms.
What should the firm do when developing and deploying the LLM? (Select TWO.)

A. Avoid overfitting on the training data.
B. Adjust the temperature parameter of the model.
C. Modify the training data to mitigate bias.
D. Include fairness metrics for model evaluation.
E. Apply prompt engineering techniques.

Answer: C,D

Explanation:
To implement a large language model (LLM) responsibly, the firm should focus on fairness and mitigating bias, which are critical for ethical AI deployment.
A . Include Fairness Metrics for Model Evaluation:
Fairness metrics help ensure that the model's predictions are unbiased and do not unfairly discriminate against any group.
These metrics can measure disparities in model outcomes across different demographic groups, ensuring responsible AI practices.
C . Modify the Training Data to Mitigate Bias:
Adjusting training data to be more representative and balanced can help reduce bias in the model's predictions.
Mitigating bias at the data level ensures that the model learns from a diverse and fair dataset, reducing potential harms in deployment.
Why Other Options are Incorrect:
B . Adjust the temperature parameter of the model: Controls randomness in outputs but does not directly address fairness or bias.
D . Avoid overfitting on the training data: Important for model generalization but not directly related to responsible AI practices regarding fairness and bias.
E . Apply prompt engineering techniques: Useful for improving model outputs but not specifically for mitigating bias or ensuring fairness.

NEW QUESTION # 39
An AI practitioner is using an Amazon Bedrock base model to summarize session chats from the customer service department. The AI practitioner wants to store invocation logs to monitor model input and output dat a.

A. Configure model invocation logging in Amazon EventBridge.
B. Configure AWS CloudTrail as the logs destination for the model.
C. Configure AWS Audit Manager as the logs destination for the model.
D. Enable model invocation logging in Amazon Bedrock.

Answer: D

NEW QUESTION # 40
A company wants to collaborate with several research institutes to develop an AI model. The company needs standardized documentation of model version tracking and a record of model development.
Which solution meets these requirements?

A. Track the model changes by using Amazon SageMaker Model Cards.
B. Track the model changes by using Amazon Comprehend.
C. Track the model changes by using Git.
D. Track the model changes by using Amazon Fraud Detector.

Answer: A

Explanation:
Amazon SageMaker Model Cards provide a standardized way to document and track model information, including versions and performance. According to AWS documentation:
"SageMaker Model Cards provide a single source of truth for model information including intended use, training details, evaluation metrics, and ethical considerations to support governance and collaboration."

NEW QUESTION # 41
A financial company uses a generative AI model to assign credit limits to new customers. The company wants to make the decision-making process of the model more transparent to its customers.

A. Apply explainable AI techniques to show customers which factors influenced the model's decision
B. Increase the accuracy of the model to reduce the need for transparency
C. Use a rule-based system instead of an ML model
D. Develop an interactive UI for customers and provide clear technical explanations about the system

Answer: A

Explanation:
* Explainable AI (XAI) techniques such as SHAP (SHapley values) or feature attribution provide transparency by showing which input factors influenced decisions.
* A is not scalable for complex use cases.
* C does not guarantee real interpretability.
* D ignores the regulatory need for explainability.
# Reference:
AWS SageMaker Clarify - Explainable AI

NEW QUESTION # 42
A company is training a foundation model (FM). The company wants to increase the accuracy of the model up to a specific acceptance level.
Which solution will meet these requirements?

A. Increase the epochs.
B. Increase the temperature parameter.
C. Decrease the batch size.
D. Decrease the epochs.

Answer: A

Explanation:
Increasing the number of epochs during model training allows the model to learn from the data over more iterations, potentially improving its accuracy up to a certain point. This is a common practice when attempting to reach a specific level of accuracy.
* Option B (Correct): "Increase the epochs": This is the correct answer because increasing epochs allows the model to learn more from the data, which can lead to higher accuracy.
* Option A: "Decrease the batch size" is incorrect as it mainly affects training speed and may lead to overfitting but does not directly relate to achieving a specific accuracy level.
* Option C: "Decrease the epochs" is incorrect as it would reduce the training time, possibly preventing the model from reaching the desired accuracy.
* Option D: "Increase the temperature parameter" is incorrect because temperature affects the randomness of predictions, not model accuracy.
AWS AI Practitioner References:
* Model Training Best Practices on AWS: AWS suggests adjusting training parameters, like the number of epochs, to improve model performance.

NEW QUESTION # 43
......

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Reliable AIF-C01 Test Voucher & Useful Test AIF-C01 Question & Correct Valid AIF-C01 Exam Answers

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Amazon AWS Certified AI Practitioner Sample Questions (Q38-Q43):

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