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Free AIF-C01 Dumps | Reliable AIF-C01 Braindumps Ppt

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Amazon AIF-C01 Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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 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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Amazon AWS Certified AI Practitioner Sample Questions (Q34-Q39):

NEW QUESTION # 34
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 in Amazon EC2
B. Amazon Q in AWS Chatbot
C. Amazon Q Developer
D. Amazon Q in Amazon QuickSight

Answer: D

NEW QUESTION # 35
A large retail bank wants to develop an ML system to help the risk management team decide on loan allocations for different demographics.
What must the bank do to develop an unbiased ML model?

A. Create a different ML model for each demographic group.
B. Reduce the size of the training dataset.
C. Ensure that the ML model predictions are consistent with historical results.
D. Measure class imbalance on the training dataset. Adapt the training process accordingly.

Answer: D

Explanation:
Class imbalance in a training dataset can cause ML models to favor overrepresented groups, leading to biased predictions. The AWS AI Practitioner guide and SageMaker Clarify documentation emphasize the need to identify and mitigate class imbalance to ensure fairness and unbiased model outcomes.
D is correct: By measuring class imbalance and adapting the training process (e.g., through oversampling, undersampling, or using class weights), organizations can improve fairness and reduce bias across demographic groups.
A (reducing data size) could worsen bias by removing potentially useful diverse data.
B (consistency with historical results) might reinforce existing biases.
C (separate models) is not scalable and can introduce other fairness issues.
"To reduce bias, examine class imbalance in your training data and use techniques to ensure all groups are fairly represented." (Reference: AWS SageMaker Clarify: Mitigating Bias, AWS Responsible AI)
"To reduce bias, examine class imbalance in your training data and use techniques to ensure all groups are fairly represented." (Reference: AWS SageMaker Clarify: Mitigating Bias, AWS Responsible AI)

NEW QUESTION # 36
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 Fraud Detector.
C. Track the model changes by using Amazon Comprehend.
D. Track the model changes by using Git.

Answer: A

NEW QUESTION # 37
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. Decrease the batch size.
B. Decrease the epochs.
C. Increase the epochs.
D. Increase the temperature parameter.

Answer: C

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 Reference:
Model Training Best Practices on AWS: AWS suggests adjusting training parameters, like the number of epochs, to improve model performance.

NEW QUESTION # 38
A loan company is building a generative AI-based solution to offer new applicants discounts based on specific business criteria. The company wants to build and use an AI model responsibly to minimize bias that could negatively affect some customers.
Which actions should the company take to meet these requirements? (Select TWO.)

A. Ensure that the model runs frequently.
B. Use the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) technique to ensure that the model is 100% accurate.
C. Evaluate the model's behavior so that the company can provide transparency to stakeholders.
D. Detect imbalances or disparities in the data.
E. Ensure that the model's inference time is within the accepted limits.

Answer: C,D

Explanation:
To build an AI model responsibly and minimize bias, it is essential to ensure fairness and transparency throughout the model development and deployment process. This involves detecting and mitigating data imbalances and thoroughly evaluating the model's behavior to understand its impact on different groups.
* Option A (Correct): "Detect imbalances or disparities in the data": This is correct because identifying and addressing data imbalances or disparities is a critical step in reducing bias. AWS provides tools like Amazon SageMaker Clarify to detect bias during data preprocessing and model training.
* Option C (Correct): "Evaluate the model's behavior so that the company can provide transparency to stakeholders": This is correct because evaluating the model's behavior for fairness and accuracy is key to ensuring that stakeholders understand how the model makes decisions.
Transparency is a crucial aspect of responsible AI.
* Option B: "Ensure that the model runs frequently" is incorrect because the frequency of model runs does not address bias.
* Option D: "Use the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) technique to ensure that the model is 100% accurate" is incorrect because ROUGE is a metric for evaluating the quality of text summarization models, not for minimizing bias.
* Option E: "Ensure that the model's inference time is within the accepted limits" is incorrect as it relates to performance, not bias reduction.
AWS AI Practitioner References:
* Amazon SageMaker Clarify: AWS offers tools such as SageMaker Clarify for detecting bias in datasets and models, and for understanding model behavior to ensure fairness and transparency.
* Responsible AI Practices: AWS promotes responsible AI by advocating for fairness, transparency, and inclusivity in model development and deployment.

NEW QUESTION # 39
......

Actual AWS Certified AI Practitioner (AIF-C01) dumps are designed to help applicants crack the Amazon AIF-C01 test in a short time. There are dozens of websites that offer AIF-C01 exam questions. But all of them are not trustworthy. Some of these platforms may provide you with AWS Certified AI Practitioner (AIF-C01) invalid dumps. Upon using outdated Amazon AIF-C01 dumps you fail in the AWS Certified AI Practitioner (AIF-C01) test and lose your resources.

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Paul Smith Paul Smith

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