Free Amazon AIF-C01 Exam Questions
Absolute Free AIF-C01 Exam Practice for Comprehensive Preparation
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Amazon AIF-C01 Exam Questions
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Provided By: Amazon
- Exam: AWS Certified AI Practitioner
- Certification: AWS Certified Foundational
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Total Questions: 595
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Updated On: Jan 14, 2026
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Rated: 4.9 |
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Online Users: 1190
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Question 1
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Which of the following generative AI techniques are used in Amazon Q Business web application workflow? (Select two)
Answer: A,B
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Which of the following generative AI techniques are used in Amazon Q Business web application workflow? (Select two)
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Question 2
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What is a key difference between supervised machine learning and unsupervised machine learning?
Answer: B
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What is a key difference between supervised machine learning and unsupervised machine learning?
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Question 3
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A customer support company is using Amazon Bedrock to automate responses to frequently asked questions through its AI-driven chatbot. The development team is adjusting various inference parameters to control the responses. They are particularly interested in the Response length parameter, since this parameter is critical for providing clear, customer-friendly interactions.
How does the inference parameter Response length influence the model response for Amazon Bedrock?
Answer: D
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A customer support company is using Amazon Bedrock to automate responses to frequently asked questions through its AI-driven chatbot. The development team is adjusting various inference parameters to control the responses. They are particularly interested in the Response length parameter, since this parameter is critical for providing clear, customer-friendly interactions.
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Question 4
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In generative AI, there is a specific concept used to represent words, sub-words, or characters that the model processes as discrete units of text. What is this concept called?
Answer: D
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In generative AI, there is a specific concept used to represent words, sub-words, or characters that the model processes as discrete units of text. What is this concept called?
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Question 5
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A healthcare startup is developing a machine learning model to predict patient outcomes based on historical medical data. During the training process, the data science team notices signs of overfitting, where the model performs well on the training data but struggles with new, unseen data. To ensure the model generalizes effectively and avoids memorizing the training data, the team needs to implement strategies to prevent overfitting.
How can you prevent model-overfitting in machine learning?
Answer: A
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A healthcare startup is developing a machine learning model to predict patient outcomes based on historical medical data. During the training process, the data science team notices signs of overfitting, where the model performs well on the training data but struggles with new, unseen data. To ensure the model generalizes effectively and avoids memorizing the training data, the team needs to implement strategies to prevent overfitting.