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AIF-C01 Practice Questions

Question # 1
A company has built a chatbot that can respond to natural language questions with images. The company wants to ensure that the chatbot does not return inappropriate or unwanted images.
Which solution will meet these requirements?
A. Implement moderation APIs.
B. Retrain the model with a general public dataset.
C. Perform model validation.
D. Automate user feedback integration.


A. Implement moderation APIs.


Question # 2
A company has built a solution by using generative AI. The solution uses large language models (LLMs) to translate training manuals from English into other languages. The company wants to evaluate the accuracy of the solution by examining the text generated for the manuals.
Which model evaluation strategy meets these requirements?
A. Bilingual Evaluation Understudy (BLEU)
B. Root mean squared error (RMSE)
C. Recall-Oriented Understudy for Gisting Evaluation (ROUGE)
D. F1 score


A. Bilingual Evaluation Understudy (BLEU)


Question # 3
A company has a foundation model (FM) that was customized by using Amazon Bedrock to answer customer queries about products. The company wants to validate the model's responses to new types of queries. The company needs to upload a new dataset that Amazon Bedrock can use for validation.
Which AWS service meets these requirements?
A. Amazon S3
B. Amazon Elastic Block Store (Amazon EBS)
C. Amazon Elastic File System (Amazon EFS)
D. AWS Snowcone


A. Amazon S3


Question # 4
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. Data leakage
B. Hallucination
C. Overfitting
D. Underfitting


B. Hallucination


Question # 5
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 inference responses by using Amazon SageMaker.
D. Encrypt the confidential data in the custom model by using AWS Key Management Service (AWS KMS).


A. Delete the custom model. Remove the confidential data from the training dataset. Retrain the custom model.


Question # 6
A company is building a chatbot to improve user experience. The company is using a large language model (LLM) from Amazon Bedrock for intent detection. The company wants to use few-shot learning to improve intent detection accuracy.

Which additional data does the company need to meet these requirements?
A. Pairs of chatbot responses and correct user intents
B. Pairs of user messages and correct chatbot responses
C. Pairs of user messages and correct user intents
D. Pairs of user intents and correct chatbot responses


C. Pairs of user messages and correct user intents


Question # 7
A company is using domain-specific models. The company wants to avoid creating new models from the beginning. The company instead wants to adapt pre-trained models to create models for new, related tasks.
Which ML strategy meets these requirements?
A. Increase the number of epochs.
B. Use transfer learning.
C. Decrease the number of epochs.
D. Use unsupervised learning.


B. Use transfer learning.


Question # 8
An education provider is building a question and answer application that uses a generative AI model to explain complex concepts. The education provider wants to automatically change the style of the model response depending on who is asking the question. The education provider will give the model the age range of the user who has asked the question.
Which solution meets these requirements with the LEAST implementation effort?
A. Fine-tune the model by using additional training data that is representative of the various age ranges that the application will support.
B. Add a role description to the prompt context that instructs the model of the age range that the response should target.
C. Use chain-of-thought reasoning to deduce the correct style and complexity for a response suitable for that user.
D. Summarize the response text depending on the age of the user so that younger users receive shorter responses.


B. Add a role description to the prompt context that instructs the model of the age range that the response should target.


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Amazon Web Services AIF-C01 Exam Dumps

Exam Name: AWS Certified AI Practitioner Exam(AI1-C01)
Certification Name: AWS Certified AI Practitioner

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