Free Amazon AIF-C01 Exam Questions

Become Amazon Certified with updated AIF-C01 exam questions and correct answers

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Total 737 Questions | Updated On: May 24, 2026

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Question 1

A company wants to create a chatbot that answers questions about human resources policies. The company is using a large language model (LLM) and has a large digital documentation base. Which technique should the company use to optimize the generated responses?

A : Use Retrieval Augmented Generation (RAG).

B : Use few-shot prompting.

C : Set the temperature to 1.

D : Decrease the token size.

Answer: A

Question 2

An AI paritioner is developing a new ML model. After training the model, the AI practitionerevaluates the accuracy of the model's predictions. The model's accuracy is low when the model usesboth the training dataset and the test dataset.Which scenario is the MOST likely cause of this problem?

A : Overfitting

B : Hallucination

C : Underfitting

D : Cross-validation

Answer: C

Question 3

How would you differentiate between K-Means and K-Nearest Neighbors (KNN) algorithms in machine learning?

A : K-Means is primarily used for regression tasks, while KNN is used for reducing the dimensionality of data

B : K-Means is an unsupervised learning algorithm used for clustering data points into groups, while KNN is a supervised learning algorithm used for classifying data points based on their proximity to labeled examples

C : K-Means is a supervised learning algorithm used for classification, while KNN is an unsupervised learning algorithm used for clustering

D : K-Means requires labeled data to form clusters, whereas KNN does not use labeled data for making predictions

Answer: B

Question 4

What is an example of structured data?

A : A file of text comments from an online forum

B : A compilation of video files that contains news broadcasts

C : A CSV file that consists of measurement data

D : Transcribed conversations between call center agents and customers

Answer: C

Question 5

An insurance company is transitioning to AWS Cloud and wants to use Amazon Bedrock for product recommendations. The company wants to supplement organization-specific information to the underlying Foundation Model (FM).

Which of the following represents the best-fit solution for the given use case?

A : Implement Reinforcement Learning from Human Feedback (RLHF) in Amazon Bedrock by leveraging the contextual information from the company's private data

B : Use Knowledge Bases for Amazon Bedrock to supplement contextual information from the company's private data to the FM using Reinforcement Learning from Human Feedback (RLHF)

C : Use Knowledge Bases for Amazon Bedrock to supplement contextual information from the company's private data to the FM using Retrieval Augmented Generation (RAG)

D : Fine-tune the base Foundation Model (FM) used by Amazon Bedrock by leveraging the contextual information from the company's private data

Answer: C

Page: 1 / 148

Total 737 Questions | Updated On: May 24, 2026

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