Amazon AIF-C01 Exam Real Questions

Prepare and pass your AWS Certified AI Practitioner with free AIF-C01 exam questions.

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Total 284 Questions | Updated On: Sep 27, 2024

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Question 1

An AI practitioner has built a deep learning model to classify the types of materials in images. The AI

practitioner now wants to measure the model performance.

Which metric will help the AI practitioner evaluate the performance of the model?

A : Confusion matrix

B : Correlation matrix

C : R2 score

D : Mean squared error (MSE)

Answer: A

Question 2

Which of the following summarizes the differences between Foundation Model (FM) and model customization in the context of generative AI?

A : Both model customization and FM refer to an AI model with a large number of parameters and trained on a massive amount of diverse data

B : Model customization refers to an AI model with a large number of parameters and trained on a massive amount of diverse data, whereas, FM refers to the process of using training data to adjust the model parameter values in a base model to create a custom model

C : Both model customization and FM refer to the process of using training data to adjust the model parameter values in a base model to create a custom model

D : FM is an AI model with a large number of parameters and trained on a massive amount of diverse data, whereas, model customization is the process of using training data to adjust the model parameter values in a base model to create a custom model

Answer: D

Question 3

A company is using Amazon Bedrock and it wants to regulate the percentage of most-likely candidates considered for the next word in the model's output.

Which of the following inference parameters would you recommend for the given use case?

A : Top K

B : Top P

C : Temperature

D : Stop sequences

Answer: A

Question 4

Which of the following represents a best practice in generative AI adoption?

A : Prioritizing rapid deployment over the ethical considerations and potential biases in AI models

B : Using generative AI exclusively for creative applications and avoiding its use in business operations

C : Implementing guardrails and enhancing transparency for generative AI applications

D : Disregarding continuous monitoring and updating of AI models after deployment

Answer: C

Question 5

What is the difference between a machine learning (ML) algorithm and a machine learning (ML) model?

A : An ML algorithm is used for storing large datasets, whereas an ML model is used for deploying applications

B : An ML algorithm is responsible for the security of the machine learning pipeline, while an ML model manages data preprocessing

C : An ML algorithm is a set of mathematical instructions for solving a specific type of problem, while an ML model is the output of the algorithm after being trained on data

D : An ML algorithm is a pre-trained neural network, while an ML model is the raw data used to train the algorithm

Answer: C

Page: 1 / 57

Total 284 Questions | Updated On: Sep 27, 2024

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