AIF-C01 Dumps with Practice Exam Questions Answers

A confusion matrix is the correct metric for evaluating the performance of a classification model, such as the deep learning model built to classify types of materials in images.

Confusion Matrix:

It is a table used to describe the performance of a classification model by comparing the actual and predicted classifications.

Provides detailed insights into the model’s performance, including true positives, true negatives, false positives, and false negatives.

Why Option A is Correct:

Performance Measurement: Helps measure various performance metrics like accuracy, precision, recall, and F1-score, which are critical for evaluating a classification model.

Comprehensive Evaluation: Allows for a thorough analysis of where the model is making errors and the types of errors being made.

Why Other Options are Incorrect:

B. Correlation matrix: Used to identify relationships between variables, not for evaluating classification performance.

C. R2 score: Used for regression models, not classification.

D. Mean squared error (MSE): Also a metric for regression, measuring the average of the squares of the errors.

To securely use large language models (LLMs) on Amazon Bedrock, companies should design clear and specific prompts to avoid unintended outputs and ensure proper configuration of AWS Identity and Access Management (IAM) roles and policies with the principle of least privilege. This approach limits access to sensitive resources and minimizes the potential impact of security incidents.

Option A (Correct): "Design clear and specific prompts. Configure AWS Identity and Access Management (IAM) roles and policies by using least privilege access": This is the correct answer as it directly addresses both security practices in prompt design and access management.

Option B: "Enable AWS Audit Manager for automatic model evaluation jobs" is incorrect because Audit Manager is for compliance and auditing, not directly related to secure LLM usage.

Option C: "Enable Amazon Bedrock automatic model evaluation jobs" is incorrect because Bedrock does not provide automatic model evaluation jobs specifically for security purposes.

Option D: "Use Amazon CloudWatch Logs to make models explainable and to monitor for bias" is incorrect because CloudWatch Logs are used for monitoring and not directly for making models explainable or secure.

AWS AI Practitioner References:

Secure AI Practices on AWS: AWS recommends configuring IAM roles and using least privilege access to ensure secure usage of AI models.

Amazon SageMaker Model Cards document model details, performance, intended use cases, and risk considerations. They support responsible AI by improving transparency and governance.

Rekognition is computer vision.

Comprehend is NLP for entity/sentiment.

Lex is conversational AI.

???? Reference:

AWS Documentation – SageMaker Model Cards

AWS References:

Amazon Bedrock – Retrieval Augmented Generation (RAG)

AWS Generative AI Guide – Zero-shot & Few-shot learning