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Amazon AIF-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	Fundamentals of Generative AI: This domain explores the basics of generative AI, focusing on techniques for creating new content from learned patterns, including text and image generation. It targets professionals interested in understanding generative models, such as developers and researchers in AI.
Topic 2	Security, Compliance, and Governance for AI Solutions: This domain covers the security measures, compliance requirements, and governance practices essential for managing AI solutions. It targets security professionals, compliance officers, and IT managers responsible for safeguarding AI systems, ensuring regulatory compliance, and implementing effective governance frameworks.
Topic 3	Fundamentals of AI and ML: This domain covers the fundamental concepts of artificial intelligence (AI) and machine learning (ML), including core algorithms and principles. It is aimed at individuals new to AI and ML, such as entry-level data scientists and IT professionals.
Topic 4	Applications of Foundation Models: This domain examines how foundation models, like large language models, are used in practical applications. It is designed for those who need to understand the real-world implementation of these models, including solution architects and data engineers who work with AI technologies to solve complex problems.
Topic 5	Guidelines for Responsible AI: This domain highlights the ethical considerations and best practices for deploying AI solutions responsibly, including ensuring fairness and transparency. It is aimed at AI practitioners, including data scientists and compliance officers, who are involved in the development and deployment of AI systems and need to adhere to ethical standards.

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Free PDF 2025 Amazon AIF-C01: AWS Certified AI Practitioner –Reliable Test Guide Online

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Amazon AWS Certified AI Practitioner Sample Questions (Q144-Q149):

NEW QUESTION # 144
A company wants to improve the accuracy of the responses from a generative AI application. The application uses a foundation model (FM) on Amazon Bedrock.
Which solution meets these requirements MOST cost-effectively?

A. Train a new FM.
B. Retrain the FM.
C. Use prompt engineering.
D. Fine-tune the FM.

Answer: C

Explanation:
The company wants to improve the accuracy of a generative AI application using a foundation model (FM) on Amazon Bedrock in the most cost-effective way. Prompt engineering involves optimizing the input prompts to guide the FM to produce more accurate responses without modifying the model itself. This approach is cost-effective because it does not require additional computational resources or training, unlike fine-tuning or retraining.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Prompt engineering is a cost-effective technique to improve the performance of foundation models. By crafting precise and context-rich prompts, users can guide the model to generate more accurate and relevant responses without the need for fine-tuning or retraining." (Source: AWS Bedrock User Guide, Prompt Engineering for Foundation Models) Detailed Option A: Fine-tune the FM.Fine-tuning involves retraining the FM on a custom dataset, which requirescomputational resources, time, and cost (e.g., for Amazon Bedrock fine-tuning jobs). It is not the most cost-effective solution.
Option B: Retrain the FM.Retraining an FM from scratch is highly resource-intensive and expensive, as it requires large datasets and significant compute power. This is not cost-effective.
Option C: Train a new FM.Training a new FM is the most expensive option, as it involves building a model from the ground up, requiring extensive data, compute resources, and expertise. This is not cost-effective.
Option D: Use prompt engineering.This is the correct answer. Prompt engineering adjusts the input prompts to improve the FM's responses without incurring additional compute costs, making it the most cost-effective solution for improving accuracy on Amazon Bedrock.
Reference:
AWS Bedrock User Guide: Prompt Engineering for Foundation Models (https://docs.aws.amazon.com/bedrock/latest/userguide/prompt-engineering.html) AWS AI Practitioner Learning Path: Module on Generative AI Optimization Amazon Bedrock Developer Guide: Cost Optimization for Generative AI (https://aws.amazon.com/bedrock/)

NEW QUESTION # 145
A food service company wants to develop an ML model to help decrease daily food waste and increase sales revenue. The company needs to continuously improve the model's accuracy.
Which solution meets these requirements?

A. Use Amazon CloudWatch to analyze customer orders.
B. Use Amazon Personalize and iterate with historical data.
C. Use Amazon SageMaker AI and iterate with the most recent data.
D. Use Amazon Rekognition to optimize the model.

Answer: C

Explanation:
Amazon SageMaker is AWS's fully managed ML service that supports retraining and deploying models with new, recent data for continuous improvement. This directly meets the requirement to iterate and continuously improve model accuracy.
* A is correct:
"Amazon SageMaker enables teams to retrain models using the most recent data, ensuring ongoing improvement in model accuracy." (Reference: Amazon SageMaker Overview)
* B (Amazon Personalize) is for recommendations, not general ML or waste reduction.
* C (CloudWatch) is for monitoring, not ML training or deployment.
* D (Rekognition) is for image/video analysis.

NEW QUESTION # 146
A company is developing an ML model to make loan approvals. The company must implement a solution to detect bias in the model. The company must also be able to explain the model's predictions.
Which solution will meet these requirements?

A. Amazon SageMaker Clarify
B. Amazon SageMaker Data Wrangler
C. AWS AI Service Cards
D. Amazon SageMaker Model Cards

Answer: A

Explanation:
Amazon SageMaker Clarify provides built-in tools to detect bias in data and models, and to generate detailed explainability reports for model predictions, including SHAP values and feature importance.
A is correct:
"Amazon SageMaker Clarify provides bias detection, explainability for ML models, and comprehensive reports to satisfy regulatory and ethical requirements." (Reference: Amazon SageMaker Clarify Overview)
"Amazon SageMaker Clarify provides bias detection, explainability for ML models, and comprehensive reports to satisfy regulatory and ethical requirements." (Reference: Amazon SageMaker Clarify Overview) B (Data Wrangler) is for data preparation, not bias/explainability.
C (Model Cards) document models, but don't detect bias or explain predictions.
D (AI Service Cards) provide transparency for AWS AI services, not custom model explainability.

NEW QUESTION # 147
Which component of Amazon Bedrock Studio can help secure the content that AI systems generate?

A. Function calling
B. Knowledge bases
C. Access controls
D. Guardrails

Answer: D

Explanation:
Amazon Bedrock Studio provides tools to build and manage generative AI applications, and the company needs a component to secure the content generated by AI systems. Guardrails in Amazon Bedrock are designed to ensure safe and responsible AI outputs by filtering harmful or inappropriate content, making them the key component for securing generated content.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Guardrails in Amazon Bedrock provide mechanisms to secure the content generated by AI systems by filtering out harmful or inappropriate outputs, such as hate speech, violence, or misinformation, ensuring responsible AI usage." (Source: AWS Bedrock User Guide, Guardrails for Responsible AI) Detailed Option A: Access controlsAccess controls manage who can use or interact with the AI system but do not directly secure the content generated by the system.
Option B: Function callingFunction calling enables AI models to interact with external tools or APIs, but it is not related to securing generated content.
Option C: GuardrailsThis is the correct answer. Guardrails in Amazon Bedrock secure generated content by filtering out harmful or inappropriate material, ensuring safe outputs.
Option D: Knowledge basesKnowledge bases provide data for AI models to generate responses but do not inherently secure the content that is generated.
Reference:
AWS Bedrock User Guide: Guardrails for Responsible AI (https://docs.aws.amazon.com/bedrock/latest/userguide/guardrails.html) AWS AI Practitioner Learning Path: Module on Responsible AI and Model Safety Amazon Bedrock Developer Guide: Securing AI Outputs (https://aws.amazon.com/bedrock/)

NEW QUESTION # 148
A company is developing a new model to predict the prices of specific items. The model performed well on the training dataset. When the company deployed the model to production, the model's performance decreased significantly.
What should the company do to mitigate this problem?

A. Increase the model training time.
B. Add hyperparameters to the model.
C. Increase the volume of data that is used in training.
D. Reduce the volume of data that is used in training.

Answer: C

Explanation:
When a model performs well on the training data but poorly in production, it is often due to overfitting. Overfitting occurs when a model learns patterns and noise specific to the training data, which does not generalize well to new, unseen data in production. Increasing the volume of data used in training can help mitigate this problem by providing a more diverse and representative dataset, which helps the model generalize better.
Option C (Correct): "Increase the volume of data that is used in training": Increasing the data volume can help the model learn more generalized patterns rather than specific features of the training dataset, reducing overfitting and improving performance in production.
Option A: "Reduce the volume of data that is used in training" is incorrect, as reducing data volume would likely worsen the overfitting problem.
Option B: "Add hyperparameters to the model" is incorrect because adding hyperparameters alone does not address the issue of data diversity or model generalization.
Option D: "Increase the model training time" is incorrect because simply increasing training time does not prevent overfitting; the model needs more diverse data.
AWS AI Practitioner Reference:
Best Practices for Model Training on AWS: AWS recommends using a larger and more diverse training dataset to improve a model's generalization capability and reduce the risk of overfitting.

NEW QUESTION # 149
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