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Amazon AIF-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 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	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 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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Amazon AWS Certified AI Practitioner Sample Questions (Q11-Q16):

NEW QUESTION # 11
An accounting firm wants to implement a large language model (LLM) to automate document processing.
The firm must proceed responsibly to avoid potential harms.
What should the firm do when developing and deploying the LLM? (Select TWO.)

A. Apply prompt engineering techniques.
B. Include fairness metrics for model evaluation.
C. Adjust the temperature parameter of the model.
D. Avoid overfitting on the training data.
E. Modify the training data to mitigate bias.

Answer: B,E

Explanation:
To implement a large language model (LLM) responsibly, the firm should focus on fairness and mitigating bias, which are critical for ethical AI deployment.
* A. Include Fairness Metrics for Model Evaluation:
* Fairness metrics help ensure that the model's predictions are unbiased and do not unfairly discriminate against any group.
* These metrics can measure disparities in model outcomes across different demographic groups, ensuring responsible AI practices.
* C. Modify the Training Data to Mitigate Bias:
* Adjusting training data to be more representative and balanced can help reduce bias in the model's predictions.
* Mitigating bias at the data level ensures that the model learns from a diverse and fair dataset, reducing potential harms in deployment.
* Why Other Options are Incorrect:
* B. Adjust the temperature parameter of the model: Controls randomness in outputs but does not directly address fairness or bias.
* D. Avoid overfitting on the training data: Important for model generalization but not directly related to responsible AI practices regarding fairness and bias.
* E. Apply prompt engineering techniques: Useful for improving model outputs but not specifically for mitigating bias or ensuring fairness.

NEW QUESTION # 12
A company wants to use a pre-trained generative AI model to generate content for its marketing campaigns. The company needs to ensure that the generated content aligns with the company's brand voice and messaging requirements.
Which solution meets these requirements?

A. Optimize the model's architecture and hyperparameters to improve the model's overall performance.
B. Create effective prompts that provide clear instructions and context to guide the model's generation.
C. Select a large, diverse dataset to pre-train a new generative model.
D. Increase the model's complexity by adding more layers to the model's architecture.

Answer: B

NEW QUESTION # 13
How can companies use large language models (LLMs) securely on Amazon Bedrock?

A. Use Amazon CloudWatch Logs to make models explainable and to monitor for bias.
B. Enable AWS Audit Manager for automatic model evaluation jobs.
C. Enable Amazon Bedrock automatic model evaluation jobs.
D. Design clear and specific prompts. Configure AWS Identity and Access Management (IAM) roles and policies by using least privilege access.

Answer: D

Explanation:
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.

NEW QUESTION # 14
A company has built an image classification model to predict plant diseases from photos of plant leaves. The company wants to evaluate how many images the model classified correctly.
Which evaluation metric should the company use to measure the model's performance?

A. Learning rate
B. R-squared score
C. Accuracy
D. Root mean squared error (RMSE)

Answer: C

Explanation:
Accuracy is the most appropriate metric to measure the performance of an image classification model. It indicates the percentage of correctly classified images out of the total number of images. In the context of classifying plant diseases from images, accuracy will help the company determine how well the model is performing by showing how many images were correctly classified.
* Option B (Correct): "Accuracy": This is the correct answer because accuracy measures the proportion of correct predictions made by the model, which is suitable for evaluating the performance of a classification model.
* Option A: "R-squared score" is incorrect as it is used for regression analysis, not classification tasks.
* Option C: "Root mean squared error (RMSE)" is incorrect because it is also used for regression tasks to measure prediction errors, not for classification accuracy.
* Option D: "Learning rate" is incorrect as it is a hyperparameter for training, not a performance metric.
AWS AI Practitioner References:
* Evaluating Machine Learning Models on AWS: AWS documentation emphasizes the use of appropriate metrics, like accuracy, for classification tasks.

NEW QUESTION # 15
A company wants to use a large language model (LLM) to develop a conversational agent. The company needs to prevent the LLM from being manipulated with common prompt engineering techniques to perform undesirable actions or expose sensitive information.
Which action will reduce these risks?

A. Decrease the number of input tokens on invocations of the LLM.
B. Create a prompt template that teaches the LLM to detect attack patterns.
C. Increase the temperature parameter on invocation requests to the LLM.
D. Avoid using LLMs that are not listed in Amazon SageMaker.

Answer: B

NEW QUESTION # 16
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