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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

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.

Topic 3

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 4

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 5

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.

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Amazon AWS Certified AI Practitioner Sample Questions (Q62-Q67):

NEW QUESTION # 62
An AI practitioner trained a custom model on Amazon Bedrock by using a training dataset that contains confidential dat a. The AI practitioner wants to ensure that the custom model does not generate inference responses based on confidential data.
How should the AI practitioner prevent responses based on confidential data?

A. Delete the custom model. Remove the confidential data from the training dataset. Retrain the custom model.
B. Encrypt the confidential data in the inference responses by using Amazon SageMaker.
C. Mask the confidential data in the inference responses by using dynamic data masking.
D. Encrypt the confidential data in the custom model by using AWS Key Management Service (AWS KMS).

Answer: A

NEW QUESTION # 63
An AI practitioner is building a model to generate images of humans in various professions. The AI practitioner discovered that the input data is biased and that specific attributes affect the image generation and create bias in the model.
Which technique will solve the problem?

A. Watermark detection for images
B. Retrieval Augmented Generation (RAG)
C. Data augmentation for imbalanced classes
D. Model monitoring for class distribution

Answer: C

NEW QUESTION # 64
A company has built a solution by using generative AI. The solution uses large language models (LLMs) to translate training manuals from English into other languages. The company wants to evaluate the accuracy of the solution by examining the text generated for the manuals.
Which model evaluation strategy meets these requirements?

A. Recall-Oriented Understudy for Gisting Evaluation (ROUGE)
B. F1 score
C. Root mean squared error (RMSE)
D. Bilingual Evaluation Understudy (BLEU)

Answer: D

Explanation:
BLEU (Bilingual Evaluation Understudy) is a metric used to evaluate the accuracy of machine-generated translations by comparing them against reference translations. It is commonly used for translation tasks to measure how close the generated output is to professional human translations.
* Option A (Correct): "Bilingual Evaluation Understudy (BLEU)": This is the correct answer because BLEU is specifically designed to evaluate the quality of translations, making it suitable for the company's use case.
* Option B: "Root mean squared error (RMSE)" is incorrect because RMSE is used for regression tasks to measure prediction errors, not translation quality.
* Option C: "Recall-Oriented Understudy for Gisting Evaluation (ROUGE)" is incorrect as it is used to evaluate text summarization, not translation.
* Option D: "F1 score" is incorrect because it is typically used for classification tasks, not for evaluating translation accuracy.
AWS AI Practitioner References:
* Model Evaluation Metrics on AWS: AWS supports various metrics like BLEU for specific use cases, such as evaluating machine translation models.

NEW QUESTION # 65
An AI practitioner is using a large language model (LLM) to create content for marketing campaigns. The generated content sounds plausible and factual but is incorrect.
Which problem is the LLM having?

A. Hallucination
B. Underfitting
C. Data leakage
D. Overfitting

Answer: A

Explanation:
In the context of AI, "hallucination" refers to the phenomenon where a model generates outputs that are plausible-sounding but are not grounded in reality or the training data. This problem often occurs with large language models (LLMs) when they create information that sounds correct but is actually incorrect or fabricated.
* Option B (Correct): "Hallucination": This is the correct answer because the problem described involves generating content that sounds factual but is incorrect, which is characteristic of hallucination in generative AI models.
* Option A: "Data leakage" is incorrect as it involves the model accidentally learning from data it shouldn't have access to, which does not match the problem of generating incorrect content.
* Option C: "Overfitting" is incorrect because overfitting refers to a model that has learned the training data too well, including noise, and performs poorly on new data.
* Option D: "Underfitting" is incorrect because underfitting occurs when a model is too simple to capture the underlying patterns in the data, which is not the issue here.
AWS AI Practitioner References:
* Large Language Models on AWS: AWS discusses the challenge of hallucination in large language models and emphasizes techniques to mitigate it, such as using guardrails and fine-tuning.

NEW QUESTION # 66
An ecommerce company is deploying a chatbot. The chatbot will give users the ability to ask questions about the company's products and receive details on users' orders. The company must implement safeguards for the chatbot to filter harmful content from the input prompts and chatbot responses.
Which AWS feature or resource meets these requirements?

A. Amazon Bedrock Agents
B. Amazon Bedrock inference APIs
C. Amazon Bedrock custom models
D. Amazon Bedrock Guardrails

Answer: D

Explanation:
The ecommerce company is deploying a chatbot that needs safeguards to filter harmful content from input prompts and responses. Amazon Bedrock Guardrails provide mechanisms to ensure responsible AI usage by filtering harmful content, such as hate speech, violence, or misinformation, making it the appropriate feature for this requirement.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Amazon Bedrock Guardrails enable developers to implement safeguards for generative AI applications, such as chatbots, by filtering harmful content in input prompts and model responses. Guardrails include content filters, word filters, and denied topics to ensure safe and responsible outputs." (Source: AWS Bedrock User Guide, Guardrails for Responsible AI) Detailed Option A: Amazon Bedrock GuardrailsThis is the correct answer. Amazon Bedrock Guardrails are specifically designed to filter harmful content from chatbot inputs and responses, ensuring safe interactions for users.
Option B: Amazon Bedrock AgentsAmazon Bedrock Agents are used to automate tasks and integrate with external tools, not to filter harmful content. This option does not meet the requirement.
Option C: Amazon Bedrock inference APIsAmazon Bedrock inference APIs allow users to invoke foundation models for generating responses, but they do not provide built-in content filtering mechanisms.
Option D: Amazon Bedrock custom modelsCustom models on Amazon Bedrock allow users to fine-tune models, but they do not inherently include safeguards for filtering harmful content unless explicitly implemented.
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: Building Safe AI Applications (https://aws.amazon.com/bedrock/)

NEW QUESTION # 67
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