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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

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 3

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 4

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 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 (Q158-Q163):

NEW QUESTION # 158
A bank has fine-tuned a large language model (LLM) to expedite the loan approval process. During an external audit of the model, the company discovered that the model was approving loans at a faster pace for a specific demographic than for other demographics.
How should the bank fix this issue MOST cost-effectively?

A. Use AWS Trusted Advisor checks to eliminate bias.
B. Pre-train a new LLM with more diverse training data.
C. Use Retrieval Augmented Generation (RAG) with the fine-tuned model.
D. Include more diverse training data. Fine-tune the model again by using the new data.

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The best practice for mitigating bias in AI/ML models, according to AWS and responsible AI frameworks, is to ensure that the training data is representative and diverse. If a model demonstrates bias (such as favoring a particular demographic), the recommended, cost-effective approach is to collect additional data from underrepresented groups and retrain (fine-tune) the model with the improved dataset.
A . Include more diverse training data. Fine-tune the model again by using the new data:
"The most effective method to reduce model bias is to curate and include diverse, representative training data, then retrain or fine-tune the model." (Reference: AWS Responsible AI, SageMaker Clarify Bias Mitigation)
"The most effective method to reduce model bias is to curate and include diverse, representative training data, then retrain or fine-tune the model." (Reference: AWS Responsible AI, SageMaker Clarify Bias Mitigation) B (RAG) is unrelated to model fairness or bias mitigation; it's for grounding LLMs with external knowledge.
C (AWS Trusted Advisor) is for AWS resource optimization/security-not for ML model bias detection or mitigation.
D (Pre-train a new LLM) would be extremely costly and is unnecessary; fine-tuning with better data is much more efficient.
Reference:
Responsible AI on AWS
Amazon SageMaker Clarify: Detecting and Mitigating Bias
AWS Certified AI Practitioner Exam Guide

NEW QUESTION # 159
A company is building a new generative AI chatbot. The chatbot uses an Amazon Bedrock foundation model (FM) to generate responses. During testing, the company notices that the chatbot is prone to prompt injection attacks.
What can the company do to secure the chatbot with the LEAST implementation effort?

A. Use chain-of-thought prompting to produce secure responses.
B. Fine-tune the FM to avoid harmful responses.
C. Change the FM to a more secure FM.
D. Use Amazon Bedrock Guardrails content filters and denied topics.

Answer: D

Explanation:
Amazon Bedrock Guardrails allow developers to create safeguards that filter harmful content and prevent sensitive topics from being discussed. This functionality helps mitigate prompt injection attacks with minimal implementation effort. According to the official Amazon Bedrock documentation:
Explanation:
Amazon Bedrock Guardrails allow developers to create safeguards that filter harmful content and prevent sensitive topics from being discussed. This functionality helps mitigate prompt injection attacks with minimal implementation effort. According to the official Amazon Bedrock documentation:
"You can configure Guardrails for Amazon Bedrock to define denied topics, use content filters, and apply sensitive information filters, offering protection against prompt injection attacks with minimal development effort."

NEW QUESTION # 160
Which option is a benefit of ongoing pre-training when fine-tuning a foundation model (FM)?

A. Improves model performance over time
B. Decreases the training time requirement
C. Helps decrease the model's complexity
D. Optimizes model inference time

Answer: A

Explanation:
Ongoing pre-training when fine-tuning a foundation model (FM) improves model performance over time by continuously learning from new data.
* Ongoing Pre-Training:
* Involves continuously training a model with new data to adapt to changing patterns, enhance generalization, and improve performance on specific tasks.
* Helps the model stay updated with the latest data trends and minimize drift over time.
* Why Option B is Correct:
* Performance Enhancement: Continuously updating the model with new data improves its accuracy and relevance.
* Adaptability: Ensures the model adapts to new data distributions or domain-specific nuances.
* Why Other Options are Incorrect:
* A. Decrease model complexity: Ongoing pre-training typically enhances complexity by learning new patterns, not reducing it.
* C. Decreases training time requirement: Ongoing pre-training may increase the time needed for training.
* D. Optimizes inference time: Does not directly affect inference time; rather, it affects model performance.

NEW QUESTION # 161
Which technique involves training AI models on labeled datasets to adapt the models to specific industry terminology and requirements?

A. Fine-tuning
B. Continuous pre-training
C. Model quantization
D. Data augmentation

Answer: A

Explanation:
Fine-tuning involves training a pre-trained AI model on a labeled dataset specific to a particular task or domain, adapting it to industry terminology and requirements. This process adjusts the model's parameters to better fit the target use case, such as understanding specialized vocabulary or meeting domain-specific needs.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Fine-tuning allows you to adapt a pre-trained foundation model to your specific use case by training it on a labeled dataset. This technique is commonly used to customize models forindustry-specific terminology, improving their accuracy for specialized tasks." (Source: AWS Bedrock User Guide, Model Customization) Detailed Explanation:
* Option A: Data augmentationData augmentation involves generating synthetic data to expand a training dataset, typically for tasks like image or text generation. It does not specifically adapt models to industry terminology or requirements.
* Option B: Fine-tuningThis is the correct answer. Fine-tuning trains a pre-trained model on a labeled dataset tailored to the target domain, enabling it to learn industry-specific terminology and requirements, as described in the question.
* Option C: Model quantizationModel quantization reduces the precision of a model's weights to optimize it for deployment (e.g., on edge devices). It does not involve training on labeled datasets or adapting to industry terminology.
* Option D: Continuous pre-trainingContinuous pre-training extends the initial training of a model on a large, general dataset. While it can improve general performance, it is not specifically tailored to industry requirements using labeled datasets, unlike fine-tuning.
References:
AWS Bedrock User Guide: Model Customization (https://docs.aws.amazon.com/bedrock/latest/userguide
/custom-models.html)
AWS AI Practitioner Learning Path: Module on Model Training and Customization Amazon SageMaker Developer Guide: Fine-Tuning Models (https://docs.aws.amazon.com/sagemaker/latest
/dg/algos.html)

NEW QUESTION # 162
A manufacturing company uses AI to inspect products and find any damages or defects.
Which type of AI application is the company using?

A. Computer vision
B. Image processing
C. Recommendation system
D. Natural language processing (NLP)

Answer: A

Explanation:
The manufacturing company uses AI to inspect products for damages or defects, which involves analyzing visual data (e.g., images or videos of products). This task falls under computer vision, a type of AI application that enables machines to interpret and understand visual information, such as identifying defects in manufacturing.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"Computer vision enables machines to interpret and understand visual data from the world, such as images or videos. Common applications include defect detection in manufacturing, where AI models analyze product images to identify damages or anomalies." (Source: AWS AI Practitioner Learning Path, Module on AI Concepts) Detailed Explanation:
* Option A: Recommendation systemRecommendation systems suggest items or actions based on user preferences (e.g., product recommendations). They are not relevant for inspecting products for defects.
* Option B: Natural language processing (NLP)NLP focuses on processing and understanding text or speech, not visual data like product images. This option is incorrect.
* Option C: Computer visionThis is the correct answer. Computer vision is used for tasks like defect detection in manufacturing by analyzing visual data to identify damages or defects.
* Option D: Image processingWhile image processing involves manipulating images (e.g., filtering, resizing), it is a lower-level technique, not an AI application. Computer vision, which often uses image processing as a component, is the broader AI application here.
References:
AWS AI Practitioner Learning Path: Module on AI Concepts
Amazon Rekognition Developer Guide: Image Analysis (https://docs.aws.amazon.com/rekognition/latest/dg
/what-is.html)
AWS Documentation: Introduction to Computer Vision (https://aws.amazon.com/computer-vision/)

NEW QUESTION # 163
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