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AIF-C01 Flexible Testing Engine - AIF-C01 PDF

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Amazon AIF-C01 Exam Syllabus Topics:TopicDetailsTopic 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

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

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 (Q176-Q181):

NEW QUESTION # 176
Which type of AI model makes numeric predictions?

A. Transformer
B. Diffusion
C. Multi-modal
D. Regression

Answer: D

Explanation:
The regression model is a fundamental type of supervised machine learning algorithm that is specifically designed to make numeric predictions. In regression tasks, the goal is to predict a continuous numerical value based on input features. This contrasts with classification, which predicts discrete labels.
* According to AWS documentation:
"Regression models are used for predicting a continuous value. Examples include predicting house prices, stock market prices, or customer credit limits." (Reference: AWS Machine Learning Foundations: Regression, AWS AI Practitioner Study Guide)
* Option A (Diffusion) relates to generative models and is not primarily used for numeric prediction.
* Option C (Transformer) is a neural network architecture, often used for sequence modeling tasks (e.g., NLP).
* Option D (Multi-modal) describes a model handling multiple data types, not specifically numeric prediction.
References:
AWS AI/ML Learning Path - Regression Models
AWS Certified AI Practitioner Study Guide (Pearson)

NEW QUESTION # 177
A company has documents that are missing some words because of a database error. The company wants to build an ML model that can suggest potential words to fill in the missing text.
Which type of model meets this requirement?

A. BERT-based models
B. Clustering models
C. Topic modeling
D. Prescriptive ML models

Answer: A

Explanation:
BERT-based models (Bidirectional Encoder Representations from Transformers) are suitable for tasks that involve understanding the context of words in a sentence and suggesting missing words. These models use bidirectional training, which considers the context from both directions (left and right of the missing word) to predict the appropriate word to fill in the gaps.
* BERT-based Models:
* BERT is a pre-trained transformer model designed for natural language understanding tasks, including text completion, where certain words are missing.
* It excels at understanding context and relationships between words in a sentence, making it ideal for suggesting potential words to fill in missing text.
* Why Option D is Correct:
* Contextual Understanding: BERT uses its bidirectional training to understand the context around missing words, making it highly accurate in suggesting suitable replacements.
* Text Completion Capability: BERT's architecture is explicitly designed for tasks like masked language modeling, where certain words in a text are masked (or missing), and the model predicts the missing words.
* Why Other Options are Incorrect:
* A. Topic modeling: Focuses on identifying topics in a text corpus, not on predicting missing words.
* B. Clustering models: Group similar data points together, which is not suitable for predicting missing text.
* C. Prescriptive ML models: Focus on providing recommendations based on data analysis, not on natural language processing tasks like filling in missing text.

NEW QUESTION # 178
A company is using a pre-trained large language model (LLM) to extract information from documents. The company noticed that a newer LLM from a different provider is available on Amazon Bedrock. The company wants to transition to the new LLM on Amazon Bedrock.
What does the company need to do to transition to the new LLM?

A. Adjust the prompt template.
B. Create a new labeled dataset
C. Fine-tune the LLM.
D. Perform feature engineering.

Answer: A

Explanation:
Transitioning to a new large language model (LLM) on Amazon Bedrock typically involves minimal changes when the new model is pre-trained and available as a foundation model. Since the company is moving from one pre-trained LLM to another, the primary task is to ensure compatibility between the new model's input requirements and the existing application. Adjusting the prompt template is often necessary because different LLMs may have varying prompt formats, tokenization methods, or response behaviors, even for similar tasks like document extraction.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"When switching between foundation models in Amazon Bedrock, you may need to adjust the prompt template to align with the new model's expected input format and optimize its performance for your use case.
Prompt engineering is critical to ensure the model understands the task and generates accurate outputs." (Source: AWS Bedrock User Guide, Prompt Engineering for Foundation Models) Detailed Explanation:
Option A: Create a new labeled dataset.Creating a new labeled dataset is unnecessary when transitioning to a new pre-trained LLM, as pre-trained models are already trained on large datasets. This option would only be relevant if the company were training a custom model from scratch, which is not the case here.
Option B: Perform feature engineering.Feature engineering is typically associated with traditional machine learning models, not pre-trained LLMs. LLMs process raw text inputs, and transitioning to a new LLM does not require restructuring input features. This option is incorrect.
Option C: Adjust the prompt template.This is the correct approach. Different LLMs may interpret prompts differently due to variations in training data, tokenization, or model architecture. Adjusting the prompt template ensures the new LLM understands the task (e.g., document extraction) and produces the desired output format. AWS documentation emphasizes prompt engineering as a key step when adopting a new foundation model.
Option D: Fine-tune the LLM.Fine-tuning is not required for transitioning to a new pre-trained LLM unless the company needs to customize the model for a highly specific task. Since the question does not indicate a need for customization beyond document extraction (a common LLM capability), fine-tuning is unnecessary.
References:
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 Working with Foundation Models in Amazon Bedrock Amazon Bedrock Developer Guide: Transitioning Between Models (https://docs.aws.amazon.com/bedrock
/latest/devguide/)

NEW QUESTION # 179
A company is building a customer service chatbot. The company wants the chatbot to improve its responses by learning from past interactions and online resources.
Which AI learning strategy provides this self-improvement capability?

A. Supervised learning with a manually curated dataset of good responses and bad responses
B. Reinforcement learning with rewards for positive customer feedback
C. Supervised learning with a continuously updated FAQ database
D. Unsupervised learning to find clusters of similar customer inquiries

Answer: B

Explanation:
Reinforcement learning allows a model to learn and improve over time based on feedback from its environment. In this case, the chatbot can improve its responses by being rewarded for positive customer feedback, which aligns well with the goal of self-improvement based on past interactions and new information.
* Option B (Correct): "Reinforcement learning with rewards for positive customer feedback": This is the correct answer as reinforcement learning enables the chatbot to learn from feedback and adapt its behavior accordingly, providing self-improvement capabilities.
* Option A: "Supervised learning with a manually curated dataset" is incorrect because it does not support continuous learning from new interactions.
* Option C: "Unsupervised learning to find clusters of similar customer inquiries" is incorrect because unsupervised learning does not provide a mechanism for improving responses based on feedback.
* Option D: "Supervised learning with a continuously updated FAQ database" is incorrect because it still relies on manually curated data rather than self-improvement from feedback.
AWS AI Practitioner References:
* Reinforcement Learning on AWS: AWS provides reinforcement learning frameworks that can be used to train models to improve their performance based on feedback.

NEW QUESTION # 180
A company has a foundation model (FM) that was customized by using Amazon Bedrock to answer customer queries about products. The company wants to validate the model's responses to new types of queries. The company needs to upload a new dataset that Amazon Bedrock can use for validation.
Which AWS service meets these requirements?

A. Amazon Elastic File System (Amazon EFS)
B. Amazon Elastic Block Store (Amazon EBS)
C. AWS Snowcone
D. Amazon S3

Answer: D

Explanation:
Amazon S3 is the optimal choice for storing and uploading datasets used for machine learning model validation and training. It offers scalable, durable, and secure storage, making it ideal for holding datasets required by Amazon Bedrock for validation purposes.
* Option A (Correct): "Amazon S3": This is the correct answer because Amazon S3 is widely used for storing large datasets that are accessed by machine learning models, including those in Amazon Bedrock.
* Option B: "Amazon Elastic Block Store (Amazon EBS)" is incorrect because EBS is a block storage service for use with Amazon EC2, not for directly storing datasets for Amazon Bedrock.
* Option C: "Amazon Elastic File System (Amazon EFS)" is incorrect as it is primarily used for file storage with shared access by multiple instances.
* Option D: "AWS Snowcone" is incorrect because it is a physical device for offline data transfer, not suitable for directly providing data to Amazon Bedrock.
AWS AI Practitioner References:
* Storing and Managing Datasets on AWS for Machine Learning: AWS recommends using S3 for storing and managing datasets required for ML model training and validation.

NEW QUESTION # 181
......

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