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NEW QUESTION # 197
A company wants to build a lead prioritization application for its employees to contact potential customers.
The application must give employees the ability to view and adjust the weights assigned to different variables in the model based on domain knowledge and expertise.
Which ML model type meets these requirements?
Answer: C
Explanation:
The company needs an ML model for a lead prioritization application where employees can view and adjust the weights assigned to different variables based on domain knowledge. Logistic regression is a linear model that assigns interpretable weights to input features, making it easy for users to understand and modify these weights. This interpretability and adjustability make it suitable for the requirements.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"Logistic regression is a supervised learning algorithm used for classification tasks. It is highly interpretable, as it assigns weights to each feature, allowing users to understand and adjust the importance of different variables based on domain expertise." (Source: AWS AI Practitioner Learning Path, Module on Machine Learning Algorithms) Detailed Explanation:
* Option A: Logistic regression modelThis is the correct answer. Logistic regression provides interpretable coefficients (weights) for each feature, enabling employees to view and adjust them based on domain knowledge, meeting the application's requirements.
* Option B: Deep learning model built on principal componentsDeep learning models, even when using principal components, are complex and lack interpretability. The weights in such models are not easily adjustable by users, making this option unsuitable.
* Option C: K-nearest neighbors (k-NN) modelk-NN is a non-parametric model that does not assign explicit weights to features. It relieson distance metrics, which are not easily adjustable based on domain knowledge, so it does not meet the requirements.
* Option D: Neural networkNeural networks are highly complex and lack interpretability, as their weights are not directly tied to input features in a human-understandable way. Adjusting weights based on domain knowledge is impractical, making this option incorrect.
References:
AWS AI Practitioner Learning Path: Module on Machine Learning Algorithms Amazon SageMaker Developer Guide: Logistic Regression (https://docs.aws.amazon.com/sagemaker/latest/dg
/algos.html)
AWS Documentation: Interpretable Machine Learning Models (https://aws.amazon.com/machine-learning/)
NEW QUESTION # 198
Which type of ML technique provides the MOST explainability?
Answer: B
Explanation:
The most explainable model in machine learning is Linear regression. It provides clear mathematical relationships between input features and predicted outcomes, making it highly transparent. According to AWS documentation and Responsible AI best practices, linear regression models allow users to see the exact weight or coefficient assigned to each feature. This makes it easy to explain model decisions to non-technical stakeholders and is especially important in regulated industries like finance and healthcare. Support vector machines, random cut forests, and neural networks are more complex and often operate as black boxes with non-linear transformations that require additional explainability tools like SHAP or LIME. AWS recommends starting with simpler, interpretable models when transparency is a requirement.
Referenced AWS AI/ML Documents and Study Guides:
AWS Responsible AI Whitepaper - Model Transparency and Explainability
Amazon SageMaker Documentation - Interpretable Models
NEW QUESTION # 199
A company has built a chatbot that can respond to natural language questions with images. The company wants to ensure that the chatbot does not return inappropriate or unwanted images.
Which solution will meet these requirements?
Answer: D
NEW QUESTION # 200
A company is building an ML model to analyze archived data. The company must perform inference on large datasets that are multiple GBs in size. The company does not need to access the model predictions immediately.
Which Amazon SageMaker inference option will meet these requirements?
Answer: A
Explanation:
Batch transform in Amazon SageMaker is designed for offline processing of large datasets. It is ideal for scenarios where immediate predictions are not required, and the inference can be done on large datasets that are multiple gigabytes in size. This method processes data in batches, making it suitable for analyzing archived data without the need for real-time access to predictions.
Option A (Correct): "Batch transform": This is the correct answer because batch transform is optimized for handling large datasets and is suitable when immediate access to predictions is not required.
Option B: "Real-time inference" is incorrect because it is used for low-latency, real-time prediction needs, which is not required in this case.
Option C: "Serverless inference" is incorrect because it is designed for small-scale, intermittent inference requests, not for large batch processing.
Option D: "Asynchronous inference" is incorrect because it is used when immediate predictions are required, but with high throughput, whereas batch transform is more suitable for very large datasets.
AWS AI Practitioner Reference:
Batch Transform on AWS SageMaker: AWS recommends using batch transform for large datasets when real-time processing is not needed, ensuring cost-effectiveness and scalability.
NEW QUESTION # 201
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?
Answer: D
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 Reference:
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 # 202
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