Question 81 · 417 in bank
A company built a deep learning model for object detection and deployed the model to production. Which AI process occurs when the model analyzes a new image to identify objects?
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Answer: B
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A company built a deep learning model for object detection and deployed the model to production. Which AI process occurs when the model analyzes a new image to identify objects?
Answer: B
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?
Answer: A
Data augmentation for imbalanced classes: If the input data is biased and leads to undesirable attributes in the generated images (such as certain professions being overrepresented by specific attributes like gender or race), data augmentation can help balance the dataset. Data augmentation involves creating new training samples by applying transformations like cropping, rotating, or altering color schemes to existing data. This can help create a more diverse, balanced dataset and reduce bias by ensuring the model sees a more representative set of examples.
Source: Adapted from community discussion — Jessiii
A company is implementing the Amazon Titan foundation model (FM) by using Amazon Bedrock. The company needs to supplement the model by using relevant data from the company's private data sources. Which solution will meet this requirement?
Answer: C
C. Create an Amazon Bedrock knowledge base. This would allow the company use the knowledge base for Retrieval Augmented Generation (RAG) to enhance the model's knowledge with company's private data sources.
Source: Adapted from community discussion — minime
A medical company is customizing a foundation model (FM) for diagnostic purposes. The company needs the model to be transparent and explainable to meet regulatory requirements. Which solution will meet these requirements?
Answer: B
Amazon SageMaker Clarify is specifically designed to help make machine learning models more transparent and explainable by generating metrics and reports on model bias, data bias, and feature importance.
Source: Adapted from community discussion — jove
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?
Answer: B
Accuracy is the most straightforward metric, measuring the proportion of correctly predicted instances out of the total instances. It is suitable when the classes are balanced (but can be misleading for imbalanced datasets).
Source: Adapted from community discussion — galliaj
A company wants to deploy a conversational chatbot to answer customer questions. The chatbot is based on a fine-tuned Amazon SageMaker JumpStart model. The application must comply with multiple regulatory frameworks. Which capabilities can the company show compliance for? (Choose two.)
Answer: BC
The two capabilities that the company can show compliance for are: C. Data protection B. Threat detection Here's a breakdown: Data Protection: Amazon SageMaker offers robust data protection features, including data encryption at rest and in transit. By leveraging these features, the company can ensure that customer data is handled securely and complies with relevant data privacy regulations. Threat Detection: Amazon Web Services (AWS) provides a comprehensive security suite, including services like Amazon GuardDuty and AWS Security Hub. These services can help detect and respond to potential threats, such as unauthorized access, data breaches, and malicious activity. By utilizing these services, the company can demonstrate its commitment to security and compliance.
Source: Adapted from community discussion — eesa
A company is training a foundation model (FM). The company wants to increase the accuracy of the model up to a specific acceptance level. Which solution will meet these requirements?
Answer: B
B. Increase the epochs: In deep learning, training a model for more epochs means that the model will go through the dataset more times, which generally leads to better learning and improved accuracy. Increasing the number of epochs allows the model to learn patterns more effectively, helping it reach the desired accuracy level. However, there’s a trade-off, as increasing epochs too much could lead to overfitting.
Source: Adapted from community discussion — Jessiii
A company is building a large language model (LLM) question answering chatbot. The company wants to decrease the number of actions call center employees need to take to respond to customer questions. Which business objective should the company use to evaluate the effect of the LLM chatbot?
Answer: B
Which functionality does Amazon SageMaker Clarify provide?
Answer: D
Amazon SageMaker Clarify provides functionality to detect and identify potential bias in data both before and after training, helping teams uncover imbalances in datasets that might lead to biased model predictions. This is essential for ensuring fairness and compliance, especially in sensitive applications. Why Not the Other Options? A. Integrates a Retrieval Augmented Generation (RAG) workflow: RAG workflows are used for combining retrieved documents with model outputs, typically in language models, but this is not a function of SageMaker Clarify. B. Monitors the quality of ML models in production: Monitoring model quality in production is handled by SageMaker Model Monitor, not SageMaker Clarify. C. Documents critical details about ML models: This functionality is part of Amazon SageMaker Model Cards, which documents model details for transparency and compliance.
Source: Adapted from community discussion — jove
A company is developing a new model to predict the prices of specific items. The model performed well on the training dataset. When the company deployed the model to production, the model's performance decreased significantly. What should the company do to mitigate this problem?
Answer: C
C: Increase the volume of data that is used in training. Explanation: The issue described is likely caused by overfitting, where the model performs well on the training dataset but fails to generalize to unseen data. Increasing the volume of training data can help mitigate overfitting by providing the model with more diverse examples, improving its ability to generalize to new data in production.
Source: Adapted from community discussion — Moon
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