A food service company wants to develop an ML model to help decrease daily food waste and increase sales revenue. The company needs to continuously improve the model's accuracy.Which solution meets these requirements?
Answer(s): A
To continuously improve the accuracy of the machine learning model, Amazon SageMaker is the appropriate solution. SageMaker allows for efficient model training, deployment, and iteration. The company can useSageMaker to retrain the model regularly with newer data to account for changes in customer behavior, market trends, and other dynamic factors that may affect food waste and sales revenue. Iterating with newer data helps improve the model's performance over time, ensuring it remains accurate and relevant.
A company that streams media is selecting an Amazon Nova foundation model (FM) to process documents and images. The company is comparing Nova Micro and Nova Lite. The company wants to minimize costs.Which model characteristics should the company consider to meet these requirements?
Answer(s): C
Nova Micro supports only text processing, while Nova Lite supports multimodal inputs including images,videos, and text. Selecting the model with broader multimodal support allows the company to choose the most cost-effective option that still meets document and image processing requirements.
A company is training ML models on datasets. The datasets contain some classes that have more examples than other classes. The company wants to measure how well the model balances detecting and labeling the classes.Which metric should the company use?
Answer(s): D
The F1 score is the harmonic mean of precision and recall, making it especially useful for evaluating model performance on datasets with class imbalance. It measures how well the model balances detecting (recall) and correctly labeling (precision) all classes.
A company trains image and text generation models on Amazon SageMaker AI. The company releases the models by using Amazon Bedrock. The company must retain a tamper-proof, queryable record of every APIcall from SageMaker AI, Amazon Bedrock, and AWS Identity and Access Management (IAM).Which AWS service will meet these requirements?
AWS CloudTrail Lake provides a tamper-proof, immutable, and queryable event data store that records APIactivity across services such as Amazon SageMaker AI, Amazon Bedrock, and IAM, enabling long-term auditing, investigation, and compliance reporting.
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?
A logistic regression model is a simple, interpretable machine learning model that allows users to adjust the weights of different variables based on domain knowledge and expertise. Since logistic regression is based on a linear relationship between the input features and the predicted outcome, each feature has a corresponding weight (coefficient) that can be easily viewed and adjusted. This makes it a suitable choice for a lead prioritization application where domain experts need the ability to modify the model based on their knowledge.
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