A Machine Learning Specialist deployed a model that provides product recommendations on a company's website. Initially, the model was performing very well and resulted in customers buying more products on average. However, within the past few months, the Specialist has noticed that the effect of product recommendations has diminished and customers are starting to return to their original habits of spending less. The Specialist is unsure of what happened, as the model has not changed from its initial deployment over a year ago.Which method should the Specialist try to improve model performance?
Answer(s): D
A Machine Learning Specialist working for an online fashion company wants to build a data ingestion solution for the company's Amazon S3-based data lake.The Specialist wants to create a set of ingestion mechanisms that will enable future capabilities comprised of:•Real-time analytics•Interactive analytics of historical data•Clickstream analytics•Product recommendationsWhich services should the Specialist use?
Answer(s): A
A company is observing low accuracy while training on the default built-in image classification algorithm in Amazon SageMaker. The Data Science team wants to use an Inception neural network architecture instead of a ResNet architecture.Which of the following will accomplish this? (Choose two.)
Answer(s): C,D
A Machine Learning Specialist built an image classification deep learning model. However, the Specialist ran into an overfitting problem in which the training and testing accuracies were 99% and 75%, respectively.How should the Specialist address this issue and what is the reason behind it?
Answer(s): B
Overfitting occurs when a model is too complex and memorizes the training data instead of learning the underlying pattern. As a result, the model performs well on the training data but poorly on new, unseen data.Increasing the dropout rate, a regularization technique, can help combat overfitting by randomly dropping out some neurons during training, which prevents the model from relying too heavily on any single feature.
A Machine Learning team uses Amazon SageMaker to train an Apache MXNet handwritten digit classifier model using a research dataset. The team wants to receive a notification when the model is overfitting. Auditors want to view the Amazon SageMaker log activity report to ensure there are no unauthorized API calls.What should the Machine Learning team do to address the requirements with the least amount of code and fewest steps?
Post your Comments and Discuss Amazon MLS-C01 exam dumps with other Community members:
💬 Did you find this helpful?
Thank you for sharing! Your feedback helps the community.