A Data Scientist received a set of insurance records, each consisting of a record ID, the final outcome among 200 categories, and the date of the final outcome. Some partial information on claim contents is also provided, but only for a few of the 200 categories. For each outcome category, there are hundreds of records distributed over the past 3 years. The Data Scientist wants to predict how many claims to expect in each category from month to month, a few months in advance. What type of machine learning model should be used? A. Classification month-to-month using supervised learning of the 200 categories based on claim contents. B. Reinforcement learning using claim IDs and timestamps where the agent will identify how many claims in each category to expect from month to month. C. Forecasting using claim IDs and timestamps to identify how many claims in each category to expect from month to month. D. Classification with supervised learning of the categories for which partial information on claim contents is provided, and forecasting using claim IDs and timestamps for all other categories.  Suggested Answer: D Community Answer: C This question is in MLS-C01 AWS Certified Machine Learning – Specialty Exam For getting AWS Certified Machine Learning – Specialty Certificate Disclaimers: The website is not related to, affiliated with, endorsed or authorized by Amazon. Trademarks, certification & product names are used for reference only and belong to Amazon. The website does not contain actual questions and answers from Amazon's Certification Exam.
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