AWS Certified Machine Learning – Specialty — Question 292
A data scientist receives a collection of insurance claim records. Each record includes a claim ID. the final outcome of the insurance claim, and the date of the final outcome.
The final outcome of each claim is a selection from among 200 outcome categories. Some claim records include only partial information. However, incomplete claim records include only 3 or 4 outcome categories from among the 200 available outcome categories. The collection includes hundreds of records for each outcome category. The records are from the previous 3 years.
The data scientist must create a solution to predict the number of claims that will be in each outcome category every month, several months in advance.
Which solution will meet these requirements?
Answer options
- A. Perform classification every month by using supervised learning of the 200 outcome categories based on claim contents.
- B. Perform reinforcement learning by using claim IDs and dates. Instruct the insurance agents who submit the claim records to estimate the expected number of claims in each outcome category every month.
- C. Perform forecasting by using claim IDs and dates to identify the expected number of claims in each outcome category every month.
- D. Perform classification by using supervised learning of the outcome categories for which partial information on claim contents is provided. Perform forecasting by using claim IDs and dates for all other outcome categories.
Correct answer: C
Explanation
The objective is to predict the quantity of claims per category over a future timeline, which is a classic time-series forecasting problem best solved using the historical date and claim ID data. Classification models (options A and D) are designed to assign categories to individual instances rather than projecting aggregate counts over time. Reinforcement learning (option B) is not suited for passive time-series prediction and relies on manual agent intervention rather than automated machine learning.