Free Amazon MLS-C01 Exam Questions

Question 1

A company supplies wholesale clothing to thousands of retail stores. A data scientist must create a model that predicts the daily sales volume for each item for each store. The data scientist discovers that more than half of the stores have been in business for less than 6 months. Sales data is highly consistent from week to week. Daily data from the database has been aggregated weekly, and weeks with no sales are omitted from the current dataset. Five years (100 MB) of sales data is available in Amazon S3.

Which factors will adversely impact the performance of the forecast model to be developed, and which actions should the data scientist take to mitigate them? (Choose two.)

A : tecting seasonality for the majority of stores will be an issue. Request categorical data to relate new stores with similar stores that have more historical data.

B : sales data does not have enough variance. Request external sales data from other industries to improve the model's ability to generalize.

C : les data is aggregated by week. Request daily sales data from the source database to enable building a daily model.

D : sales data is missing zero entries for item sales. Request that item sales data from the source database include zero entries to enable building the model.

E : ly 100 MB of sales data is available in Amazon S3. Request 10 years of sales data, which would provide 200 MB of training data for the model.

Answer: A,B

Question 2

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?

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.

Answer: C

Question 3

A company is running a machine learning prediction service that generates 100 TB of predictions every day A Machine Learning Specialist must generate a visualization of the daily precision-recall curve from the predictions, and forward a read-only version to the Business team.

Which solution requires the LEAST coding effort?

A : n a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3 Give the Business team read-only access to S3

B : nerate daily precision-recall data in Amazon QuickSight, and publish the results in a dashboard shared with the Business team

C : n a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3 Visualize the arrays in Amazon QuickSight, and publish them in a dashboard shared with the Business team

D : nerate daily precision-recall data in Amazon ES, and publish the results in a dashboard shared with the Business team.

Answer: C

Question 4

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 : assification month-to-month using supervised learning of the 200 categories based on claim contents.

B : inforcement learning using claim IDs and timestamps where the agent will identify how many claims in

each category to expect from month to month.

C : recasting using claim IDs and timestamps to identify how many claims in each category to expect from

month to month.

D : assification 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.

Answer: D

Question 5

A large consumer goods manufacturer has the following products on sale

* 34 different toothpaste variants

* 48 different toothbrush variants

* 43 different mouthwash variants

The entire sales history of all these products is available in Amazon S3 Currently, the company is using custom-built autoregressive integrated moving average (ARIMA) models to forecast demand for these products The company wants to predict the demand for a new product that will soon be launched

Which solution should a Machine Learning Specialist apply?

A : ain a custom ARIMA model to forecast demand for the new product.

B : ain an Amazon SageMaker DeepAR algorithm to forecast demand for the new product

C : ain an Amazon SageMaker k-means clustering algorithm to forecast demand for the new product.

D : ain a custom XGBoost model to forecast demand for the new product

Answer: B