Free Amazon MLS-C01 Exam Questions

Question 1

A Machine Learning Specialist is attempting to build a linear regression model.

Given the displayed residual plot only, what is the MOST likely problem with the model?

A : Linear regression is inappropriate. The residuals do not have constant variance.

B : Linear regression is inappropriate. The underlying data has outliers.

C : Linear regression is appropriate. The residuals have a zero mean.

D : Linear regression is appropriate. The residuals have constant variance.

Answer: D

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 machine learning specialist works for a fruit processing company and needs to build a system that

categorizes apples into three types. The specialist has collected a dataset that contains 150 images for each

type of apple and applied transfer learning on a neural network that was pretrained on ImageNet with this

dataset.

The company requires at least 85?curacy to make use of the model.

After an exhaustive grid search, the optimal hyperparameters produced the following:

68?curacy on the training set

67?curacy on the validation set

What can the machine learning specialist do to improve the system's accuracy?

A : Upload the model to an Amazon SageMaker notebook instance and use the Amazon SageMaker HPOfeature to optimize the model's hyperparameters.

B : more data to the training set and retrain the model using transfer learning to reduce the bias.

C : Use a neural network model with more layers that are pretrained on ImageNet and apply transfer learningto increase the variance.

D : ain a new model using the current neural network architecture.

Answer: B

Question 4

A company wants to conduct targeted marketing to sell solar panels to homeowners. The company wants to use machine learning (ML) technologies to identify which houses already have solar panels. The company has collected 8,000 satellite images as training data and will use Amazon SageMaker Ground Truth to label the data. The company has a small internal team that is working on the project. The internal team has no ML expertise and no ML experience. Which solution will meet these requirements with the LEAST amount of effort from the internal team?

A : t up a private workforce that consists of the internal team. Use the private workforce and the SageMaker Ground Truth active learning feature to label the data. Use Amazon Rekognition Custom Labels for model training and hosting.

B : t up a private workforce that consists of the internal team. Use the private workforce to label the data. Use Amazon Rekognition Custom Labels for model training and hosting

C : t up a private workforce that consists of the internal team. Use the private workforce and the SageMaker Ground Truth active learning feature to label the data. Use the SageMaker Object Detection algorithm to train a model. Use SageMaker batch transform for inference

D : t up a public workforce. Use the public workforce to label the data. Use the SageMaker Object Detection algorithm to train a model. Use SageMaker batch transform for inference.

Answer: B

Question 5

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