Free Amazon MLS-C01 Exam Questions

Question 1

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%r respectively.

How should the Specialist address this issue and what is the reason behind it?

A : learning rate should be increased because the optimization process was trapped at a local minimum.

B : dropout rate at the flatten layer should be increased because the model is not generalized enough.

C : dimensionality of dense layer next to the flatten layer should be increased because the model is not complex enough.

D : epoch number should be increased because the optimization process was terminated before it reached the global minimum.

Answer: D

Question 2

A financial services company wants to adopt Amazon SageMaker as its default data science environment. The company's data scientists run machine learning (ML) models on confidential financial dat

a. The company is worried about data egress and wants an ML engineer to secure the environment.

Which mechanisms can the ML engineer use to control data egress from SageMaker? (Choose three.)

A : nnect to SageMaker by using a VPC interface endpoint powered by AWS PrivateLink.

B : SCPs to restrict access to SageMaker.

C : able root access on the SageMaker notebook instances.

D : able network isolation for training jobs and models.

E : trict notebook presigned URLs to specific IPs used by the company.

F : otect data with encryption at rest and in transit. Use AWS Key Management Service (AWS KMS) to manage encryption keys.

Answer: A,B,D

Question 3

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 4

A company hosts a public web application on AWS. The application provides a user feedback feature that consists of free-text fields where users can submit text to provide feedback. The company receives a large amount of free-text user feedback from the online web application. The product managers at the company classify the feedback into a set of fixed categories including user interface issues, performance issues, new feature request, and chat issues for further actions by the company's engineering teams.

A machine learning (ML) engineer at the company must automate the classification of new user feedback into these fixed categories by using Amazon SageMaker. A large set of accurate data is available from the historical user feedback that the product managers previously classified.

Which solution should the ML engineer apply to perform multi-class text classification of the user feedback?

A : Use the SageMaker Latent Dirichlet Allocation (LDA) algorithm.

B : Use the SageMaker BlazingText algorithm.

C : Use the SageMaker Neural Topic Model (NTM) algorithm.

D : Use the SageMaker CatBoost algorithm.

Answer: B

Question 5

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