Free Amazon MLS-C01 Exam Questions
Try our Free Demo Practice Tests for Comprehensive MLS-C01 Exam Preparation
-
Amazon MLS-C01 Exam Questions
-
Provided By: Amazon
- Exam: AWS Certified Machine Learning - Specialty
- Certification: AWS Certified Machine Learning
-
Total Questions: 385
-
Updated On: Nov 11, 2024
-
Rated: 4.9 |
-
Online Users: 770
-
Question 1
-
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 data. 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.)
Answer: A,B,D
-
-
Question 2
-
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?
Answer: B
-
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.
-
Question 3
-
A bank's Machine Learning team is developing an approach for credit card fraud detection The company has a large dataset of historical data labeled as fraudulent The goal is to build a model to take the information from new transactions and predict whether each transaction is fraudulent or notWhich built-in Amazon SageMaker machine learning algorithm should be used for modeling this problem?
Answer: C
-
-
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?
Answer: B
-
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?
-
Question 5
-
A gaming company has launched an online game where people can start playing for free but they need to pay if they choose to use certain features The company needs to build an automated system to predict whether or not a new user will become a paid user within 1 year The company has gathered a labeled dataset from 1 million usersThe training dataset consists of 1.000 positive samples (from users who ended up paying within 1 year) and 999.000 negative samples (from users who did not use any paid features) Each data sample consists of 200 features including user age, device, location, and play patternsUsing this dataset for training, the Data Science team trained a random forest model that converged with over 99?curacy on the training set However, the prediction results on a test dataset were not satisfactory.Which of the following approaches should the Data Science team take to mitigate this issue? (Select TWO.)
Answer: C,D
-