Free Amazon MLS-C01 Exam Questions

Question 1

A Data Scientist is developing a machine learning model to predict future patient outcomes based on information collected about each patient and their treatment plans. The model should output a continuous value as its prediction. The data available includes labeled outcomes for a set of 4,000 patients. The study was conducted on a group of individuals over the age of 65 who have a particular disease that is known to worsen with age.

Initial models have performed poorly. While reviewing the underlying data, the Data Scientist notices that, out of 4,000 patient observations, there are 450 where the patient age has been input as 0. The other features for these observations appear normal compared to the rest of the sample population.

How should the Data Scientist correct this issue?

A : op all records from the dataset where age has been set to 0.

B : place the age field value for records with a value of 0 with the mean or median value from the dataset.

C : op the age feature from the dataset and train the model using the rest of the features.

D : k-means clustering to handle missing features.

Answer: B

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 company is training machine learning (ML) models on Amazon SageMaker by using 200 TB of data that is stored in Amazon S3 buckets. The training data consists of individual files that are each larger than 200 MB in size. The company needs a data access solution that offers the shortest processing time and the least amount of setup. Which solution will meet these requirements?

A : File mode in SageMaker to copy the dataset from the S3 buckets to the ML instance storage.

B : eate an Amazon FSx for Lustre file system. Link the file system to the S3 buckets.

C : eate an Amazon Elastic File System (Amazon EFS) file system. Mount the file system to the training instances

D : FastFile mode in SageMaker to stream the files on demand from the S3 buckets

Answer: D

Question 4

A data scientist must build a custom recommendation model in Amazon SageMaker for an online retail company. Due to the nature of the company's products, customers buy only 4-5 products every 5-10 years. So, the company relies on a steady stream of new customers. When a new customer signs up, the company collects data on the customer's preferences. Below is a sample of the data available to the data scientist.

How should the data scientist split the dataset into a training and test set for this use case?

A : uffle all interaction data. Split off the last 10% of the interaction data for the test set.

B : entify the most recent 10% of interactions for each user. Split off these interactions for the test set.

C : entify the 10% of users with the least interaction data. Split off all interaction data from these users for the test set.

D : ndomly select 10% of the users. Split off all interaction data from these users for the test set.

Answer: D

Question 5

A data scientist is working on a public sector project for an urban traffic system. While studying the traffic patterns, it is clear to the data scientist that the traffic behavior at each light is correlated, subject to a small stochastic error term. The data scientist must model the traffic behavior to analyze the traffic patterns and reduce congestion.

How will the data scientist MOST effectively model the problem?

A : data scientist should obtain a correlated equilibrium policy by formulating this problem as a multi-agent reinforcement learning problem.

B : data scientist should obtain the optimal equilibrium policy by formulating this problem as a single-agent reinforcement learning problem.

C : ther than finding an equilibrium policy, the data scientist should obtain accurate predictors of traffic flow by using historical data through a supervised learning approach.

D : ther than finding an equilibrium policy, the data scientist should obtain accurate predictors of traffic flow by using unlabeled simulated data representing the new traffic patterns in the city and applying an unsupervised learning approach.

Answer: D