Free Amazon MLS-C01 Exam Questions

Question 1

A data engineer is using AWS Glue to create optimized, secure datasets in Amazon S3. The data science team wants the ability to access the ETL scripts directly from Amazon SageMaker notebooks within a VPC. After this setup is complete, the data science team wants the ability to run the AWS Glue job and invoke the SageMaker training job. Which combination of steps should the data engineer take to meet these requirements? (Choose three.)

A : Create a SageMaker development endpoint in the data science team's VPC.

B : Create an AWS Glue development endpoint in the data science team's VPC

C : Create SageMaker notebooks by using the AWS Glue development endpoint.

D : Create SageMaker notebooks by using the SageMaker console

E : Attach a decryption policy to the SageMaker notebooks.

F : Create an IAM policy and an IAM role for the SageMaker notebooks.

Answer: A,D,F

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 retail company is selling products through a global online marketplace. The company wants to use machine learning (ML) to analyze customer feedback and identify specific areas for improvement. A developer has built a tool that collects customer reviews from the online marketplace and stores them in an Amazon S3 bucket. This process yields a dataset of 40 reviews. A data scientist building the ML models must identify additional sources of data to increase the size of the dataset.

Which data sources should the data scientist use to augment the dataset of reviews? (Choose three.)

A : ails exchanged by customers and the company's customer service agents

B : cial media posts containing the name of the company or its products

C : publicly available collection of news articles

D : publicly available collection of customer reviews

E : oduct sales revenue figures for the company

F : truction manuals for the company's products

Answer: A,B,D

Question 4

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

Question 5

A data scientist is building a new model for an ecommerce company. The model will predict how many minutes it will take to deliver a package. During model training, the data scientist needs to evaluate model performance. Which metrics should the data scientist use to meet this requirement? (Select TWO.)

A : InferenceLatency

B : Mean squared error (MSE)

C : Root mean squared error (RMSE)

D : Precision

E : Accuracy

Answer: B,C