Free Amazon MLS-C01 Exam Questions

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 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 2

A Machine Learning team uses Amazon SageMaker to train an Apache MXNet handwritten digit classifier model using a research dataset. The team wants to receive a notification when the model is overfitting. Auditors want to view the Amazon SageMaker log activity report to ensure there are no unauthorized API calls.

What should the Machine Learning team do to address the requirements with the least amount of code and fewest steps?

A : plement an AWS Lambda function to long Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.

B : AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.

C : plement an AWS Lambda function to log Amazon SageMaker API calls to AWS CloudTrail. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.

D : AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Set up Amazon SNS to receive a notification when the model is overfitting.

Answer: B

Question 3

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

Question 4

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 5

A company has a podcast platform that has thousands of users. The company has implemented an anomaly detection algorithm to detect low podcast engagement based on a 10-minute running window of user events such as listening, pausing, and exiting the podcast. A machine learning (ML) specialist is designing the data ingestion of these events with the knowledge that the event payload needs some small transformations before inference. How should the ML specialist design the data ingestion to meet these requirements with the LEAST operational overhead?

A : gest event data by using a GraphQLAPI in AWS AppSync. Store the data in an Amazon DynamoDB table. Use DynamoDB Streams to call an AWS Lambda function to transform the most recent 10 minutes of data before inference.

B : gest event data by using Amazon Kinesis Data Streams. Store the data in Amazon S3 by using Amazon Kinesis Data Firehose. Use AWS Glue to transform the most recent 10 minutes of data before inference.

C : gest event data by using Amazon Kinesis Data Streams. Use an Amazon Kinesis Data Analytics for Apache Flink application to transform the most recent 10 minutes of data before inference.

D : Ingest event data by using Amazon Managed Streaming for Apache Kafka (Amazon MSK). Use an AWS Lambda function to transform the most recent 10 minutes of data before inference

Answer: B