Free Amazon MLS-C01 Exam Questions

Question 1

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 2

A retail company uses a machine learning (ML) model for daily sales forecasting. The model has provided inaccurate results for the past 3 weeks. At the end of each day, an AWS Glue job consolidates the input data that is used for the forecasting with the actual daily sales data and the predictions of the model. The AWS Glue job stores the data in Amazon S3.

The company's ML team determines that the inaccuracies are occurring because of a change in the value distributions of the model features. The ML team must implement a solution that will detect when this type of change occurs in the future.

Which solution will meet these requirements with the LEAST amount of operational overhead?

A : Use Amazon SageMaker Model Monitor to create a data quality baseline. Confirm that the emit_metrics option is set to Enabled in the baseline constraints file. Set up an Amazon CloudWatch alarm for the metric.

B : Use Amazon SageMaker Model Monitor to create a model quality baseline. Confirm that the emit_metrics option is set to Enabled in the baseline constraints file. Set up an Amazon CloudWatch alarm for the metric.

C : Use Amazon SageMaker Debugger to create rules to capture feature values Set up an Amazon CloudWatch alarm for the rules.

D : Use Amazon CloudWatch to monitor Amazon SageMaker endpoints. Analyze logs in Amazon CloudWatch Logs to check for data drift.

Answer: A

Question 3

A Machine Learning Specialist is building a prediction model for a large number of features using linear models, such as linear regression and logistic regression During exploratory data analysis the Specialist observes that many features are highly correlated with each other This may make the model unstable

What should be done to reduce the impact of having such a large number of features?

A : rform one-hot encoding on highly correlated features

B : matrix multiplication on highly correlated features.

C : eate a new feature space using principal component analysis (PCA)

D : ply the Pearson correlation coefficient

Answer: C

Question 4

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 5

An insurance company developed a new experimental machine learning (ML) model to replace an existing model that is in production. The company must validate the quality of predictions from the new experimental model in a production environment before the company uses the new experimental model to serve general user requests.

New one model can serve user requests at a time. The company must measure the performance of the new experimental model without affecting the current live traffic.

Which solution will meet these requirements?

A : A/B testing

B : Canary release

C : Shadow deployment

D : Blue/green deployment

Answer: D