Free Amazon MLS-C01 Exam Dumps and MLS-C01 Exam Questions

Question No: 1

MultipleChoice

A data scientist has been running an Amazon SageMaker notebook instance for a few weeks. During this time, a new version of Jupyter Notebook was released along with additional software updates. The security team mandates that all running SageMaker notebook instances use the latest security and software updates provided by SageMaker.

How can the data scientist meet this requirements?

Options

ACall the CreateNotebookInstanceLifecycleConfig API operation

BCreate a new SageMaker notebook instance and mount the Amazon Elastic Block Store (Amazon EBS) volume from the original instance

CStop and then restart the SageMaker notebook instance

DCall the UpdateNotebookInstanceLifecycleConfig API operation

Question No: 2

MultipleChoice

A company that promotes healthy sleep patterns by providing cloud-connected devices currently hosts a sleep

tracking application on AWS. The application collects device usage information from device users. The

company's Data Science team is building a machine learning model to predict if and when a user will stop

utilizing the company's devices. Predictions from this model are used by a downstream application that

determines the best approach for contacting users.

The Data Science team is building multiple versions of the machine learning model to evaluate each version

against the company's business goals. To measure long-term effectiveness, the team wants to run multiple

served by the models.

Which solution satisfies these requirements with MINIMAL effort?

Options

ABuild and host multiple models in Amazon SageMaker. Create multiple Amazon SageMaker endpoints, one
for each model. Programmatically control invoking different models for inference at the application layer.

BBuild and host multiple models in Amazon SageMaker. Create an Amazon SageMaker endpoint
configuration with multiple production variants. Programmatically control the portion of the inferences
served by the multiple models by updating the endpoint configuration.

CBuild and host multiple models in Amazon SageMaker Neo to take into account different types of medical
devices. Programmatically control which model is invoked for inference based on the medical device type.

DBuild and host multiple models in Amazon SageMaker. Create a single endpoint that accesses multiple
models. Use Amazon SageMaker batch transform to control invoking the different models through the
single endpoint.

Question No: 3

MultipleChoice

A Data Engineer needs to build a model using a dataset containing customer credit card information.

How can the Data Engineer ensure the data remains encrypted and the credit card information is secure?

Use a custom encryption algorithm to encrypt the data and store the data on an Amazon SageMaker

instance in a VPC. Use the SageMaker DeepAR algorithm to randomize the credit card numbers.

Options

BUse an IAM policy to encrypt the data on the Amazon S3 bucket and Amazon Kinesis to automatically
discard credit card numbers and insert fake credit card numbers.

CUse an Amazon SageMaker launch configuration to encrypt the data once it is copied to the SageMaker
instance in a VPC. Use the SageMaker principal component analysis (PCA) algorithm to reduce the length
of the credit card numbers.

DUse AWS KMS to encrypt the data on Amazon S3 and Amazon SageMaker, and redact the credit card numbers from the customer data with AWS Glue.

Question No: 4

MultipleChoice

An Machine Learning Specialist discover the following statistics while experimenting on a model.

What can the Specialist from the experiments?

Options

AThe model In Experiment 1 had a high variance error lhat was reduced in Experiment 3 by regularization Experiment 2 shows that there is minimal bias error in Experiment 1

BThe model in Experiment 1 had a high bias error that was reduced in Experiment 3 by regularization Experiment 2 shows that there is minimal variance error in Experiment 1

CThe model in Experiment 1 had a high bias error and a high variance error that were reduced in Experiment 3 by regularization Experiment 2 shows thai high bias cannot be reduced by increasing layers and neurons in the model

DThe model in Experiment 1 had a high random noise error that was reduced in Expenment 3 by regularization Expenment 2 shows that random noise cannot be reduced by increasing layers and neurons in the model

Question No: 5

MultipleChoice

A company is using Amazon Polly to translate plaintext documents to speech for automated company announcements However company acronyms are being mispronounced in the current documents How should a Machine Learning Specialist address this issue for future documents'?

Options

AConvert current documents to SSML with pronunciation tags

BCreate an appropriate pronunciation lexicon.

COutput speech marks to guide in pronunciation

DUse Amazon Lex to preprocess the text files for pronunciation