Free Amazon MLS-C01 Exam Questions

Question 1

Given the following confusion matrix for a movie classification model, what is the true class frequency for Romance and the predicted class frequency for Adventure?

A : true class frequency for Romance is 77.56% and the predicted class frequency for Adventure is 20 85%

B : true class frequency for Romance is 57.92% and the predicted class frequency for Adventure is 1312%

C : true class frequency for Romance is 0 78 and the predicted class frequency for Adventure is (0 47 - 0.32).

D : true class frequency for Romance is 77.56% * 0.78 and the predicted class frequency for Adventure is 20 85% ' 0.32

Answer: B

Question 2

n online delivery company wants to choose the fastest courier for each delivery at the moment an order is placed. The company wants to implement this feature for existing users and new users of its application. Data scientists have trained separate models with XGBoost for this purpose, and the models are stored in Amazon S3. There is one model for each city where the company operates.

Operation engineers are hosting these models in Amazon EC2 for responding to the web client requests, with one instance for each model, but the instances have only a 5% utilization in CPU and memory. The operation engineers want to avoid managing unnecessary resources.

Which solution will enable the company to achieve its goal with the LEAST operational overhead?

A : Create an Amazon SageMaker notebook instance for pulling all the models from Amazon S3 using the boto3 library. Remove the existing instances and use the notebook to perform a SageMaker batch transform for performing inferences offline for all the possible users in all the cities. Store the results in different files in Amazon S3. Point the web client to the files.

B : Prepare an Amazon SageMaker Docker container based on the open-source multi-model server. Remove the existing instances and create a multi-model endpoint in SageMaker instead, pointing to the S3 bucket containing all the models. Invoke the endpoint from the web client at runtime, specifying the TargetModel parameter according to the city of each request.

C : Keep only a single EC2 instance for hosting all the models. Install a model server in the instance and load each model by pulling it from Amazon S3. Integrate the instance with the web client using Amazon API Gateway for responding to the requests in real time, specifying the target resource according to the city of each request.

D : Prepare a Docker container based on the prebuilt images in Amazon SageMaker. Replace the existing instances with separate SageMaker endpoints, one for each city where the company operates. Invoke the endpoints from the web client, specifying the URL and EndpointName parameter according to the city of each request.

Answer: B

Question 3

A large mobile network operating company is building a machine learning model to predict customers who are likely to unsubscribe from the service. The company plans to offer an incentive for these customers as the cost of churn is far greater than the cost of the incentive.

The model produces the following confusion matrix after evaluating on a test dataset of 100 customers:

Based on the model evaluation results, why is this a viable model for production?

A : model is 86?curate and the cost incurred by the company as a result of false negatives is less than the false positives.

B : precision of the model is 86%, which is less than the accuracy of the model.

C : model is 86?curate and the cost incurred by the company as a result of false positives is less than the false negatives.

D : precision of the model is 86%, which is greater than the accuracy of the model.

Answer: A

Question 4

A Machine Learning Specialist is assigned a TensorFlow project using Amazon SageMaker for training, and needs to continue working for an extended period with no Wi-Fi access.

Which approach should the Specialist use to continue working?

A : tall Python 3 and boto3 on their laptop and continue the code development using that environment.

B : wnload the TensorFlow Docker container used in Amazon SageMaker from GitHub to their local environment, and use the Amazon SageMaker Python SDK to test the code.

C : wnload TensorFlow from tensorflow.org to emulate the TensorFlow kernel in the SageMaker environment.

D : wnload the SageMaker notebook to their local environment then install Jupyter Notebooks on their laptop and continue the development in a local notebook.

Answer: B

Question 5

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