Free Amazon MLS-C01 Exam Questions

Question 1

A data scientist receives a collection of insurance claim records. Each record includes a claim ID. the final outcome of the insurance claim, and the date of the final outcome.

The final outcome of each claim is a selection from among 200 outcome categories. Some claim records include only partial information. However, incomplete claim records include only 3 or 4 outcome categories from among the 200 available outcome categories. The collection includes hundreds of records for each outcome category. The records are from the previous 3 years.

The data scientist must create a solution to predict the number of claims that will be in each outcome category every month, several months in advance.

Which solution will meet these requirements?

A : Perform classification every month by using supervised learning of the 200 outcome categories based on claim contents.

B : Perform reinforcement learning by using claim IDs and dates. Instruct the insurance agents who submit the claim records to estimate the expected number of claims in each outcome category every month.

C : Perform forecasting by using claim IDs and dates to identify the expected number of claims in each outcome category every month.

D : Perform classification by using supervised learning of the outcome categories for which partial information on claim contents is provided. Perform forecasting by using claim IDs and dates for all other outcome categories.

Answer: C

Question 2

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 3

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

Question 4

A company is running a machine learning prediction service that generates 100 TB of predictions every day A Machine Learning Specialist must generate a visualization of the daily precision-recall curve from the predictions, and forward a read-only version to the Business team.

Which solution requires the LEAST coding effort?

A : n a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3 Give the Business team read-only access to S3

B : nerate daily precision-recall data in Amazon QuickSight, and publish the results in a dashboard shared with the Business team

C : n a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3 Visualize the arrays in Amazon QuickSight, and publish them in a dashboard shared with the Business team

D : nerate daily precision-recall data in Amazon ES, and publish the results in a dashboard shared with the Business team.

Answer: C

Question 5

A company is using Amazon SageMaker to build a machine learning (ML) model to predict customer churn based on customer call transcripts. Audio files from customer calls are located in an on-premises VoIP system that has petabytes of recorded calls. The on-premises infrastructure has high-velocity networking and connects to the company's AWS infrastructure through a VPN connection over a 100 Mbps connection. The company has an algorithm for transcribing customer calls that requires GPUs for inference. The company wants to store these transcriptions in an Amazon S3 bucket in the AWS Cloud for model development. Which solution should an ML specialist use to deliver the transcriptions to the S3 bucket as quickly as possible?

A : der and use an AWS Snowball Edge Compute Optimized device with an NVIDIA Tesla module to run the transcription algorithm. Use AWS DataSync to send the resulting transcriptions to the transcription S3 bucket.

B : der and use an AWS Snowcone device with Amazon EC2 Inf1 instances to run the transcription algorithm. Use AWS DataSync to send the resulting transcriptions to the transcription S3 bucket.

C : der and use AWS Outposts to run the transcription algorithm on GPU-based Amazon EC2 instances. Store the resulting transcriptions in the transcription S3 bucket.

D : AWS DataSync to ingest the audio files to Amazon S3. Create an AWS Lambda function to run the transcription algorithm on the audio files when they are uploaded to Amazon S3. Configure the function to write the resulting transcriptions to the transcription S3 bucket.

Answer: D