100% Free Amazon MLS-C01 Practice Test Questions

Question 1

A company is setting up an Amazon SageMaker environment. The corporate data security policy does not allow communication over the internet.

How can the company enable the Amazon SageMaker service without enabling direct internet access to Amazon SageMaker notebook instances?

A : Create a NAT gateway within the corporate VPC.

B : Route Amazon SageMaker traffic through an on-premises network.

C : Create Amazon SageMaker VPC interface endpoints within the corporate VPC.

D : Create VPC peering with Amazon VPC hosting Amazon SageMaker.

Answer: A

Question 2

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 : Order 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 : Order 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 : Order 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 : Use 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

Question 3

A company supplies wholesale clothing to thousands of retail stores. A data scientist must create a model that predicts the daily sales volume for each item for each store. The data scientist discovers that more than half of the stores have been in business for less than 6 months. Sales data is highly consistent from week to week. Daily data from the database has been aggregated weekly, and weeks with no sales are omitted from the current dataset. Five years (100 MB) of sales data is available in Amazon S3.

Which factors will adversely impact the performance of the forecast model to be developed, and which actions should the data scientist take to mitigate them? (Choose two.)

A : Detecting seasonality for the majority of stores will be an issue. Request categorical data to relate new stores with similar stores that have more historical data.

B : The sales data does not have enough variance. Request external sales data from other industries to improve the model's ability to generalize.

C : Sales data is aggregated by week. Request daily sales data from the source database to enable building a daily model.

D : The sales data is missing zero entries for item sales. Request that item sales data from the source database include zero entries to enable building the model.

E : Only 100 MB of sales data is available in Amazon S3. Request 10 years of sales data, which would provide 200 MB of training data for the model.

Answer: A,B

Question 4

A financial services company wants to automate its loan approval process by building a machine learning (ML) model. Each loan data point contains credit history from a third-party data source and demographic information about the customer. Each loan approval prediction must come with a report that contains an explanation for why the customer was approved for a loan or was denied for a loan. The company will use Amazon SageMaker to build the model. Which solution will meet these requirements with the LEAST development effort?

A : Use SageMaker Model Debugger to automatically debug the predictions, generate the explanation, and attach the explanation report.

B : Use AWS Lambda to provide feature importance and partial dependence plots. Use the plots to generate and attach the explanation report

C : Use SageMaker Clarify to generate the explanation report. Attach the report to the predicted results.

D : Use custom Amazon CloudWatch metrics to generate the explanation report. Attach the report to the predicted results.

Answer: A

Question 5

A Machine Learning Specialist built an image classification deep learning model. However the Specialist ran into an overfitting problem in which the training and testing accuracies were 99% and 75%r respectively.

How should the Specialist address this issue and what is the reason behind it?

A : The learning rate should be increased because the optimization process was trapped at a local minimum.

B : The dropout rate at the flatten layer should be increased because the model is not generalized enough.

C : The dimensionality of dense layer next to the flatten layer should be increased because the model is not complex enough.

D : The epoch number should be increased because the optimization process was terminated before it reached the global minimum.

Answer: D

Free Practice Amazon MLS-C01 Exam Questions 2025