Free Amazon MLS-C01 Exam Questions

Question 1

A data scientist is building a new model for an ecommerce company. The model will predict how many minutes it will take to deliver a package. During model training, the data scientist needs to evaluate model performance. Which metrics should the data scientist use to meet this requirement? (Select TWO.)

A : InferenceLatency

B : Mean squared error (MSE)

C : Root mean squared error (RMSE)

D : Precision

E : Accuracy

Answer: B,C

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 : 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

Question 3

The displayed graph is from a foresting model for testing a time series.

Considering the graph only, which conclusion should a Machine Learning Specialist make about the behavior of the model?

A : model predicts both the trend and the seasonality well.

B : model predicts the trend well, but not the seasonality.

C : model predicts the seasonality well, but not the trend.

D : model does not predict the trend or the seasonality well.

Answer: D

Question 4

A Data Scientist is developing a machine learning model to predict future patient outcomes based on information collected about each patient and their treatment plans. The model should output a continuous value as its prediction. The data available includes labeled outcomes for a set of 4,000 patients. The study was conducted on a group of individuals over the age of 65 who have a particular disease that is known to worsen with age.

Initial models have performed poorly. While reviewing the underlying data, the Data Scientist notices that, out of 4,000 patient observations, there are 450 where the patient age has been input as 0. The other features for these observations appear normal compared to the rest of the sample population.

How should the Data Scientist correct this issue?

A : op all records from the dataset where age has been set to 0.

B : place the age field value for records with a value of 0 with the mean or median value from the dataset.

C : op the age feature from the dataset and train the model using the rest of the features.

D : k-means clustering to handle missing features.

Answer: B

Question 5

A machine learning specialist works for a fruit processing company and needs to build a system that

categorizes apples into three types. The specialist has collected a dataset that contains 150 images for each

type of apple and applied transfer learning on a neural network that was pretrained on ImageNet with this

dataset.

The company requires at least 85?curacy to make use of the model.

After an exhaustive grid search, the optimal hyperparameters produced the following:

68?curacy on the training set

67?curacy on the validation set

What can the machine learning specialist do to improve the system's accuracy?

A : Upload the model to an Amazon SageMaker notebook instance and use the Amazon SageMaker HPOfeature to optimize the model's hyperparameters.

B : more data to the training set and retrain the model using transfer learning to reduce the bias.

C : Use a neural network model with more layers that are pretrained on ImageNet and apply transfer learningto increase the variance.

D : ain a new model using the current neural network architecture.

Answer: B