Free Amazon MLS-C01 Exam Questions

Question 1

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

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

C : 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 2

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 3

A machine learning (ML) specialist has prepared and used a custom container image with Amazon SageMaker to train an image classification model. The ML specialist is performing hyperparameter optimization (HPO) with this custom container image to produce a higher quality image classifier. The ML specialist needs to determine whether HPO with the SageMaker built-in image classification algorithm will produce a better model than the model produced by HPO with the custom container image. All ML experiments and HPO jobs must be invoked from scripts inside SageMaker Studio notebooks. How can the ML specialist meet these requirements in the LEAST amount of time?

A : epare a custom HPO script that runs multiple training jobs in SageMaker Studio in local mode to tune the model of the custom container image. Use the automatic model tuning capability of SageMaker with early stopping enabled to tune the model of the built-in image classification algorithm. Select the model with the best objective metric value

B : SageMaker Autopilot to tune the model of the custom container image. Use the automatic model tuning capability of SageMaker with early stopping enabled to tune the model of the built-in image classification algorithm. Compare the objective metric values of the resulting models of the SageMaker AutopilotAutoML job and the automatic model tuning job. Select the model with the best objective metric value.

C : Use SageMaker Experiments to run and manage multiple training jobs and tune the model of the custom container image. Use the automatic model tuning capability of SageMaker to tune the model of the built-in image classification algorithm. Select the model with the best objective metric value

D : the automatic model tuning capability of SageMaker to tune the models of the custom container image and the built-in image classification algorithm at the same time. Select the model with the best objective metric value.

Answer: B

Question 4

A Machine Learning Specialist is building a convolutional neural network (CNN) that will classify 10 types of animals. The Specialist has built a series of layers in a neural network that will take an input image of an animal, pass it through a series of convolutional and pooling layers, and then finally pass it through a dense and fully connected layer with 10 nodes The Specialist would like to get an output from the neural network that is a probability distribution of how likely it is that the input image belongs to each of the 10 classes

Which function will produce the desired output?

A : opout

B : ooth L1 loss

C : ftmax

D : ctified linear units (ReLU)

Answer: C

Question 5

A bank wants to use a machine learning (ML) model to predict if users will default on credit card payments. The

training data consists of 30,000 labeled records and is evenly balanced between two categories. For the model,

an ML specialist selects the Amazon SageMaker built-in XGBoost algorithm and configures a SageMaker

automatic hyperparameter optimization job with the Bayesian method. The ML specialist uses the validation

accuracy as the objective metric.

When the bank implements the solution with this model, the prediction accuracy is 75%. The bank has given

the ML specialist 1 day to improve the model in production.

Which approach is the FASTEST way to improve the model's accuracy?

A : n a SageMaker incremental training based on the best candidate from the current model's tuning job. Monitor the same metric that was used as the objective metric in the previous tuning, and look for improvements

B : t the Area Under the ROC Curve (AUC) as the objective metric for a new SageMaker automatic hyperparameter tuning job. Use the same maximum training jobs parameter that was used in the previous tuning job.

C : n a SageMaker warm start hyperparameter tuning job based on the current model’s tuning job. Use the same objective metric that was used in the previous tuning.

D : t the F1 score as the objective metric for a new SageMaker automatic hyperparameter tuning job. Double the maximum training jobs parameter that was used in the previous tuning job.

Answer: A