Free Amazon MLS-C01 Exam Questions

Question 1

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 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 large consumer goods manufacturer has the following products on sale

* 34 different toothpaste variants

* 48 different toothbrush variants

* 43 different mouthwash variants

The entire sales history of all these products is available in Amazon S3 Currently, the company is using custom-built autoregressive integrated moving average (ARIMA) models to forecast demand for these products The company wants to predict the demand for a new product that will soon be launched

Which solution should a Machine Learning Specialist apply?

A : ain a custom ARIMA model to forecast demand for the new product.

B : ain an Amazon SageMaker DeepAR algorithm to forecast demand for the new product

C : ain an Amazon SageMaker k-means clustering algorithm to forecast demand for the new product.

D : ain a custom XGBoost model to forecast demand for the new product

Answer: B

Question 4

An interactive online dictionary wants to add a widget that displays words used in similar contexts. A Machine Learning Specialist is asked to provide word features for the downstream nearest neighbor model powering the widget.

What should the Specialist do to meet these requirements?

A : eate one-hot word encoding vectors.

B : oduce a set of synonyms for every word using Amazon Mechanical Turk.

C : eate word embedding factors that store edit distance with every other word.

D : wnload word embedding's pre-trained on a large corpus.

Answer: D

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