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Amazon Web Services Updated MLS-C01 Exam Questions and Answers by baxter

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Amazon Web Services MLS-C01 Exam Overview :

Exam Name: AWS Certified Machine Learning - Specialty
Exam Code: MLS-C01 Dumps
Vendor: Amazon Web Services Certification: AWS Certified Specialty
Questions: 307 Q&A's Shared By: baxter
Question 12

A machine learning (ML) specialist is building a credit score model for a financial institution. The ML specialist has collected data for the previous 3 years of transactions and third-party metadata that is related to the transactions.

After the ML specialist builds the initial model, the ML specialist discovers that the model has low accuracy for both the training data and the test data. The ML specialist needs to improve the accuracy of the model.

Which solutions will meet this requirement? (Select TWO.)

Options:

A.

Increase the number of passes on the existing training data. Perform more hyperparameter tuning.

B.

Increase the amount of regularization. Use fewer feature combinations.

C.

Add new domain-specific features. Use more complex models.

D.

Use fewer feature combinations. Decrease the number of numeric attribute bins.

E.

Decrease the amount of training data examples. Reduce the number of passes on the existing training data.

Discussion
Question 13

A machine learning engineer is building a bird classification model. The engineer randomly separates a dataset into a training dataset and a validation dataset. During the training phase, the model achieves very high accuracy. However, the model did not generalize well during validation of the validation dataset. The engineer realizes that the original dataset was imbalanced.

What should the engineer do to improve the validation accuracy of the model?

Options:

A.

Perform stratified sampling on the original dataset.

B.

Acquire additional data about the majority classes in the original dataset.

C.

Use a smaller, randomly sampled version of the training dataset.

D.

Perform systematic sampling on the original dataset.

Discussion
Question 14

A Machine Learning Specialist is training a model to identify the make and model of vehicles in images The Specialist wants to use transfer learning and an existing model trained on images of general objects The Specialist collated a large custom dataset of pictures containing different vehicle makes and models.

What should the Specialist do to initialize the model to re-train it with the custom data?

Options:

A.

Initialize the model with random weights in all layers including the last fully connected layer

B.

Initialize the model with pre-trained weights in all layers and replace the last fully connected layer.

C.

Initialize the model with random weights in all layers and replace the last fully connected layer

D.

Initialize the model with pre-trained weights in all layers including the last fully connected layer

Discussion
Question 15

A company is building a demand forecasting model based on machine learning (ML). In the development stage, an ML specialist uses an Amazon SageMaker notebook to perform feature engineering during work hours that consumes low amounts of CPU and memory resources. A data engineer uses the same notebook to perform data preprocessing once a day on average that requires very high memory and completes in only 2 hours. The data preprocessing is not configured to use GPU. All the processes are running well on an ml.m5.4xlarge notebook instance.

The company receives an AWS Budgets alert that the billing for this month exceeds the allocated budget.

Which solution will result in the MOST cost savings?

Options:

A.

Change the notebook instance type to a memory optimized instance with the same vCPU number as the ml.m5.4xlarge instance has. Stop the notebook when it is not in use. Run both data preprocessing and feature engineering development on that instance.

B.

Keep the notebook instance type and size the same. Stop the notebook when it is not in use. Run data preprocessing on a P3 instance type with the same memory as the ml.m5.4xlarge instance by using Amazon SageMaker Processing.

C.

Change the notebook instance type to a smaller general-purpose instance. Stop the notebook when it is not in use. Run data preprocessing on an ml. r5 instance with the same memory size as the ml.m5.4xlarge instance by using Amazon SageMaker Processing.

D.

Change the notebook instance type to a smaller general-purpose instance. Stop the notebook when it is not in use. Run data preprocessing on an R5 instance with the same memory size as the ml.m5.4xlarge instance by using the Reserved Instance option.

Discussion
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