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

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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: 281 Q&A's Shared By: jace
Question 64

A Machine Learning Specialist is working with multiple data sources containing billions of records that need to be joined. What feature engineering and model development approach should the Specialist take with a dataset this large?

Options:

A.

Use an Amazon SageMaker notebook for both feature engineering and model development

B.

Use an Amazon SageMaker notebook for feature engineering and Amazon ML for model development

C.

Use Amazon EMR for feature engineering and Amazon SageMaker SDK for model development

D.

Use Amazon ML for both feature engineering and model development.

Discussion
Question 65

Which of the following metrics should a Machine Learning Specialist generally use to compare/evaluate machine learning classification models against each other?

Options:

A.

Recall

B.

Misclassification rate

C.

Mean absolute percentage error (MAPE)

D.

Area Under the ROC Curve (AUC)

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Question 66

A Machine Learning Specialist is required to build a supervised image-recognition model to identify a cat. The ML Specialist performs some tests and records the following results for a neural network-based image classifier:

Total number of images available = 1,000 Test set images = 100 (constant test set)

The ML Specialist notices that, in over 75% of the misclassified images, the cats were held upside down by their owners.

Which techniques can be used by the ML Specialist to improve this specific test error?

Options:

A.

Increase the training data by adding variation in rotation for training images.

B.

Increase the number of epochs for model training.

C.

Increase the number of layers for the neural network.

D.

Increase the dropout rate for the second-to-last layer.

Discussion
Question 67

A company has an ecommerce website with a product recommendation engine built in TensorFlow. The recommendation engine endpoint is hosted by Amazon SageMaker. Three compute-optimized instances support the expected peak load of the website.

Response times on the product recommendation page are increasing at the beginning of each month. Some users are encountering errors. The website receives the majority of its traffic between 8 AM and 6 PM on weekdays in a single time zone.

Which of the following options are the MOST effective in solving the issue while keeping costs to a minimum? (Choose two.)

Options:

A.

Configure the endpoint to use Amazon Elastic Inference (EI) accelerators.

B.

Create a new endpoint configuration with two production variants.

C.

Configure the endpoint to automatically scale with the Invocations Per Instance metric.

D.

Deploy a second instance pool to support a blue/green deployment of models.

E.

Reconfigure the endpoint to use burstable instances.

Discussion
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