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

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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: 322 Q&A's Shared By: renae
Question 36

A Machine Learning team runs its own training algorithm on Amazon SageMaker. The training algorithm

requires external assets. The team needs to submit both its own algorithm code and algorithm-specific

parameters to Amazon SageMaker.

What combination of services should the team use to build a custom algorithm in Amazon SageMaker?

(Choose two.)

Options:

A.

AWS Secrets Manager

B.

AWS CodeStar

C.

Amazon ECR

D.

Amazon ECS

E.

Amazon S3

Discussion
Question 37

A Machine Learning Specialist wants to determine the appropriate SageMaker Variant Invocations Per Instance setting for an endpoint automatic scaling configuration. The Specialist has performed a load test on a single instance and determined that peak requests per second (RPS) without service degradation is about 20 RPS As this is the first deployment, the Specialist intends to set the invocation safety factor to 0 5

Based on the stated parameters and given that the invocations per instance setting is measured on a per-minute basis, what should the Specialist set as the sageMaker variant invocations Per instance setting?

Options:

A.

10

B.

30

C.

600

D.

2,400

Discussion
Question 38

A data scientist has developed a machine learning translation model for English to Japanese by using Amazon SageMaker's built-in seq2seq algorithm with 500,000 aligned sentence pairs. While testing with sample sentences, the data scientist finds that the translation quality is reasonable for an example as short as five words. However, the quality becomes unacceptable if the sentence is 100 words long.

Which action will resolve the problem?

Options:

A.

Change preprocessing to use n-grams.

B.

Add more nodes to the recurrent neural network (RNN) than the largest sentence's word count.

C.

Adjust hyperparameters related to the attention mechanism.

D.

Choose a different weight initialization type.

Discussion
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Question 39

A machine learning (ML) engineer is preparing a dataset for a classification model. The ML engineer notices that some continuous numeric features have a significantly greater value than most other features. A business expert explains that the features are independently informative and that the dataset is representative of the target distribution.

After training, the model's inferences accuracy is lower than expected.

Which preprocessing technique will result in the GREATEST increase of the model's inference accuracy?

Options:

A.

Normalize the problematic features.

B.

Bootstrap the problematic features.

C.

Remove the problematic features.

D.

Extrapolate synthetic features.

Discussion
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