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MLS-C01 Exam Dumps : AWS Certified Machine Learning - Specialty

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Amazon Web Services MLS-C01 Exam Dumps FAQs

Q. # 1: What is the Amazon Web Services MLS-C01 Exam?

The mazon Web Services MLS-C01 Exam validates expertise in building, training, tuning, and deploying machine learning models on AWS. It's designed for individuals with hands-on experience in ML or deep learning workloads on AWS.

Q. # 2: Who should take the Amazon Web Services MLS-C01 Exam?

The Amazon Web Services MLS-C01 exam is ideal for individuals with at least two years of hands-on experience developing, architecting, and running machine learning (ML) or deep learning (DL) workloads on the AWS Cloud. It caters to professionals like:

  • ML engineers
  • Data scientists
  • ML architects
  • Solution architects working with ML

Q. # 3: What topics are covered in the Amazon Web Services MLS-C01 Exam?

The Amazon Web Services MLS-C01 exam delves into various aspects of building, training, deploying, and managing ML workloads on AWS. Key areas include:

  • ML workflow and infrastructure
  • Data ingestion and pre-processing
  • Model training and evaluation
  • Model deployment and optimization
  • Machine learning security

Q. # 4: How many questions are on the Amazon Web Services MLS-C01 Exam?

The Amazon Web Services MLS-C01 exam consists of 65 questions.

Q. # 5: How long is the Amazon Web Services MLS-C01 Exam?

The Amazon Web Services MLS-C01 exam has a duration of 180 minutes.

Q. # 6: What is the passing score for the Amazon Web Services MLS-C01 Exam?

The passing score for the Amazon Web Services MLS-C01 exam is 750 out of 1000.

Q. # 7: What is the difference between Amazon Web Services MLS-C01 and AXS-C01 Exams?

Here's a comparison between the Amazon Web Services Certified Machine Learning - Specialty (MLS-C01) Exam and the Amazon Web Services Certified Alexa Skill Builder - Specialty (AXS-C01) Exam:

  • Amazon Web Services MLS-C01 Exam: The Amazon Web Services MLS-C01 Exam is tailored for individuals with a strong grasp of machine learning, requiring hands-on experience with ML or deep learning workloads on AWS. It validates your skills in building, training, and deploying machine learning models.
  • Amazon Web Services AXS-C01 Exam: The Amazon Web Services AXS-C01 Exam aimed at developers in the Alexa ecosystem, this exam tests your ability to design, test, and publish Alexa skills. It's perfect for those with a background in voice-first design and user experience within the Alexa Skills Kit.

Q. # 8: How can CertsTopics help me prepare for the Amazon Web Services MLS-C01 Exam?

CertsTopics provides comprehensive MLS-C01 study materials, including Exam Dumps, Questions and Answers, and Practice Tests. With a smooth purchasing process, you can access our MLS-C01 preparation materials instantly after adding them to your cart and completing the payment.

Q. # 9: Does CertsTopics provide any demo for Amazon Web Services MLS-C01 PDF questions?

CertsTopics provides sample MLS-C01 PDF questions and a demo of our testing engine to help candidates understand the quality and format of our MLS-C01 study materials before purchase.

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Yes, CertsTopics often provides discounts and promotions. Check the website frequently for the latest deals to get the best value on MLS-C01 exam dumps and practice tests.

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AWS Certified Machine Learning - Specialty Questions and Answers

Question 1

An e-commerce company needs a customized training model to classify images of its shirts and pants products The company needs a proof of concept in 2 to 3 days with good accuracy Which compute choice should the Machine Learning Specialist select to train and achieve good accuracy on the model quickly?

Options:

A.

m5 4xlarge (general purpose)

B.

r5.2xlarge (memory optimized)

C.

p3.2xlarge (GPU accelerated computing)

D.

p3 8xlarge (GPU accelerated computing)

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Question 2

A credit card company wants to build a credit scoring model to help predict whether a new credit card applicant

will default on a credit card payment. The company has collected data from a large number of sources with

thousands of raw attributes. Early experiments to train a classification model revealed that many attributes are

highly correlated, the large number of features slows down the training speed significantly, and that there are

some overfitting issues.

The Data Scientist on this project would like to speed up the model training time without losing a lot of

information from the original dataset.

Which feature engineering technique should the Data Scientist use to meet the objectives?

Options:

A.

Run self-correlation on all features and remove highly correlated features

B.

Normalize all numerical values to be between 0 and 1

C.

Use an autoencoder or principal component analysis (PCA) to replace original features with new features

D.

Cluster raw data using k-means and use sample data from each cluster to build a new dataset

Question 3

An ecommerce company has used Amazon SageMaker to deploy a factorization machines (FM) model to suggest products for customers. The company's data science team has developed two new models by using the TensorFlow and PyTorch deep learning frameworks. The company needs to use A/B testing to evaluate the new models against the deployed model.

...required A/B testing setup is as follows:

• Send 70% of traffic to the FM model, 15% of traffic to the TensorFlow model, and 15% of traffic to the Py Torch model.

• For customers who are from Europe, send all traffic to the TensorFlow model

..sh architecture can the company use to implement the required A/B testing setup?

Options:

A.

Create two new SageMaker endpoints for the TensorFlow and PyTorch models in addition to the existing SageMaker endpoint. Create an Application Load Balancer Create a target group for each endpoint. Configure listener rules and add weight to the target groups. To send traffic to the TensorFlow model for customers who are from Europe, create an additional listener rule to forward traffic to the TensorFlow target group.

B.

Create two production variants for the TensorFlow and PyTorch models. Create an auto scaling policy and configure the desired A/B weights to direct traffic to each production variant Update the existing SageMaker endpoint with the auto scaling policy. To send traffic to the TensorFlow model for customers who are from Europe, set the TargetVariant header in the request to point to the variant name of the TensorFlow model.

C.

Create two new SageMaker endpoints for the TensorFlow and PyTorch models in addition to the existing SageMaker endpoint. Create a Network Load Balancer. Create a target group for each endpoint. Configure listener rules and add weight to the target groups. To send traffic to the TensorFlow model for customers who are from Europe, create an additional listener rule to forward traffic to the TensorFlow target group.

D.

Create two production variants for the TensorFlow and PyTorch models. Specify the weight for each production variant in the SageMaker endpoint configuration. Update the existing SageMaker endpoint with the new configuration. To send traffic to the TensorFlow model for customers who are from Europe, set the TargetVariant header in the request to point to the variant name of the TensorFlow model.