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

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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Question 37

A data scientist uses an Amazon SageMaker notebook instance to conduct data exploration and analysis. This requires certain Python packages that are not natively available on Amazon SageMaker to be installed on the notebook instance.

How can a machine learning specialist ensure that required packages are automatically available on the notebook instance for the data scientist to use?

Options:

A.

Install AWS Systems Manager Agent on the underlying Amazon EC2 instance and use Systems Manager Automation to execute the package installation commands.

B.

Create a Jupyter notebook file (.ipynb) with cells containing the package installation commands to execute and place the file under the /etc/init directory of each Amazon SageMaker notebook instance.

C.

Use the conda package manager from within the Jupyter notebook console to apply the necessary conda packages to the default kernel of the notebook.

D.

Create an Amazon SageMaker lifecycle configuration with package installation commands and assign the lifecycle configuration to the notebook instance.

Discussion
Question 38

A Machine Learning Specialist is designing a system for improving sales for a company. The objective is to use the large amount of information the company has on users' behavior and product preferences to predict which products users would like based on the users' similarity to other users.

What should the Specialist do to meet this objective?

Options:

A.

Build a content-based filtering recommendation engine with Apache Spark ML on Amazon EMR.

B.

Build a collaborative filtering recommendation engine with Apache Spark ML on Amazon EMR.

C.

Build a model-based filtering recommendation engine with Apache Spark ML on Amazon EMR.

D.

Build a combinative filtering recommendation engine with Apache Spark ML on Amazon EMR.

Discussion
Question 39

A manufacturing company has a production line with sensors that collect hundreds of quality metrics. The company has stored sensor data and manual inspection results in a data lake for several months. To automate quality control, the machine learning team must build an automated mechanism that determines whether the produced goods are good quality, replacement market quality, or scrap quality based on the manual inspection results.

Which modeling approach will deliver the MOST accurate prediction of product quality?

Options:

A.

Amazon SageMaker DeepAR forecasting algorithm

B.

Amazon SageMaker XGBoost algorithm

C.

Amazon SageMaker Latent Dirichlet Allocation (LDA) algorithm

D.

A convolutional neural network (CNN) and ResNet

Discussion
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