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To take the AWS Certified Machine Learning - Specialty exam, candidates must have a minimum of one year of experience in designing and implementing machine learning models on the AWS platform. They should also have a strong understanding of machine learning algorithms, data modeling, and data processing techniques. AWS-Certified-Machine-Learning-Specialty Exam consists of 65 multiple-choice and multiple-response questions, and candidates have 3 hours to complete it.

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Achieving the Amazon MLS-C01 certification demonstrates the candidate's expertise in machine learning on the AWS platform and opens up various career opportunities in the field of machine learning. AWS Certified Machine Learning - Specialty certification is ideal for professionals such as data scientists, software developers, and machine learning engineers who want to specialize in the field of machine learning on the AWS platform.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q160-Q165):

NEW QUESTION # 160
A manufacturer is operating a large number of factories with a complex supply chain relationship where unexpected downtime of a machine can cause production to stop at several factories. A data scientist wants to analyze sensor data from the factories to identify equipment in need of preemptive maintenance and then dispatch a service team to prevent unplanned downtime. The sensor readings from a single machine can include up to 200 data points including temperatures, voltages, vibrations, RPMs, and pressure readings.
To collect this sensor data, the manufacturer deployed Wi-Fi and LANs across the factories. Even though many factory locations do not have reliable or high-speed internet connectivity, the manufacturer would like to maintain near-real-time inference capabilities.
Which deployment architecture for the model will address these business requirements?

A. Deploy the model on AWS IoT Greengrass in each factory. Run sensor data through this model to infer which machines need maintenance.
B. Deploy the model in Amazon SageMaker and use an IoT rule to write data to an Amazon DynamoDB table. Consume a DynamoDB stream from the table with an AWS Lambda function to invoke the endpoint.
C. Deploy the model to an Amazon SageMaker batch transformation job. Generate inferences in a daily batch report to identify machines that need maintenance.
D. Deploy the model in Amazon SageMaker. Run sensor data through this model to predict which machines need maintenance.

Answer: A

Explanation:
AWS IoT Greengrass is a service that extends AWS to edge devices, such as sensors and machines, so they can act locally on the data they generate, while still using the cloud for management, analytics, and durable storage. AWS IoT Greengrass enables local device messaging, secure data transfer, and local computing using AWS Lambda functions and machine learning models. AWS IoT Greengrass can run machine learning inference locally on devices using models that are created and trained in the cloud. This allows devices to respond quickly to local events, even when they are offline or have intermittent connectivity. Therefore, option B is the best deployment architecture for the model to address the business requirements of the manufacturer.
Option A is incorrect because deploying the model in Amazon SageMaker would require sending the sensor data to the cloud for inference, which would not work well for factory locations that do not have reliable or high-speed internet connectivity. Moreover, this option would not provide near-real-time inference capabilities, as there would be latency and bandwidth issues involved in transferring the data to and from the cloud. Option C is incorrect because deploying the model to an Amazon SageMaker batch transformation job would not provide near-real-time inference capabilities, as batch transformation is an asynchronous process that operates on large datasets. Batch transformation is not suitable for streaming data that requires low- latency responses. Option D is incorrect because deploying the model in Amazon SageMaker and using an IoT rule to write data to an Amazon DynamoDB table would also require sending the sensor data to the cloud for inference, which would have the same drawbacks as option A. Moreover, this option would introduce additional complexity and cost by involving multiple services, such as IoT Core, DynamoDB, and Lambda.
AWS Greengrass Machine Learning Inference - Amazon Web Services
Machine learning components - AWS IoT Greengrass
What is AWS Greengrass? | AWS IoT Core | Onica
GitHub - aws-samples/aws-greengrass-ml-deployment-sample
AWS IoT Greengrass Architecture and Its Benefits | Quick Guide - XenonStack

NEW QUESTION # 161
An ecommerce company wants to train a large image classification model with 10.000 classes. The company runs multiple model training iterations and needs to minimize operational overhead and cost. The company also needs to avoid loss of work and model retraining.
Which solution will meet these requirements?

A. Create the training jobs as AWS Batch jobs that use Amazon EC2 Spot Instances in a managed compute environment.
B. Use Amazon EC2 Spot Instances to run the training jobs. Use a Spot Instance interruption notice to save a snapshot of the model to Amazon S3 before an instance is terminated.
C. Use managed spot training in Amazon SageMaker. Launch the training jobs with checkpointing enabled.
D. Use AWS Lambda to run the training jobs. Save model weights to Amazon S3.

Answer: C

Explanation:
Amazon SageMaker managed spot training allows for cost-effective training by utilizing Spot Instances, which are lower-cost EC2 instances that can be interrupted when demand is high. By enabling checkpointing in SageMaker, the company can save intermediate model states to Amazon S3, allowing training to resume from the last checkpoint if interrupted. This solution minimizes operational overhead by automating the checkpointing process and resuming work after interruptions, reducing the need for retraining from scratch.
This setup provides a reliable and cost-efficient approach to training large models with minimal operational overhead and risk of data loss.

NEW QUESTION # 162
A company is setting up an Amazon SageMaker environment. The corporate data security policy does not allow communication over the internet.
How can the company enable the Amazon SageMaker service without enabling direct internet access to Amazon SageMaker notebook instances?

A. Create Amazon SageMaker VPC interface endpoints within the corporate VPC.
B. Route Amazon SageMaker traffic through an on-premises network.
C. Create a NAT gateway within the corporate VPC.
D. Create VPC peering with Amazon VPC hosting Amazon SageMaker.

Answer: A

Explanation:
Explanation
To enable the Amazon SageMaker service without enabling direct internet access to Amazon SageMaker notebook instances, the company should create Amazon SageMaker VPC interface endpoints within the corporate VPC. A VPC interface endpoint is a gateway that enables private connections between the VPC and supported AWS services without requiring an internet gateway, a NAT device, a VPN connection, or an AWS Direct Connect connection. The instances in the VPC do not need to connect to the public internet in order to communicate with the Amazon SageMaker service. The VPC interface endpoint connects the VPC directly to the Amazon SageMaker service using AWS PrivateLink, which ensures that the traffic between the VPC and the service does not leave the AWS network1.
References:
1: Connect to SageMaker Within your VPC - Amazon SageMaker

NEW QUESTION # 163
A Machine Learning Specialist is using an Amazon SageMaker notebook instance in a private subnet of a corporate VPC. The ML Specialist has important data stored on the Amazon SageMaker notebook instance's Amazon EBS volume, and needs to take a snapshot of that EBS volume. However, the ML Specialist cannot find the Amazon SageMaker notebook instance's EBS volume or Amazon EC2 instance within the VPC.
Why is the ML Specialist not seeing the instance visible in the VPC?

A. Amazon SageMaker notebook instances are based on the EC2 instances within the customer account, but they run outside of VPCs.
B. Amazon SageMaker notebook instances are based on the Amazon ECS service within customer accounts.
C. Amazon SageMaker notebook instances are based on AWS ECS instances running within AWS service accounts.
D. Amazon SageMaker notebook instances are based on EC2 instances running within AWS service accounts.

Answer: D

Explanation:
https://docs.aws.amazon.com/sagemaker/latest/dg/gs-setup-working-env.html

NEW QUESTION # 164
A Data Scientist is building a linear regression model and will use resulting p-values to evaluate the statistical significance of each coefficient. Upon inspection of the dataset, the Data Scientist discovers that most of the features are normally distributed. The plot of one feature in the dataset is shown in the graphic.

What transformation should the Data Scientist apply to satisfy the statistical assumptions of the linear regression model?

A. Logarithmic transformation
B. Exponential transformation
C. Sinusoidal transformation
D. Polynomial transformation

Answer: A

Explanation:
Explanation
The plot in the graphic shows a right-skewed distribution, which violates the assumption of normality for linear regression. To correct this, the Data Scientist should apply a logarithmic transformation to the feature.
This will help to make the distribution more symmetric and closer to a normal distribution, which is a key assumption for linear regression. References:
Linear Regression
Linear Regression with Amazon Machine Learning
Machine Learning on AWS

NEW QUESTION # 165
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AWS-Certified-Machine-Learning-Specialty Reliable Test Testking - AWS-Certified-Machine-Learning-Specialty Exam Sample Online

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q160-Q165):

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