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NEW QUESTION: 1

A. Option A
B. Option D
C. Option C
D. Option B
Answer: D

NEW QUESTION: 2
HOTSPOT
Your company has four offices. The offices are located in Montreal, Seattle, New York, and Miami.
Users access all of the web-based resources by using web proxy servers. The IP addresses of the web proxies at each office are configured as shown in the following table.

The connections to the web proxies are balanced by using round-robin DNS.
The company plans to deploy a new application. The new application has a farm of front-end web servers that connect to a back-end application server. When a session to a web server is established, the web server stores data until the session closes. Once the session closes, the data is sent to the application server.
You need to ensure that the incoming sessions to the web server farm are distributed among the web servers. The solution must ensure that if a web server fails, the users are NOT directed to the failed server.
How should you configure the port rule? To answer, select the appropriate options in the answer area.
Hot Area:

Answer:
Explanation:

NEW QUESTION: 3
A Machine Learning team uses Amazon SageMaker to train an Apache MXNet handwritten digit classifier model using a research dataset. The team wants to receive a notification when the model is overfitting. Auditors want to view the Amazon SageMaker log activity report to ensure there are no unauthorized API calls.
What should the Machine Learning team do to address the requirements with the least amount of code and fewest steps?
A. Use AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.
B. Use AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Set up Amazon SNS to receive a notification when the model is overfitting
C. Implement an AWS Lambda function to log Amazon SageMaker API calls to AWS CloudTrail.
Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.
D. Implement an AWS Lambda function to log Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.
Answer: A
Explanation:
Log Amazon SageMaker API Calls with AWS CloudTrail
https://docs.aws.amazon.com/sagemaker/latest/dg/logging-using-cloudtrail.html

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