A. Option D
B. Option C
C. Option B
D. Option A
Answer: C
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NEW QUESTION: 1
A. Option D
B. Option C
C. Option B
D. Option A
Answer: C
NEW QUESTION: 2
A company is using Amazon Machine Learning as part of a medical software application. The application will predict the most likely blood type for a patient based on a variety of other clinical tests that are available when blood type knowledge is unavailable.
What is the appropriate model choice and target attribute combination for the problem?
A. Regression model with a numeric target attribute
B. Multi-class classification model with a categorical target attribute
C. Binary Classification with a categorical target attribute
D. K-Nearest Neighbors model with a multi-class target attribute
Answer: C
NEW QUESTION: 3
You have three Amazon EC2 instances with Elastic IP addresses in the US East (Virginia) region, and you want to distribute requests across all three IPs evenly for users for whom US East (Virginia) is the appropriate region.
How many EC2 instances would be sufficient to distribute requests in other regions?
A. 0
B. 1
C. 2
D. 3
Answer: A
Explanation:
If your application is running on Amazon EC2 instances in two or more Amazon EC2 regions, and if you have more than one Amazon EC2 instance in one or more regions, you can use latency-based routing to route traffic to the correct region and then use weighted resource record sets to route traffic to instances within the region based on weights that you specify.
For example, suppose you have three Amazon EC2 instances with Elastic IP addresses in the US East
(Virginia) region and you want to distribute requests across all three IPs evenly for users for whom US
East (Virginia) is the appropriate region. Just one Amazon EC2 instance is sufficient in the other regions, although you can apply the same technique to many regions at once.
Reference: http://docs.aws.amazon.com/Route53/latest/DeveloperGuide/Tutorials.html