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NEW QUESTION: 1
A Solutions Architect is responsible for redesigning a legacy Java application to improve its availability, data durability, and scalability. Currently, the application runs on a single high- memory Amazon EC2 instance. It accepts HTTP requests from upstream clients, adds them to an in-memory queue, and responds with a 200 status. A separate application thread reads items from the queue, processes them, and persists the results to an Amazon RDS MySQL instance.
The processing time for each item takes 90 seconds on average., most of which is spent waiting on external service calls, but the application is written to process multiple items in parallel.
Traffic to this service is unpredictable. During periods of high load, items may sit in the internal queue for over an hour while the application processes the backing. In addition, the current system has issues with availability and data if the single application node fails.
Clients that access this service cannot be modified. They expect to receive a response to each HTTP request they send within 10 seconds before they will time out and retry the request.
Which approach would improve the availability and durability of the system while decreasing the processing latency and minimizing costs?
A. Create an Amazon API Gateway REST API that uses Lambda proxy integration to pass requests to an AWS Lambda function. Migrate the core processing code to a Lambda function and write a wrapper class that provides a handler method that converts the proxy events to the internal application data model and invokes the processing module.
B. Update the application to use a Redis task queue instead of the in-memory queue. Build a Docker container image for the application. Create an Amazon ECS task definition that includes the application container and a separate container to host Redis. Deploy the new task definition as an ECS service using AWS Fargate and enable Auto Scaling.
C. Create an Amazon API Gateway REST API that uses a service proxy to put items in an Amazon SQS queue. Extract the core processing code from the existing application and update it to pull items from Amazon SQS queue. Extract the core processing code from the existing application and update it to pull items from Amazon SQS instead of an in-memory queue. Deploy the new processing application to smaller EC2 instances within an Auto Scaling group that scales dynamically based on the approximate number of messages in the Amazon SQS queue.
D. Modify the application to use Amazon DynamoDB instead of Amazon RDS. Configure Auto Scaling for the DynamoDB table. Deploy the application within an Auto Scaling group with a scaling policy based on CPU utilization. Back the in-memory queue with a memory-mapped file to an instance store volume and periodically write that file to Amazon S3.
Answer: C
NEW QUESTION: 2
Refer to the exhibit.
When would the EEM applet be triggered?
A. once a month
B. once a minute
C. once a day
D. once an hour
Answer: B
Explanation:
Interface Counter Event Detector-Rate Based Trigger The interface counter Event Detector (ED) adds the ability for an interface event to be
triggered based on a rate of change over a period of time. A rate can be specified both for
the entry value and the exit value.
The "event interface" applet CLI command has been modified to accept three new
keywords:
[entry-type {value | increment | rate}]
[exit-type {value | increment | rate}]
[average-factor <average-factor-value>]
Similar commands are added for Tcl scripts:
[entry_type {value | increment | rate}]
[exit_type {value | increment | rate}]
[average_factor <average-factor-value>]
Applet syntax :
[no] event [<ev-label>] interface name <interface-name> parameter <counter-name>
entry-val <entry-val> entry-op {gt|ge|eq|ne|lt|le} [entry-type {value | increment | rate}]
[exit-comb {or | and}]
[exit-val <exit-val> exit-op {gt|ge|eq|ne|lt|le} exit-type {value | increment | rate}]
[exit-time <exit-time-val>]
poll-interval <poll-int-val>
The following is an example of the rate based trigger in action. This applet monitors for
errors on an interface. If the rate of change averages to two or more over three 60 second
polling cycles, then the interface is reset by doing a shut/no shut. The policy will re-arm
after the rate has dropped below 1.
event manager applet int-rate-test
event interface name FastEthernet0/24 parameter input_errors entry-op ge entry-val 2
entry-type rate exit-op lt
exit-val 1 exit-type rate average-factor 3 poll-interval 60
action 1.0 syslog msg "Interface input error rate for $_interface_name is $_interface_value;
resetting..."
action 2.0 cli command "enable"
action 3.0 cli command "interface $_interface_name"
action 4.0 cli command "shut"
action 5.0 cli command "no shut"
action 6.0 cli command "end"
NEW QUESTION: 3
You have enabled the NetInstall service on a computer with OS X Server, and configured it to store images on the startup volume. In what folder on the server should you store a network disk image so that it can be used by the NetInstall service?
A. /etc/NetBoot/NetBootCMents0/
B. /etc/NetBoot/NetBootSP0/
C. /Library/NetBoot/NetBootSP0/
D. /Library/NetBoot/NetBootClients0/
Answer: C
Explanation:
Reference:http://www.krypted.com/?tag=netinstall(search the answer)
NEW QUESTION: 4
What muscle is the prime mover for a seated lateral dumbbell raise to shoulder level with elbows slightly flexed?
A. Anterior deltoid
B. Middle deltoid
C. Posterior deltoid
D. Bicep brachii
Answer: B
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