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NEW QUESTION: 1
A. Option D
B. Option A
C. Option B
D. Option C
Answer: C
NEW QUESTION: 2
You manage a cloud service that hosts a customer-facing application. The application allows users to upload images and create collages.
The cloud service is running in two medium instances and utilizes Azure Queue storage for image processing.
The storage account is configured to be locally redundant.
The sales department plans to send a newsletter to potential clients. As a result, you expect a significant increase in global traffic.
You need to recommend a solution that meets the following requirements:
Configure the cloud service to ensure the application is responsive to the traffic increase.
Minimize hosting and administration costs.
What are two possible ways to achieve this goal? Each correct answer presents a complete solution.
A. Configure the cloud service to run in two Large instances.
B. Configure the cloud service to auto-scale to three instances when processor utilization is above 80%.
C. Configure the cloud service to auto-scale when the queue exceeds 1000 entries per machine.
D. Configure the storage account to be geo-redundant
E. Deploy a new cloud service in a separate data center.
Use Azure Traffic Manager to load balance traffic between the cloud services.
Answer: B,C
Explanation:
Explanation/Reference:
Explanation:
An autoscaling solution reduces the amount of manual work involved in dynamically scaling an application.
It can do this in two different ways: either preemptively by setting constraints on the number of role instances based on a timetable, or reactively by adjusting the number of role instances in response to some counter(s) or measurement(s) that you can collect from your application or from the Azure environment.
NEW QUESTION: 3
この質問については、TerramEarthのケーススタディを参照してください。
TerramEarthのCTOは、接続された車両の生データを使用して、開発チームの車両が障害に集中する時期を特定するのに役立てたいと考えています。アナリストが車両データを一元的に照会できるようにする必要があります。どのアーキテクチャをお勧めしますか？
A）
B）
C）
D）
A. オプションC
B. オプションD
C. オプションA
D. オプションB
Answer: C
Explanation:
Explanation
https://cloud.google.com/solutions/iot/
https://cloud.google.com/solutions/designing-connected-vehicle-platform
https://cloud.google.com/solutions/designing-connected-vehicle-platform#data_ingestion
http://www.eweek.com/big-data-and-analytics/google-touts-value-of-cloud-iot-core-for-analyzing-connected-car
https://cloud.google.com/solutions/iot/
The push endpoint can be a load balancer.
A container cluster can be used.
Cloud Pub/Sub for Stream Analytics
References: https://cloud.google.com/pubsub/
https://cloud.google.com/solutions/iot/
https://cloud.google.com/solutions/designing-connected-vehicle-platform
https://cloud.google.com/solutions/designing-connected-vehicle-platform#data_ingestion
http://www.eweek.com/big-data-and-analytics/google-touts-value-of-cloud-iot-core-for-analyzing-connected-car
https://cloud.google.com/solutions/iot/