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NEW QUESTION: 1
When optimizing applications that open many short-lived HTTP connections, which operating mode will give the best performance?
A. Full Transparency
B. Port Transparency
C. Full Transparency with reset
D. Correct Addressing
E. Any of the above, because performance is not affected by these
Answer: D

NEW QUESTION: 2
Case Study: 7 - Mountkirk Games
Company Overview
Mountkirk Games makes online, session-based, multiplayer games for mobile platforms. They build all of their games using some server-side integration. Historically, they have used cloud providers to lease physical servers.
Due to the unexpected popularity of some of their games, they have had problems scaling their global audience, application servers, MySQL databases, and analytics tools.
Their current model is to write game statistics to files and send them through an ETL tool that loads them into a centralized MySQL database for reporting.
Solution Concept
Mountkirk Games is building a new game, which they expect to be very popular. They plan to deploy the game's backend on Google Compute Engine so they can capture streaming metrics, run intensive analytics, and take advantage of its autoscaling server environment and integrate with a managed NoSQL database.
Business Requirements
Increase to a global footprint.

Improve uptime - downtime is loss of players.

Increase efficiency of the cloud resources we use.

Reduce latency to all customers.

Technical Requirements
Requirements for Game Backend Platform
Dynamically scale up or down based on game activity.

Connect to a transactional database service to manage user profiles and game state.

Store game activity in a timeseries database service for future analysis.

As the system scales, ensure that data is not lost due to processing backlogs.

Run hardened Linux distro.

Requirements for Game Analytics Platform
Dynamically scale up or down based on game activity

Process incoming data on the fly directly from the game servers

Process data that arrives late because of slow mobile networks

Allow queries to access at least 10 TB of historical data

Process files that are regularly uploaded by users' mobile devices

Executive Statement
Our last successful game did not scale well with our previous cloud provider, resulting in lower user adoption and affecting the game's reputation. Our investors want more key performance indicators (KPIs) to evaluate the speed and stability of the game, as well as other metrics that provide deeper insight into usage patterns so we can adapt the game to target users.
Additionally, our current technology stack cannot provide the scale we need, so we want to replace MySQL and move to an environment that provides autoscaling, low latency load balancing, and frees us up from managing physical servers.
For this question, refer to the Mountkirk Games case study. Mountkirk Games wants you to design a way to test the analytics platform's resilience to changes in mobile network latency.
What should you do?
A. Build a test client that can be run from a mobile phone emulator on a Compute Engine virtual machine, and run multiple copies in Google Cloud Platform regions all over the world to generate realistic traffic.
B. Deploy failure injection software to the game analytics platform that can inject additional latency to mobile client analytics traffic.
C. Add the ability to introduce a random amount of delay before beginning to process analytics files uploaded from mobile devices.
D. Create an opt-in beta of the game that runs on players' mobile devices and collects response times from analytics endpoints running in Google Cloud Platform regions all over the world.
Answer: D

NEW QUESTION: 3
When configuring the Transaction Reporter, what is the function of the Cache Remove Count?
A. It specifies that the Transaction Reporter cache is moved to History after the number of transactions in the cache exceeds the Cache Remove Count.
B. It specifies that the count of Transaction Reporter cache items is reported in the Transactions workspace.
C. It specifies that the Transaction Reporter polls the Aggregation agents for data at the default rate of every 120 seconds.
D. It specifies the number of Aggregation Periods of data that are moved to History when the cache period is exceeded.
Answer: C