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NEW QUESTION: 1
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. You need to analyze and define the technical architecture for the compute workloads for your company, Mountkirk Games.
Considering the Mountkirk Games business and technical requirements, what should you do?
A. Create network load balancers. Use non-preemptible Compute Engine instances.
B. Create network load balancers. Use preemptible Compute Engine instances.
C. Create a global load balancer with managed instance groups and autoscaling policies. Use preemptible Compute Engine instances.
D. Create a global load balancer with managed instance groups and autoscaling policies. Use non- preemptible Compute Engine instances.
Answer: D

NEW QUESTION: 2
The implementations group has been using the test bed to do a 'proof-of-concept' that requires both Client 1 and Client 2 to access the WEB Server at 209.65.200.241. After several changes to the network addressing, routing scheme, DHCP services,
NTP services, layer 2 connectivity, FHRP services, and device security, a trouble ticket has been opened indicating that Client 1 cannot ping the 209.65.200.241 address.
Use the supported commands to isolated the cause of this fault and answer the following questions.
On which device is the fault condition located?
A. ASW2
B. R1
C. ASW1
D. DSW1
E. R2
F. R3
G. R4
H. DSW2
Answer: G
Explanation:
On R4, in the redistribution of EIGRP routing protocol, we need to change name of route-map to resolve the issue. It references route-map OSPF_to_EIGRP but the actual route map is called OSPF->EIGRP.

NEW QUESTION: 3
A web application running on Amazon EC2 instances writes data synchronously to an Amazon DynamoDB table configured for 60 write capacity units. During normal operation the application writes 50 KB/s to the tale, but can scale up to 500 KB/ s during peak hours.
The application is currently throttling errors from the DynamoDB table during peak hours.
What is the MOST cost-efficient change to support the increased traffic with minimal changes to the application?
A. Change DynamoDB table configuration to 600 write capacity units.
B. Use Amazon SQS to manage the write operations to the DynamoDB table.
C. Increase the number of Amazon EC2 instances to support the traffic.
D. Configure Amazon DynamoDB Auto Scaling to handle the extra demand.
Answer: D