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NEW QUESTION: 1
The Huawei Oceanstor 9000 NFS service features the correct statement: (multiple choices in this question)
A. Provide shared services in a clustered manner, and the nodes can monitor each other's service status.
B. After the node fails, the client service connected to this node is quickly switched to another node to take over, and the service is not interrupted.
C. Load balancing can be achieved according to service and node status, and data access is evenly distributed within the cluster.
D. Oceanstor 9000 supports NFSv3 and v4 protocols.
E. Compatible with multiple operating systems, enabling file resource sharing in heterogeneous network environments
Answer: A,B,C,D,E
NEW QUESTION: 2
iQuery is a proprietary protocol that distributes metrics gathered from which three sources?
(Choose three.)
A. monitors from Generic Host Servers
B. SNMP
C. DNS root servers
D. monitors from LTM Systems
E. path probes such as ICMP
Answer: B,D,E
NEW QUESTION: 3
How are monitored application activity details sent to the Core Server?
A. PSAPI.DLL
B. XFER.EXE
C. LDISCN32.EXE
D. SOFTMON.EXE
Answer: C
NEW QUESTION: 4
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 database workloads for your company, Mountkirk Games.
Considering the business and technical requirements, what should you do?
A. Use Cloud Bigtable for time series data, use Cloud Spanner for transactional data, and use BigQuery for historical data queries.
B. Use Cloud SQL to replace MySQL, and use Cloud Spanner for historical data queries.
C. Use Cloud Bigtable to replace MySQL, and use BigQuery for historical data queries.
D. Use Cloud SQL for time series data, and use Cloud Bigtable for historical data queries.
Answer: A
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