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NEW QUESTION: 1
Your company produces customer commissioned one-of-a-kind skiing helmets, combining high fashion with custom technical enhancements. Customers can show off their individuality on the ski slopes and have access to head-up-displays, GPS, rear-view cams and any other technical Innovation they wish to embed in the helmet.
The current manufacturing process is data rich and complex, including assessments to ensure that the custom electronics and materials used to assemble the helmets are to the highest standards. Assessments are a mixture of human and automated assessments.
You need to add a new set of assessment to model the failure modes of the custom electronics using GPUs with CUDA, across a cluster of servers with low latency networking.
What architecture would allow you to automate the existing process using a hybrid approach, and ensure that the architecture can support the evolution of processes over time.
A. Use AWS Data Pipeline to manage movement of data & meta-data and assessments.
Use auto-scaling group of C3 with SR-IOV (Single Root I/O Visualization).
B. Use Amazon Simple Workflow (SWF) to manage assessments, movement of data & meta-data.
Use an auto-scaling group of C3 instances with SR-IOV (Single Root I/O Visualization).
C. Use Amazon Simple Workflow (SWF) to manage assessments, movement of data & meta-data.
Use an auto-scaling group of G2 instances in a placement group.
D. Use AWS Data Pipeline to manage movement of data & meta-data and assessments.
Use an auto- scaling group of G2 instances in a placement group.
Answer: C
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. 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.
B. Add the ability to introduce a random amount of delay before beginning to process analytics files uploaded from mobile devices.
C. 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.
D. Deploy failure injection software to the game analytics platform that can inject additional latency to mobile client analytics traffic.
Answer: A
NEW QUESTION: 3
Examine the parameters for your database instance:
Which three statements are true about the process of automatic optimization by using cardinality feedback? (Choose three.)
A. After the optimizer identifies a query as a re-optimization candidate, statistics collected by the collectors are submitted to the optimizer.
B. The optimizer can re optimize a query only once using cardinality feedback.
C. The optimizer automatically changes a plan during subsequent execution of a SQL statement if there is a huge difference in optimizer estimates and execution statistics.
D. The optimizer enables monitoring for cardinality feedback after the first execution of a query.
E. The optimizer does not monitor cardinality feedback if dynamic sampling and multicolumn statistics are enabled.
Answer: C,D,E
Explanation:
C: During the first execution of a SQL statement, an execution plan is generated as usual.
D: if multi-column statistics are not present for the relevant combination of columns, the optimizer can fall back on cardinality feedback.
(not B)* Cardinality feedback. This feature, enabled by default in 11.2, is intended to improve plans for repeated executions.
optimizer_dynamic_sampling
optimizer_features_enable
* dynamic sampling or multi-column statistics allow the optimizer to more accurately estimate selectivity of conjunctive predicates.
Note:
* OPTIMIZER_DYNAMIC_SAMPLING controls the level of dynamic sampling performed by the optimizer.
Range of values. 0 to 10
* Cardinality feedback was introduced in Oracle Database 11gR2. The purpose of this feature is to automatically improve plans for queries that are executed repeatedly, for which the optimizer does not estimate cardinalities in the plan properly. The optimizer may misestimate cardinalities for a variety of reasons, such as missing or inaccurate statistics, or complex predicates. Whatever the reason for the misestimate, cardinality feedback may be able to help.