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NEW QUESTION: 1
Case Study 2 - MJTelco
Company Overview
MJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the world.
The company has patents for innovative optical communications hardware. Based on these patents, they can create many reliable, high-speed backbone links with inexpensive hardware.
Company Background
Founded by experienced telecom executives, MJTelco uses technologies originally developed to overcome communications challenges in space. Fundamental to their operation, they need to create a distributed data infrastructure that drives real-time analysis and incorporates machine learning to continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the network allowing them to account for the impact of dynamic regional politics on location availability and cost.
Their management and operations teams are situated all around the globe creating many-to-many relationship between data consumers and provides in their system. After careful consideration, they decided public cloud is the perfect environment to support their needs.
Solution Concept
MJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs:
* Scale and harden their PoC to support significantly more data flows generated when they ramp to more than 50,000 installations.
* Refine their machine-learning cycles to verify and improve the dynamic models they use to control topology definition.
MJTelco will also use three separate operating environments - development/test, staging, and production - to meet the needs of running experiments, deploying new features, and serving production customers.
Business Requirements
* Scale up their production environment with minimal cost, instantiating resources when and where needed in an unpredictable, distributed telecom user community.
* Ensure security of their proprietary data to protect their leading-edge machine learning and analysis.
* Provide reliable and timely access to data for analysis from distributed research workers
* Maintain isolated environments that support rapid iteration of their machine-learning models without affecting their customers.
Technical Requirements
* Ensure secure and efficient transport and storage of telemetry data
* Rapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows each.
* Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately
100m records/day
* Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems both in telemetry flows and in production learning cycles.
CEO Statement
Our business model relies on our patents, analytics and dynamic machine learning. Our inexpensive hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize our large distributed data pipelines to meet our reliability and capacity commitments.
CTO Statement
Our public cloud services must operate as advertised. We need resources that scale and keep our data secure. We also need environments in which our data scientists can carefully study and quickly adapt our models. Because we rely on automation to process our data, we also need our development and test environments to work as we iterate.
CFO Statement
The project is too large for us to maintain the hardware and software required for the data and analysis.
Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on automation and infrastructure. Google Cloud's machine learning will allow our quantitative researchers to work on our high-value problems instead of problems with our data pipelines.
You need to compose visualization for operations teams with the following requirements:
* Telemetry must include data from all 50,000 installations for the most recent 6 weeks (sampling once every minute)
* The report must not be more than 3 hours delayed from live data.
* The actionable report should only show suboptimal links.
* Most suboptimal links should be sorted to the top.
* Suboptimal links can be grouped and filtered by regional geography.
* User response time to load the report must be <5 seconds.
You create a data source to store the last 6 weeks of data, and create visualizations that allow viewers to see multiple date ranges, distinct geographic regions, and unique installation types. You always show the latest data without any changes to your visualizations. You want to avoid creating and updating new visualizations each month. What should you do?
A. Look through the current data and compose a small set of generalized charts and tables bound to criteria filters that allow value selection.
B. Look through the current data and compose a series of charts and tables, one for each possible combination of criteria.
C. Load the data into relational database tables, write a Google App Engine application that queries all rows, summarizes the data across each criteria, and then renders results using the Google Charts and visualization API.
D. Export the data to a spreadsheet, compose a series of charts and tables, one for each possible combination of criteria, and spread them across multiple tabs.
Answer: A
NEW QUESTION: 2
A. Option A
B. Option D
C. Option C
D. Option B
Answer: D
Explanation:
https://docs.aws.amazon.com/redshift/latest/mgmt/working-with-snapshots.html#working-with- snapshot-restore-table-from-snapshot
NEW QUESTION: 3
You are using Elastic Beanstalk to manage your application. You have a SQL script that needs to only be executed once per deployment no matter how many EC2 instances you have running. How can you do this?
A. Use a "leader command" within an Elastic Beanstalk configuration file to execute the script, ensuring that the "container only" flag is set to true.
B. Use a "Container command" within an Elastic Beanstalk configuration file to execute the script, ensuring that the "leader only" flag is set to false.
C. Use a "Container command" within an Elastic Beanstalk configuration file to execute the script, ensuring that the "leader only" flag is set to true.
D. Use Elastic Beanstalk version and a configuration file to execute the script, ensuring that the "leader only" flag is set to true.
Answer: C
Explanation:
Explanation
You can use the container_commands key to execute commands that affect your application source code.
Container commands run after the application and web server have been set up and the application version archive has been extracted, but before the application version is deployed. Non-container commands and other customization operations are performed prior to the application source code being extracted.
You can use leader_only to only run the command on a single instance, or configure a test to only run the command when a test command evaluates to true. Leader-only container commands are only executed during environment creation and deployments, while other commands and server customization operations are performed every time an instance is provisioned or updated. Leader-only container commands are not executed due to launch configuration changes, such as a change in the AMI Id or instance type. For more information on customizing containers, please visit the below URL:
* http://docs.aws.amazon.com/elasticbeanstalk/latest/dg/customize-containers-ec2.
html
NEW QUESTION: 4
In order to effectively manage risks, the supply management professional must conduct risk planning which includes confirming the current state of the organization as well as conducting impact assessments.
If the supply management professional was exploring immediate problems and their effects on second and third tier events which step of risk planning are they performing?
A. The Identification phase of risk planning
B. Valuation phase of risk planning
C. The probability estimation phase of risk planning
D. The scope phase of risk planning
Answer: D
Explanation:
In order to effectively manage risks, the supply management professional must conduct risk planning which includes confirming the current state of the organization as well as conducting impact assessments. If the supply management professional was exploring immediate problems and their effects on second and third tier events they are performing the scope phase of risk planning. The probability estimation phase of risk planning determines the occurrence for identified risks based on available information. The Identification phase of risk planning involves developing a comprehensive list of risks.
The valuation phase of risk planning involves the direct and indirect costs associated with identified risks.
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