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NEW QUESTION: 1
MJTelco Case Study
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.
MJTelco is building a custom interface to share data. They have these requirements:
1. They need to do aggregations over their petabyte-scale datasets.
2. They need to scan specific time range rows with a very fast response time (milliseconds).
Which combination of Google Cloud Platform products should you recommend?
A. BigQuery and Cloud Storage
B. Cloud Datastore and Cloud Bigtable
C. BigQuery and Cloud Bigtable
D. Cloud Bigtable and Cloud SQL
Answer: C

NEW QUESTION: 2
Which event will happen if an administrator uses an Application Override Policy?
A. App-ID processing time is increased.
B. Threat-ID processing time is decreased.
C. The application name assigned to the traffic by the security rule is written to the Traffic log.
D. The Palo Alto Networks NGFW stops App-ID processing at Layer 4.
Answer: D
Explanation:
Reference: https://live.paloaltonetworks.com/t5/Learning-Articles/Tips-amp-Tricks-How-to-Create- an-Application-Override/ta-p/65513

NEW QUESTION: 3
A Scrum Team is estimating the prioritized User Stories. Each team member has been assigned a deck of numbered cards, in which each card is numbered in a sequence. The team members use these cards to assess a particular User Story by picking and showing a card from the deck that represents their estimate for the User Story. Which estimation tool is the Scrum Team using?
A. Wideband Delphi.
B. Planning Poker.
C. Affinity Estimation.
D. Fist of Five.
Answer: B
Explanation:
Planning Poker is an agile estimating and planning technique that is consensus based. To start a poker planning session, the product owner or customer reads an agile user story or describes a feature to the estimators.
Each estimator is holding a deck of Planning Poker cards with values like 0, 1, 2, 3, 5, 8, 13, 20, 40 and 100, which is the sequence we recommend. The values represent the number of story points, ideal days, or other units in which the team estimates.
Reference:
https://www.mountaingoatsoftware.com/agile/planning-poker