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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's Google Cloud Dataflow pipeline is now ready to start receiving data from the 50,000 installations. You want to allow Cloud Dataflow to scale its compute power up as required. Which Cloud Dataflow pipeline configuration setting should you update?
A. The maximum number of workers
B. The number of workers
C. The zone
D. The disk size per worker
Answer: C

NEW QUESTION: 2
シナリオ：Citrix Architectは、パッシブデータセンターがすべてのユーザーの災害復旧データセンターとして機能する、顧客向けのアクティブ/パッシブXenAppおよびXenDesktop環境を設計しました。
以下が構成されています。
* NetScaler Global Server Load Balancing（GSLB）。アクティブなデータセンターへの単一のURL方向を提供します。
* GSLBには、負荷分散サーバー用に構成されたヘルスモニターがあります。
* NetScaler Gatewayは両方のデータセンターにあります。
* NetScalerのロードバランシングは、StoreFrontおよびDelivery ControllerのXMLサービスで発生します。
*単一のXenAppおよびXenDesktopサイトが存在します。
* 4つのDelivery Controllerがサイトのメンバーです。
* 2つのDelivery Controllerは、アクティブなデータセンターのプライマリゾーンのメンバーです。
* TwoDelivery Controllerは、パッシブデータセンターのサテライトゾーンのメンバーです。
* SQLはアクティブなデータセンターに展開されます。
*ローカルホストキャッシュが有効になっています。
* 2つのStoreFrontサーバーが各データセンターに展開され、それぞれのデータセンターのDelivery Controllerからリソースを集約します。
* Virtual Delivery Agent（VDA）マシンは両方のデータセンターに展開され、ゾーン内のDelivery Controllerに登録するように構成されています。
プライマリデータセンターのSQL Serverがオフになったらどうなりますか？
A. GSLBは、SQL Serverがダウンしており、セッションを起動できないことを識別しません。
B. プライマリデータセンターのVDAマシンは、パッシブデータセンターのDelivery Controllerに登録します。
C. プライマリデータセンターのStoreFrontは、セッションを起動できます。
D. GSLBは、セカンダリデータセンターへの接続のリダイレクトを開始します。
Answer: C

NEW QUESTION: 3
What storage type would be used for a collection of videos to be delivered via web browser to Clients?
A. File storage
B. Block storage
C. Object storage
D. SAN storage
Answer: B