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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 needs you to create a schema in Google Bigtable that will allow for the historical analysis of the last 2 years of records. Each record that comes in is sent every 15 minutes, and contains a unique identifier of the device and a data record. The most common query is for all the data for a given device for a given day. Which schema should you use?
A. Rowkey: date
Column data: device_id,data_point
B. Rowkey: data_point
Column data: device_id,date
C. Rowkey: date#data_point
Column data: device_id
D. Rowkey: device_id
Column data: date, data_point
E. Rowkey: date#device_id
Column data: data_point
Answer: B
NEW QUESTION: 2
You manage a Microsoft Dynamics CRM 2016 organization integrated with Microsoft
Exchange with server-side sync as the synchronization method.
You need to send a bulk email to 1,000 contacts. The email will contain a .zip file as an attachment.
Which service is responsible for processing the bulk email?
A. Asynchronous Processing Service
B. Sandbox Processing Service
C. Email Router Service
D. Unzip Service
Answer: A
NEW QUESTION: 3
組織は、CiscoFTDおよびCiscoISEを使用して、IDベースのアクセス制御を実行しています。ネットワーク管理者がCiscoFTDイベントを分析していて、不明なユーザトラフィックがファイアウォールの通過を許可されていることに気づきました。正当なユーザートラフィックを許可しながらトラフィックをブロックするには、これにどのように対処する必要がありますか？
A. CiscoFTDのマルウェアとファイルポリシーに不明なユーザーを追加します。
B. CiscoFTDのアクセス制御ポリシーに不明なユーザを追加します。
C. Cisco ISE許可ポリシーを変更して、ユーザへのこのアクセスを拒否します。
D. 正当なユーザ名のみをCiscoFTDに送信するようにCiscoISEを変更します。
Answer: B
NEW QUESTION: 4
AzurePipelinesでビルドパイプラインを作成しています。
サードパーティのアプリケーションが原因で失敗する可能性のあるいくつかのテストを定義します。
サードパーティのアプリケーションが正常に完了するようにするには、ビルドパイプラインが正常に完了するようにする必要があります。
利用できません。
あなたは何をするべきか？
A. 並列ジョブを使用するようにビルドパイプラインを構成します
B. テスト合格率を上げる
C. 不安定なテストを構成する
D. ダッシュボードに要件品質ウィジェットを追加します
Answer: D
Explanation:
Requirements traceability is the ability to relate and document two or more phases of a development process, which can then be traced both forward or backward from its origin. Requirements traceability help teams to get insights into indicators such as quality of requirements or readiness to ship the requirement. A fundamental aspect of requirements traceability is association of the requirements to test cases, bugs and code changes.
Reference:
https://docs.microsoft.com/en-us/azure/devops/pipelines/test/requirements-traceability