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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.
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: data_point
Column data: device_id,date
B. Rowkey: device_id
Column data: date, data_point
C. Rowkey: date
Column data: device_id,data_point
D. Rowkey: date#device_id
Column data: data_point
E. Rowkey: date#data_point
Column data: device_id
Answer: D
Explanation:
The most common query is for all the data for a given device for a given day", rowkey should have info for both device and date.

NEW QUESTION: 2
Dynpros can be placed in which program types? Select all that apply.
A. Class pools
B. Function groups
C. Executables
D. Module pools
Answer: B,C,D

NEW QUESTION: 3
ルーターが100 kbpsを受け入れるSSHの量を制限する構成はどれですか。
A）

B）

C）

D）

A. オプションC
B. オプションD
C. オプションB
D. オプションA
Answer: A
Explanation:
Explanation
CoPP protects the route processor on network devices by treating route processor resources as a separate entity with its own ingress interface (and in some implementations, egress also). CoPP is used to police traffic that is destined to the route processor of the router such as:
+ routing protocols like OSPF, EIGRP, or BGP.
+ Gateway redundancy protocols like HSRP, VRRP, or GLBP.
+ Network management protocols like telnet, SSH, SNMP, or RADIUS.

Therefore we must apply the CoPP to deal with SSH because it is in the
management plane. CoPP must be put under "control-plane" command.

NEW QUESTION: 4
contoso.comという名前の電子メールドメインを使用するMicrosoftExchangeOnlineテナントがあります。
あなたの会社はfabrikam.comという名前の別の会社とパートナーシップを結んでいます。
fabrikam.comのユーザーとの間で送受信されるすべての電子メールメッセージがTLSを使用して暗号化されていることを確認する必要があります。
Exchange管理センターで何を作成する必要がありますか？
A. 2つのコネクタ
B. 1つのリモートドメインと1つの承認済みドメイン
C. 1つのコネクタと1つの承認済みドメイン
D. 1つのコネクタと1つのリモートドメイン
Answer: A
Explanation:
Reference:
https://docs.microsoft.com/en-us/exchange/mail-flow-best-practices/use-connectors-to-configure-mail-flow/setup-connectors-for-secure-mail-flow-with-a-partner