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NEW QUESTION: 1
DRAG DROP
A company has an Office 365 tenant. You plan to use Office 365 to manage the DNS settings for a custom domain. You purchase the domain through a third-party provider.
You create a custom website. You must host the website through a third-party provider at the IPv6 address 2001:4860:4801:1:5:4d. You need to configure the correct DNS settings.
What should you do? To answer, drag the appropriate DNS record to the correct DNS target.
Each record may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
Answer:
Explanation:
Change your domain's name server (NS) records
When you get to the last step of the domains setup wizard in Office 365, you have one task remaining. To set up your domain with Office 365 services, like email, you change your domain's name server (or NS) records at your domain registrar to point to the Office 365 primary and secondary name servers.
Use (A) DNS record for the web site.
NEW QUESTION: 2
Router R3 receives an LSA with the sequence number 111 from router R4 and then receives another copy of the LSA with a sequence number 112 from router R2. What does router R3 do with the LSA from R2?
A. R3 installs the LSA received from R2 in its link state database and floods a copy to its neighbors.
B. R3 installs the LSA received from R2 in its link state database, sends an ACK to R2, and floods a copy to its neighbors.
C. R3 discards the LSA received from R2 and sends a copy of the LSA from its link state base to R2.
D. R3 discards the LSA received from R2 and sends an ACK to R2.
Answer: D
NEW QUESTION: 3
Case Study: 2,
Flowlogistic Case Study
Company Overview
Flowlogistic is a leading logistics and supply chain provider. They help businesses throughout the world manage their resources and transport them to their final destination. The company has grown rapidly, expanding their offerings to include rail, truck, aircraft, and oceanic shipping.
Company Background
The company started as a regional trucking company, and then expanded into other logistics market.
Because they have not updated their infrastructure, managing and tracking orders and shipments has become a bottleneck. To improve operations, Flowlogistic developed proprietary technology for tracking shipments in real time at the parcel level. However, they are unable to deploy it because their technology stack, based on Apache Kafka, cannot support the processing volume. In addition, Flowlogistic wants to further analyze their orders and shipments to determine how best to deploy their resources.
Solution Concept
Flowlogistic wants to implement two concepts using the cloud:
Use their proprietary technology in a real-time inventory-tracking system that indicates the location of their loads Perform analytics on all their orders and shipment logs, which contain both structured and unstructured data, to determine how best to deploy resources, which markets to expand info. They also want to use predictive analytics to learn earlier when a shipment will be delayed.
Existing Technical Environment
Flowlogistic architecture resides in a single data center:
Databases
8 physical servers in 2 clusters
SQL Server - user data, inventory, static data
3 physical servers
Cassandra - metadata, tracking messages
10 Kafka servers - tracking message aggregation and batch insert
Application servers - customer front end, middleware for order/customs 60 virtual machines across 20 physical servers Tomcat - Java services Nginx - static content Batch servers Storage appliances iSCSI for virtual machine (VM) hosts Fibre Channel storage area network (FC SAN) ?SQL server storage Network-attached storage (NAS) image storage, logs, backups Apache Hadoop /Spark servers Core Data Lake Data analysis workloads
20 miscellaneous servers
Jenkins, monitoring, bastion hosts,
Business Requirements
Build a reliable and reproducible environment with scaled panty of production. Aggregate data in a centralized Data Lake for analysis Use historical data to perform predictive analytics on future shipments Accurately track every shipment worldwide using proprietary technology Improve business agility and speed of innovation through rapid provisioning of new resources Analyze and optimize architecture for performance in the cloud Migrate fully to the cloud if all other requirements are met Technical Requirements Handle both streaming and batch data Migrate existing Hadoop workloads Ensure architecture is scalable and elastic to meet the changing demands of the company.
Use managed services whenever possible
Encrypt data flight and at rest
Connect a VPN between the production data center and cloud environment SEO Statement We have grown so quickly that our inability to upgrade our infrastructure is really hampering further growth and efficiency. We are efficient at moving shipments around the world, but we are inefficient at moving data around.
We need to organize our information so we can more easily understand where our customers are and what they are shipping.
CTO Statement
IT has never been a priority for us, so as our data has grown, we have not invested enough in our technology. I have a good staff to manage IT, but they are so busy managing our infrastructure that I cannot get them to do the things that really matter, such as organizing our data, building the analytics, and figuring out how to implement the CFO' s tracking technology.
CFO Statement
Part of our competitive advantage is that we penalize ourselves for late shipments and deliveries. Knowing where out shipments are at all times has a direct correlation to our bottom line and profitability.
Additionally, I don't want to commit capital to building out a server environment.
Flowlogistic wants to use Google BigQuery as their primary analysis system, but they still have Apache Hadoop and Spark workloads that they cannot move to BigQuery. Flowlogistic does not know how to store the data that is common to both workloads. What should they do?
A. Store the common data in BigQuery and expose authorized views.
B. Store the common data encoded as Avro in Google Cloud Storage.
C. Store the common data in BigQuery as partitioned tables.
D. Store he common data in the HDFS storage for a Google Cloud Dataproc cluster.
Answer: A
NEW QUESTION: 4
귀사는 도쿄 및 지사에 본사를두고 있으며 일본, 유럽 및 미국의 AWS에 다중 지역 배포를 지원하는 물류 소프트웨어를 사용하고 있습니다.
물류 소프트웨어는 3 계층 아키텍처로되어 있으며 현재 데이터 지속성을 위해 MySQL 5.6을 사용합니다. 각 지역은 자체 데이터베이스를 구축했습니다 HQ 지역에서는 모든 지역의 데이터를 읽는 시간별 일괄 처리 프로세스를 실행하여 이메일을 통해 모든 사무소로 전송되는 교차 지역 보고서를 계산합니다.이 배치 프로세스는 물류를 신속하게 최적화하기 위해 최대한 빨리 완료해야합니다 요구 사항을 충족시키기 위해 데이터베이스 아키텍처를 어떻게 구축합니까?
A. 각 지역 배포의 경우 해당 지역의 마스터와 함께 RDS MySQL을 사용하고 HQ 지역의 읽기 복제본을 사용합니다
B. 지역별 배포의 경우 EC2의 MySQL을 해당 지역의 마스터와 함께 사용하고 시간별 EBS 스냅 샷을 HQ 지역에 보냅니다
C. 각 지역 배포의 경우 EC2의 MySQL을 해당 지역의 마스터와 함께 사용하고 S3을 사용하여 시간별로 HQ 지역에 데이터 파일을 복사합니다.
D. 각 지역별 배포의 경우 해당 지역의 마스터와 함께 RDS MySQL을 사용하고 시간별 RDS 스냅 샷을 HQ 지역으로 보냅니다
E. Direct Connect를 사용하여 모든 지역 MySQL 배포를 HQ 영역에 연결하고 일괄 처리의 네트워크 대기 시간을 줄입니다.
Answer: A