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NEW QUESTION: 1
DRAG DROP
You deploy a server that has SQL Server installed.
You deploy a SQL Server Integration Services (SSIS) package to the server.
You need to automate the execution of the package. The solution must ensure that you receive a notification if the package to execute.
In which order should you perform all the actions?

Answer:
Explanation:


NEW QUESTION: 2
断片化を防ぐために設定されている2つのGRE機能はどれですか。 (2つ選択してください。)
A. DFビットをクリア
B. PMTUD
C. MTU無視
D. TCPウィンドウサイズ
E. TCP MSS
F. IP MTU
Answer: B,E
Explanation:
Explanation
The IP protocol was designed for use on a wide variety of transmission links. Although the maximum length of an IP datagram is 65535, most transmission links enforce a smaller maximum packet length limit, called an MTU. The value of the MTU depends on the type of the transmission link. The design of IP accommodates MTU differences since it allows routers to fragment IP datagrams as necessary. The receiving station is responsible for the reassembly of the fragments back into the original full size IP datagram.
Fragmentation and Path Maximum Transmission Unit Discovery (PMTUD) is a standardized technique to determine the maximum transmission unit (MTU) size on the network path between two hosts, usually with the goal of avoiding IP fragmentation. PMTUD was originally intended for routers in IPv4. However, all modern operating systems use it on endpoints.
The TCP Maximum Segment Size (TCP MSS) defines the maximum amount of data that a host is willing to accept in a single TCP/IP datagram. This TCP/IP datagram might be fragmented at the IP layer. The MSS value is sent as a TCP header option only in TCP SYN segments. Each side of a TCP connection reports its MSS value to the other side. Contrary to popular belief, the MSS value is not negotiated between hosts. The sending host is required to limit the size of data in a single TCP segment to a value less than or equal to the MSS reported by the receiving host.
TCP MSS takes care of fragmentation at the two endpoints of a TCP connection, but it does not handle the case where there is a smaller MTU link in the middle between these two endpoints.
PMTUD was developed in order to avoid fragmentation in the path between the endpoints. It is used to dynamically determine the lowest MTU along the path from a packet's source to its destination.
Reference:
25885-pmtud-ipfrag.html

NEW QUESTION: 3
欧州連合のマネーロンダリング指令によると、マネーロンダリングの要素として必要な「知識、意図、または目的」は、
A. 主観的な事実の状況。
B. 主観的な非事実的状況。
C. 客観的で非事実的な状況。
D. 客観的な事実の状況。
Answer: D

NEW QUESTION: 4
Case Study: 5 - Dress4win
Company Overview
Dress4win is a web-based company that helps their users organize and manage their personal wardrobe using a website and mobile application. The company also cultivates an active social network that connects their users with designers and retailers. They monetize their services through advertising, e-commerce, referrals, and a freemium app model. The application has grown from a few servers in the founder's garage to several hundred servers and appliances in a collocated data center. However, the capacity of their infrastructure is now insufficient for the application's rapid growth. Because of this growth and the company's desire to innovate faster.
Dress4Win is committing to a full migration to a public cloud.
Solution Concept
For the first phase of their migration to the cloud, Dress4win is moving their development and test environments. They are also building a disaster recovery site, because their current infrastructure is at a single location. They are not sure which components of their architecture they can migrate as is and which components they need to change before migrating them.
Existing Technical Environment
The Dress4win application is served out of a single data center location. All servers run Ubuntu LTS v16.04.
Databases:
MySQL. 1 server for user data, inventory, static data:

- MySQL 5.8
- 8 core CPUs
- 128 GB of RAM
- 2x 5 TB HDD (RAID 1)
Redis 3 server cluster for metadata, social graph, caching. Each server is:

- Redis 3.2
- 4 core CPUs
- 32GB of RAM
Compute:
40 Web Application servers providing micro-services based APIs and static content.

- Tomcat - Java
- Nginx
- 4 core CPUs
- 32 GB of RAM
20 Apache Hadoop/Spark servers:

- Data analysis
- Real-time trending calculations
- 8 core CPUS
- 128 GB of RAM
- 4x 5 TB HDD (RAID 1)
3 RabbitMQ servers for messaging, social notifications, and events:

- 8 core CPUs
- 32GB of RAM
Miscellaneous servers:

- Jenkins, monitoring, bastion hosts, security scanners
- 8 core CPUs
- 32GB of RAM
Storage appliances:
iSCSI for VM hosts

Fiber channel SAN - MySQL databases

- 1 PB total storage; 400 TB available
NAS - image storage, logs, backups

- 100 TB total storage; 35 TB available
Business Requirements
Build a reliable and reproducible environment with scaled parity of production.

Improve security by defining and adhering to a set of security and Identity and Access

Management (IAM) best practices for cloud.
Improve business agility and speed of innovation through rapid provisioning of new resources.

Analyze and optimize architecture for performance in the cloud.

Technical Requirements
Easily create non-production environment in the cloud.

Implement an automation framework for provisioning resources in cloud.

Implement a continuous deployment process for deploying applications to the on-premises

datacenter or cloud.
Support failover of the production environment to cloud during an emergency.

Encrypt data on the wire and at rest.

Support multiple private connections between the production data center and cloud

environment.
Executive Statement
Our investors are concerned about our ability to scale and contain costs with our current infrastructure. They are also concerned that a competitor could use a public cloud platform to offset their up-front investment and free them to focus on developing better features. Our traffic patterns are highest in the mornings and weekend evenings; during other times, 80% of our capacity is sitting idle.
Our capital expenditure is now exceeding our quarterly projections. Migrating to the cloud will likely cause an initial increase in spending, but we expect to fully transition before our next hardware refresh cycle. Our total cost of ownership (TCO) analysis over the next 5 years for a public cloud strategy achieves a cost reduction between 30% and 50% over our current model.
For this question, refer to the Dress4Win case study. Considering the given business requirements, how would you automate the deployment of web and transactional data layers?
A. Deploy Nginx and Tomcat using Cloud Launcher. Deploy a MySQL server using Cloud Launcher.
Deploy Jenkins to Compute Engine using Cloud Deployment Manager scripts.
B. Migrate Nginx and Tomcat to App Engine. Deploy a MySQL server using Cloud Launcher. Deploy Jenkins to Compute Engine using Cloud Launcher.
C. Migrate Nginx and Tomcat to App Engine. Deploy a Cloud Datastore server to replace the MySQL server in a high-availability configuration. Deploy Jenkins to Compute Engine using Cloud Launcher.
D. Deploy Nginx and Tomcat using Cloud Deployment Manager to Compute Engine. Deploy a Cloud SQL server to replace MySQL. Deploy Jenkins using Cloud Deployment Manager.
Answer: C

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