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NEW QUESTION: 1
All the following are characteristics of a buy-sell agreement EXCEPT
A. It provides for easier probating of the business
B. It provides liquidity for estate settlement needs
C. It provides for the continuation of the business
D. It provides a market for the business
Answer: A

NEW QUESTION: 2
Dynamics 365 for Salesシステムカスタマイザーです。
リードフォームでLinkedIn Sales Navigatorリード（メンバープロファイル）を設定する必要があります。
解決策：Dynamics 365 AI for Salesを使用します。
ソリューションは目標を達成していますか？
A. はい
B. いいえ
Answer: B
Explanation:
説明
参照：
https://docs.microsoft.com/en-us/dynamics365/customer-engagement/linkedin/add-sales-navigator-controlsforms

NEW QUESTION: 3
Planning Assistanceデータベースのシャーディング戦略を設計する必要があります。
何をお勧めしますか？
A. 速度列のバイナリ表現の範囲マッピングシャードマップ
B. ロケーション列のシャードマップをマッピングするリスト
C. 時間列の範囲マッピングシャードマップ
D. ナンバープレート列のバイナリ表現上のシャードマップをマッピングするリスト
Answer: D
Explanation:
Explanation
Data used for Planning Assistance must be stored in a sharded Azure SQL Database.
A shard typically contains items that fall within a specified range determined by one or more attributes of the data. These attributes form the shard key (sometimes referred to as the partition key). The shard key should be static. It shouldn't be based on data that might change.
References:
https://docs.microsoft.com/en-us/azure/architecture/patterns/sharding
Topic 1, Case study 1The company identifies the following business
requirements:
* External vendors must be able to perform custom analysis of data using machine learning technologies.
* You must display a dashboard on the operations status page that displays the following metrics: telemetry, volume, and processing latency.
* Traffic data must be made available to the Government Planning Department for the purpose of modeling changes to the highway system. The traffic data will be used in conjunction with other data such as information about events such as sporting events, weather conditions, and population statistics. External data used during the modeling is stored in on-premises SQL Server 2016 databases and CSV files stored in an Azure Data Lake Storage Gen2 storage account.
* Information about vehicles that have been detected as going over the speed limit during the last 30 minutes must be available to law enforcement officers. Several law enforcement organizations may respond to speeding vehicles.
* The solution must allow for searches of vehicle images by license plate to support law enforcement investigations. Searches must be able to be performed using a query language and must support fuzzy searches to compensate for license plate detection errors.
Telemetry Capture
The telemetry capture system records each time a vehicle passes in front of a sensor. The sensors run on a custom embedded operating system and record the following telemetry data:
* Time
* Location in latitude and longitude
* Speed in kilometers per hour (kmph)
* Length of vehicle in meters
Visual Monitoring
The visual monitoring system is a network of approximately 1,000 cameras placed near highways that capture images of vehicle traffic every 2 seconds. The cameras record high resolution images. Each image is approximately 3 MB in size.
This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided.
To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other question on this case study.
At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next sections of the exam. After you begin a new section, you cannot return to this section.
To start the case study
To display the first question on this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the button to return to the question.
Overview
You develop data engineering solutions for Graphics Design Institute, a global media company with offices in New York City, Manchester, Singapore, and Melbourne.
The New York office hosts SQL Server databases that stores massive amounts of customer data. The company also stores millions of images on a physical server located in the New York office. More than 2 TB of image data is added each day. The images are transferred from customer devices to the server in New York.
Many images have been placed on this server in an unorganized manner, making it difficult for editors to search images. Images should automatically have object and color tags generated. The tags must be stored in a document database, and be queried by SQL You are hired to design a solution that can store, transform, and visualize customer data.
Requirements
Business
The company identifies the following business requirements:
* You must transfer all images and customer data to cloud storage and remove on-premises servers.
* You must develop an analytical processing solution for transforming customer data.
* You must develop an image object and color tagging solution.
* Capital expenditures must be minimized.
* Cloud resource costs must be minimized.
Technical
The solution has the following technical requirements:
* Tagging data must be uploaded to the cloud from the New York office location.
* Tagging data must be replicated to regions that are geographically close to company office locations.
* Image data must be stored in a single data store at minimum cost.
* Customer data must be analyzed using managed Spark clusters.
* Power BI must be used to visualize transformed customer data.
* All data must be backed up in case disaster recovery is required.
Security and optimization
All cloud data must be encrypted at rest and in transit. The solution must support:
* parallel processing of customer data
* hyper-scale storage of images
* global region data replication of processed image data

NEW QUESTION: 4
Your network contains an Active Directory domain named contoso.com. The domain contains two member servers named Server1 and Server2. All servers run Windows Server 2012 R2.
Server1 and Server2 have the Network Load Balancing (NLB) feature installed. The servers are configured as nodes in an NLB cluster named Cluster1. Cluster1 hosts a secure web Application named WebApp1.
WebApp1 saves user state information locally on each node.
You need to ensure that when users connect to WebApp1, their session state is maintained.
What should you configure?
A. The Handling priority
B. The failover settings
C. A file server for general use
D. The possible owner
E. The cluster quorum settings
F. Affinity Single
G. Live migration
H. The Scale-Out File Server
I. Quick migration
J. The preferred owner
K. The host priority
L. Affinity None
Answer: F
Explanation:
Explanation/Reference:
Explanation:
Even though Network Load Balancing (NLB) hasn't changed significantly since Windows Server 2008 and isn't mentioned in this chapter, be sure to review the feature and its configurable options. For example, remember that in port rules for Network Load Balancing clusters, the Affinity setting determines how you want multiple connections from the same client handled by the NLB cluster. "Affinity: Single" redirects clients back to the same cluster host. "Affinity: Network" redirects clients from the local subnet to the cluster host. "Affinity: None" doesn't redirect multiple connections from the same client back to the same cluster host.
http://technet.microsoft.com/en-us/library/bb687542.aspx
Using NLB
Client Affinity
NLB offers three types of client affinity to minimize response time to clients and provide generic support for preserving session state. Each affinity specifies a different method for distributing client requests. In Application Center, the New Cluster Wizard sets affinity to Single by default. Later, you can use the cluster Properties dialog box to modify the affinity. The following table describes the three types of affinity.

No Affinity
With No affinity, NLB does not associate clients with a particular member. Every client request can be load balanced to any member. This affinity provides the best performance but might disrupt clients with established sessions, because subsequent requests might be load balanced to other members where the session information does not exist. Single Affinity In Single affinity, NLB associates clients with particular members by using the client's IP address. Thus, requests coming from the same client IP address always reach the same member. This affinity provides the best support for clients that use sessions on an intranet.
These clients cannot use No affinity because their sessions could be disrupted. Additionally, these clients cannot use Class C affinity because intranet clients typically have IP addresses within a narrow range. It is likely that this range is so narrow that all clients on an intranet have the same Class C address, which means that one member might process all of the requests while other members remain idle. Class C Affinity With Class C affinity, NLB associates clients with particular members by using the Class C portion of the client's IP address. Thus, clients coming from the same Class C address range always access the same member. This affinity provides the best performance for clusters serving the Internet. Bb687542.note (en- us,TechNet.10).gif Note It is not efficient for Internet clients to use Single affinity because, in Single affinity, NLB load balances each client by the client's entire IP address, which can span a broad range. By using Class C affinity, NLB associates clients with only the same Class C portion of the IP address with particular members. Therefore, you essentially reduce the range of IP addresses by which NLB load balances clients.

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