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NEW QUESTION: 1
Your product is currently deployed in three Google Cloud Platform (GCP) zones with your users divided between the zones. You can fail over from one zone to another, but it causes a 10-minute service disruption for the affected users. You typically experience a database failure once per quarter and can detect it within five minutes. You are cataloging the reliability risks of a new real-time chat feature for your product. You catalog the following information for each risk:
* Mean Time to Detect (MUD} in minutes
* Mean Time to Repair (MTTR) in minutes
* Mean Time Between Failure (MTBF) in days
* User Impact Percentage
The chat feature requires a new database system that takes twice as long to successfully fail over between zones. You want to account for the risk of the new database failing in one zone. What would be the values for the risk of database failover with the new system?
A. MTTD:5
MTTR: 20
MTBF: 90
Impact: 50%
B. MTTD: 5
MTTR: 10
MTBF: 90
Impact: 33%
C. MTTD:5
MTTR: 10
MTBF: 90
Impact 50%
D. MTTD:5
MTTR: 20
MTBF: 90
Impact: 33%
Answer: C

NEW QUESTION: 2
제조 공장의 작업 처리를 최적화하는 새로운 응용 프로그램을 개발 중입니다.
오버 플로우 용량을위한 "버퍼"로 작동하는 데이터 구조를 구현해야 합니다.
제조 용량을 사용할 수 있는 경우 버퍼의 항목을 버퍼에 추가 한 순서대로 처리해야 합니다.
어떤 데이터 구조를 사용하여 그러한 버퍼를 구현해야 합니까?
A. 링크 된 목록
B. 대기열
C. 배열
D. 스택
Answer: B

NEW QUESTION: 3
DRAG DROP
Testing has indicated there are performance issues with the user interface.
You need to recommend methods to improve the performance of the user interface.
What should you recommend? To answer, drag the appropriate technologies to the correct user interface components. Each technology 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.
NOTE: Each connect selection is worth one point.

Answer:
Explanation:

Explanation:

Box 1: Azure Search Index
In Azure Search, an index is a persistent store of documents and other constructs used by an Azure Search service. A document is a single unit of searchable data in your index. For example, an e-commerce retailer might have a document for each item they sell, a news organization might have a document for each article, and so forth. Mapping these concepts to more familiar database equivalents: an index is conceptually similar to a table, and documents are roughly equivalent to rows in a table.
When you add/upload documents and submit search queries to Azure Search, you submit your requests to a specific index in your search service.
Scenario: Document Inventory: This page shows a list of all processed documents provided by a customer. The page can be configured to show documents for a selected category.
Box 2: Azure Cosmos DB Index
Azure Cosmos DB supports indexing and querying of geospatial point data that's represented using the GeoJSON specification. GeoJSON data structures are always valid JSON objects, so they can be stored and queried using Azure Cosmos DB without any specialized tools or libraries. The Azure Cosmos DB SDKs provide helper classes and methods that make it easy to work with spatial data.
Scenario: The solution uses Cosmos DB for data storage.
Document Upload Sources: This page shows a map and information about the geographic distribution of uploaded documents. This page allows users to filter the map based on assigned categories.

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