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NEW QUESTION: 1

A. Option B
B. Option C
C. Option A
D. Option D
Answer: A,C

NEW QUESTION: 2
When auditing the effectiveness of a biometric system, which of the following indicators would be MOST important to review?
A. System response time
B. False negatives
C. Failure to enroll rate
D. False acceptance rate
Answer: D

NEW QUESTION: 3
メディア会社は、ソリューションアーキテクトに、Amazon EC2インスタンスの中央ドキュメントストアとして機能する高可用性ストレージソリューションの設計を依頼しました。ストレージソリューションは、POSIX準拠の動的なスケールであり、最大100の同時EC2インスタンスに対応できる必要があります。
これらの要件を満たすソリューションはどれですか？
A. Amazon S3バケットを作成し、このバケットにすべてのドキュメントを保存します。
B. Amazon EBSボリュームを作成し、複数のユーザーがそのボリュームをEC2インスタンスにマウントできるようにします
C. Amazon Glacierを使用してすべてのドキュメントを保存する
D. Amazon Elastic File System（Amazon EFS）を作成して、ドキュメントを保存および共有します。
Answer: D

NEW QUESTION: 4
You need to ensure that emergency road response vehicles are dispatched automatically.
How should you design the processing system? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation

Box1: API App

* Events generated from the IoT data sources are sent to the stream ingestion layer through Azure HDInsight Kafka as a stream of messages. HDInsight Kafka stores streams of data in topics for a configurable of time.
* Kafka consumer, Azure Databricks, picks up the message in real time from the Kafka topic, to process the data based on the business logic and can then send to Serving layer for storage.
* Downstream storage services, like Azure Cosmos DB, Azure SQL Data warehouse, or Azure SQL DB, will then be a data source for presentation and action layer.
* Business analysts can use Microsoft Power BI to analyze warehoused data. Other applications can be built upon the serving layer as well. For example, we can expose APIs based on the service layer data for third party uses.
Box 2: Cosmos DB Change Feed
Change feed support in Azure Cosmos DB works by listening to an Azure Cosmos DB container for any changes. It then outputs the sorted list of documents that were changed in the order in which they were modified.
The change feed in Azure Cosmos DB enables you to build efficient and scalable solutions for each of these patterns, as shown in the following image:

References:
https://docs.microsoft.com/bs-cyrl-ba/azure/architecture/example-scenario/data/realtime-analytics-vehicle-iot?vie