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NEW QUESTION: 1
Your company has two main offices and 10 branch offices. Each office is configured as a separate Active Directory site.
The main offices sites are named Site1 and Site2. Each office connects to Site1 and Site2 by using a WAN link. Each site contains a domain controller that runs Windows Server 2008.
You are redesigning the Active Directory infrastructure.
You plan to implement domain controllers that run Windows Server 2012 and decommission all of the domain controllers that run Windows Server 2008.
You need to recommend a placement plan for the Windows Server 2012 domain controllers to meet the following requirements:
Ensure that users can log on to the domain if a domain controller or a WAN link fails.
Minimize the number of domain controllers implemented.
What should you include in the recommendation? (Each correct answer presents part of the solution.
Choose all that apply.)
A. Writable domain controllers in the branch office sites
B. A writable domain controller in Site2
C. A writable domain controller in Site1
D. Read-only domain controllers (RODCs) in the branch office sites
Answer: A,B,C
NEW QUESTION: 2
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この状況で最も費用効果の高いストレージソリューションは何ですか？
A. Amazon S3 Glacierストレージクラスを使用してステートメントを保存します。30日後にステートメントをAmazon S3 Glacier Deep Archiveストレージに移動するライフサイクルポリシーを作成します。
B. Amazon S3スタンダードストレージクラスを使用してステートメントを保存します。 1日後にステートメントをAmazon S3 Glacierストレージに移動するライフサイクルポリシーを作成します。
C. Amazon S3標準ストレージクラスを使用してステートメントを保存します。 30日後にステートメントをAmazon S3 One Zone-InfrequencyAccess（S3 One Zone-IA）ストレージに移動するライフサイクルポリシーを作成します。
D. Amazon S3標準低頻度アクセス（S3標準-IA）ストレージクラスを使用してステートメントを保存します。
30日後にステートメントをAmazon S3 Glacierストレージに移動するライフサイクルポリシーを作成します。
Answer: A
Explanation:
Explanation
Q
NEW QUESTION: 3
A. Option C
B. Option D
C. Option B
D. Option A
Answer: B
NEW QUESTION: 4
コンピュータビジョンの種類を適切なシナリオに一致させます。
答えるには、適切なワークロードタイプを左側の列から右側のシナリオにドラッグします。各ワークロードタイプは、1回使用することも、複数回使用することも、まったく使用しないこともできます。
注：正しい選択はそれぞれ1ポイントの価値があります。
Answer:
Explanation:
Explanation
Box 1: Facial recognition
Face detection that perceives faces and attributes in an image; person identification that matches an individual in your private repository of up to 1 million people; perceived emotion recognition that detects a range of facial expressions like happiness, contempt, neutrality, and fear; and recognition and grouping of similar faces in images.
Box 2: OCR
Box 3: Objection detection
Object detection is similar to tagging, but the API returns the bounding box coordinates (in pixels) for each object found. For example, if an image contains a dog, cat and person, the Detect operation will list those objects together with their coordinates in the image. You can use this functionality to process the relationships between the objects in an image. It also lets you determine whether there are multiple instances of the same tag in an image.
The Detect API applies tags based on the objects or living things identified in the image. There is currently no formal relationship between the tagging taxonomy and the object detection taxonomy. At a conceptual level, the Detect API only finds objects and living things, while the Tag API can also include contextual terms like
"indoor", which can't be localized with bounding boxes.
Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/face/
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection