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NEW QUESTION: 1
You have an Azure Active Directory (Azure AD) tenant named contoso.com. The tenant contains the users shown in the following table.

The tenant contains computers that run Windows 10. The computers are configured as shown in the following table.

You enable Enterprise State Roaming in contoso.com for Group1 and GroupA.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation

Enterprise State Roaming provides users with a unified experience across their Windows devices and reduces the time needed for configuring a new device.
Box 1: Yes
Box 2: No
Box 3: Yes
References:
https://docs.microsoft.com/en-us/azure//////active-directory/devices/enterprise-state-roaming-overview

NEW QUESTION: 2
あなたの会社は最近、センサーを含むいくつかのハードウェアデバイスを展開しました。
センサーは1時間ごとに新しいデータを生成します。生成されたデータはオンプレミスに保存され、数年間保持されます。
過去2か月の間に、センサーは300 GBのデータを生成しました。
データをAzureに移動し、データに対して高度な分析を実行する予定です。
データ用にAzureストレージソリューションを推奨する必要があります。
どのストレージソリューションをお勧めしますか？
A. Azure Blobストレージ
B. Azure SQLデータベース
C. Azureキューストレージ
D. Azure Cosmos DB
Answer: A
Explanation:
Explanation
References:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/data-storage

NEW QUESTION: 3
What is the preferred way to pass a small number of configuration parameters to a mapper or reducer?
A. As key-value pairs in the jobconf object.
B. As a custom input key-value pair passed to each mapper or reducer.
C. Through a static variable in the MapReduce driver class (i.e., the class that submits the MapReduce job).
D. Using a plain text file via the Distributedcache, which each mapper or reducer reads.
Answer: A
Explanation:
In Hadoop, it is sometimes difficult to pass arguments to mappers and reducers. If
the number of arguments is huge (e.g., big arrays), DistributedCache might be a good choice.
However, here, we're discussing small arguments, usually a hand of configuration parameters.
In fact, the way to configure these parameters is simple. When you initialize"JobConf"object to
launch a mapreduce job, you can set the parameter by using"set"method like:
1JobConf job = (JobConf)getConf();
2job.set("NumberOfDocuments", args[0]);
Here,"NumberOfDocuments"is the name of parameter and its value is read from"args[0]", a
command line argument.
Reference:Passing Parameters and Arguments to Mapper and Reducer in Hadoop

NEW QUESTION: 4

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