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NEW QUESTION: 1
同じサブスクリプション内の10台のAzure仮想マシンで実行される5つの.NET Coreアプリケーションがあります。
アプリケーションが同じAzure Active Directory（Azure AD）IDを使用して認証できるようにするためのソリューションを推奨する必要があります。ソリューションは以下の要件を満たす必要があります。
* アプリケーションが10台の仮想マシンで実行されている場合にのみ認証できることを確認してください。
* 管理作業を最小限に抑えます。
あなたは推薦に何を含めるべきですか？回答するには、回答領域で適切なオプションを選択します。
注：それぞれ正しい選択は1ポイントの価値があります。

Answer:
Explanation:

NEW QUESTION: 2
실체 A는 실체 B를 US $ 1.000.000에 인수합니다. 취득 당시 인식된 식별 가능한 자산, 부채 및 우발 부채의 순 공정 가치는 US $ 900,000의 장부 금액과 US $ 800,000의 공정 가치를 가졌습니다. 취득일에 기록한 영업권 실체 A의 금액은
A. US $ 100,000
B. US $ 0
C. US $ 300,000
D. US $ 200,000
Answer: D
Explanation:
Goodwill equals the excess of the acquisition cost over the acquirer's interest in the net fair value of the identifiable assets, liabilities and contingent liabilities recognized. Consequently goodwill is US $200.000$1,000,000 - $800,000).

NEW QUESTION: 3
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You create an Azure Machine Learning service datastore in a workspace. The datastore contains the following files:
* /data/2018/Q1 .csv
* /data/2018/Q2.csv
* /data/2018/Q3.csv
* /data/2018/Q4.csv
* /data/2019/Q1.csv
All files store data in the following format:
id,f1,f2,l
1,1,2,0
2,1,1,1
3.2.1.0
You run the following code:

You need to create a dataset named training_data and load the data from all files into a single data frame by using the following code:

Solution: Run the following code:

Does the solution meet the goal?
A. Yes
B. No
Answer: A
Explanation:
Explanation
Use two file paths.
Use Dataset.Tabular_from_delimeted as the data isn't cleansed.
Note:
A TabularDataset represents data in a tabular format by parsing the provided file or list of files. This provides you with the ability to materialize the data into a pandas or Spark DataFrame so you can work with familiar data preparation and training libraries without having to leave your notebook. You can create a TabularDataset object from .csv, .tsv, .parquet, .jsonl files, and from SQL query results.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-register-datasets