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NEW QUESTION: 1
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 have a SharePoint Server farm and a SharePoint Online subscription. You plan to perform a bulk migration by using the SharePoint Migration Tool.
You create the following CSV file. (Line numbers are included for reference only.)

You need to modify the CSV file to ensure that the bulk migration will complete.
successfully. Solution: You change line 02 to use a UNC path instead of a local file path.
Does this meet the goal?
A. Yes
B. No
Answer: B
Explanation:
References:
https://docs.microsoft.com/en-us/sharepointmigration/how-to-format-your-csv-file-for-data-content-migration

NEW QUESTION: 2
A customer hosts a large number of JBOSS-based enterprise applications and Oracle as well as Microsoft SQL Server Databases on distributed servers. In order to reduce costs the customer initiates a project for infrastructure consolidation. The manager in charge of the project asks IBM for advice.
Which first step should the technical specialist propose?
A. An Eagle Study to prove that IBM WebSphere is less expensive than JBOSS
B. A benchmark to compare performance between Microsoft SQL server databases on distributed systems and IBM DB2 on Linux on IBM z Systems
C. An ITSO workshop to evaluate moving the entire environment on IBM z Systems
D. A fit for purpose workshop to identify which workloads are the "best-fit" on IBM z Systems
Answer: B

NEW QUESTION: 3
自然言語処理ドメインで回帰問題に取り組んでおり、データセットに1億個のラベル付きの例があります。データをランダムにシャッフルし、データセットをトレーニングサンプルとテストサンプルに分割しました（90/10の比率）。ニューラルネットワークをトレーニングし、テストセットでモデルを評価した後、モデルの二乗平均平方根誤差（RMSE）が、テストセットの2倍のトレインセットで高いことがわかります。モデルのパフォーマンスをどのように改善する必要がありますか？
A. たとえば、追加のレイヤーを導入したり、使用する語彙やn-gramのサイズを大きくしたりして、モデルの複雑さを増します。
B. より多くのデータを収集し、データセットのサイズを増やしてみてください。
C. 過剰適合を回避するために、正則化手法（バッチ正規化のドロップアウトなど）を試してください。
D. トレインテスト分割でのテストサンプルのシェアを増やします。
Answer: A