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NEW QUESTION: 1
ホストベースのソリューションと比較したネットワークベースのマルウェア対策の利点は何ですか？
A. エントリポイントでマルウェアをブロックできます。
B. マルウェアの検出にヒューリスティックエンジンを使用できます。
C. クラウドから署名と評判を受け取ることができます。
D. ホスト上のファイルの整合性をチェックできます。
Answer: A

NEW QUESTION: 2
The stakeholders on a new compliance project request a delivery date for the project from the project manager There are a number of internal as well as external dependencies for the project and milestones that have to be met within certain timeframes What should the project manager do to determine the delivery date?
A. Assess resource availability.
B. Define the critical path.
C. Build a work breakdown structure (WBS)
D. Baseline the project
Answer: B

NEW QUESTION: 3



A. Option D
B. Option A
C. Option B
D. Option C
Answer: D
Explanation:
References: https://technet.microsoft.com/en-us/library/gg412785.aspx

NEW QUESTION: 4
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:
Box 1: Yes
In machine learning, if you have labeled data, that means your data is marked up, or annotated, to show the target, which is the answer you want your machine learning model to predict.
In general, data labeling can refer to tasks that include data tagging, annotation, classification, moderation, transcription, or processing.
Box 2: No
Box 3: No
Accuracy is simply the proportion of correctly classified instances. It is usually the first metric you look at when evaluating a classifier. However, when the test data is unbalanced (where most of the instances belong to one of the classes), or you are more interested in the performance on either one of the classes, accuracy doesn't really capture the effectiveness of a classifier.
Reference:
https://www.cloudfactory.com/data-labeling-guide
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance