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NEW QUESTION: 1
YANG modeling supports which two comment styles? (Choose two.)
A. A single-line comment starts with "!" and ends at the end of the line.
B. A single-line comment starts with "#" and ends at the end of the line.
C. A block comment is enclosed within "/*" and "*/".
D. A single-line comment starts with "//" and ends at the end of the line.
E. A block comment is enclosed within "<--" and "->".
Answer: C,D
Explanation:
:
Comments are C++ style. A single line comment starts with "//" and ends at the end of the line. A block comment is enclosed within "/*" and "*/".
Reference:
https://tools.ietf.org/html/draft-ietf-netmod-yang-00

NEW QUESTION: 2
Which Alibaba Cloud service is used to run pods in a serverless cluster?
Score 2
A. ECI
B. ECS
Answer: A

NEW QUESTION: 3
The widespread and increasing industrial and commercial use and transportation of radioactive materials has increased the possibility, of radiological hazards resulting from accidents involving these materials.
Which of the following is not a valid observation?
A. In accordance with an interagency radiological assistance plan, the radiological emergency response capabilities of federal agencies can be used to protect public health and safety.
B. If plant and local emergency services are not adequate to cope with the situation, federal assistance can be requested.
C. Special emergency response capabilities have been established by the Environmental Protection Agency for coping with accidents involving nuclear weapons.
D. When a radiological incident occurs in a plant, some degree of immediate response by state and local public safety personnel usually will be required.
E. Accidents may occur in facilities where radioactive material (are used or processed.
Answer: C

NEW QUESTION: 4
機械学習モデルを作成しています。 null行を含むデータセットがあります。
Azure Machine Learning StudioのClean Missing Dataモジュールを使用して、データセット内のnullデータと欠落データを特定して解決する必要があります。
どのパラメーターを使用する必要がありますか？
A. 行全体を削除します
B. 平均で置換
C. 列全体を削除します
D. ホットデッキ
Answer: C
Explanation:
Remove entire row: Completely removes any row in the dataset that has one or more missing values. This is useful if the missing value can be considered randomly missing.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data