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NEW QUESTION: 1
ホットスポット
次の各ステートメントについて、ステートメントがtrueの場合は、[はい]を選択します。それ以外の場合は、[いいえ]を選択します。正しい選択はそれぞれ1ポイントの価値があります。

Answer:
Explanation:

NEW QUESTION: 2
Azure Stream Analytics機能を実装しています。
各要件に対してどのウィンドウ関数を使用する必要がありますか？回答するには、回答エリアで適切なオプションを選択します。
注：それぞれの正しい選択には1ポイントの価値があります。

Answer:
Explanation:

Explanation

Box 1: Tumbling
Tumbling window functions are used to segment a data stream into distinct time segments and perform a function against them, such as the example below. The key differentiators of a Tumbling window are that they repeat, do not overlap, and an event cannot belong to more than one tumbling window.

Box 2: Hoppping
Hopping window functions hop forward in time by a fixed period. It may be easy to think of them as Tumbling windows that can overlap, so events can belong to more than one Hopping window result set. To make a Hopping window the same as a Tumbling window, specify the hop size to be the same as the window size.

Box 3: Sliding
Sliding window functions, unlike Tumbling or Hopping windows, produce an output only when an event occurs. Every window will have at least one event and the window continuously moves forward by an (epsilon). Like hopping windows, events can belong to more than one sliding window.

References:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-window-functions

NEW QUESTION: 3
You are using Azure Machine Learning to train machine learning models. You need a compute target on which to remotely run the training script. You run the following Python code:


Answer:
Explanation:

Explanation:
Box 1: Yes
The compute is created within your workspace region as a resource that can be shared with other users.
Box 2: Yes
It is displayed as a compute cluster.
View compute targets
1. To see all compute targets for your workspace, use the following steps:
2. Navigate to Azure Machine Learning studio.
3. Under Manage, select Compute.
4. Select tabs at the top to show each type of compute target.

Box 3: Yes
min_nodes is not specified, so it defaults to 0.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.compute.amlcompute.amlcomputeprovisioningconfiguration
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-compute-studio

NEW QUESTION: 4
An organization uses Common Vulnerability Scoring System (CVSS) scores to prioritize remediation of vulnerabilities.
Management wants to modify the priorities based on a difficulty factor so that vulnerabilities with lower CVSS scores may get a higher priority if they are easier to implement with less risk to system functionality. Management also wants to quantify the priority. Which of the following would achieve management's objective?
A. ((CVSS Score) * 2) / Difficulty = PriorityWhere CVSS Score is weighted and Difficulty is a range from 1 to 5 with 5 being easiest and lowest risk to implement
B. (CVSS Score) * Difficulty = PriorityWhere Difficulty is a range from 0.1 to 1.0 with 1.0 being easiest and lowest risk to implement
C. (CVSS Score) / Difficulty = PriorityWhere Difficulty is a range from 1 to 10 with 10 being easiest and lowest risk to implement
D. (CVSS Score) * Difficulty = PriorityWhere Difficulty is a range from 1 to 5 with 1 being easiest and lowest risk to implement
Answer: C