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NEW QUESTION: 1
Azure Application Gateway의 성능 문제를 해결하고 있습니다.
지난 6 시간 동안의 총 요청 수와 실패한 요청 수를 비교해야 합니다.
무엇을 사용해야 합니까?
A. Azure Network Watcher의 NSG 흐름 로그
B. Azure Network Watcher의 연결 모니터
C. Application Gateway의 지표
D. Application Gateway의 진단 로그
Answer: C
Explanation:
Application Gateway currently has seven metrics to view performance counters.
Metrics are a feature for certain Azure resources where you can view performance counters in the portal. For Application Gateway, the following metrics are available:
* Total Requests
* Failed Requests
* Current Connections
* Healthy Host Count
* Response Status
* Throughput
* Unhealthy Host count
You can filter on a per backend pool basis to show healthy/unhealthy hosts in a specific backend pool References:
https://docs.microsoft.com/en-us/azure/application-gateway/application-gatewaydiagnostics# Metrics
NEW QUESTION: 2
You administer a Microsoft SQL Server 2012 database named Contoso on a server named Server01.
You need to track all SELECT statements issued in the Contoso database only by users in a role named Sales.
What should you create?
A. A Policy
B. A Resource Pool
C. An Alert
D. An Extended Event session
E. A Database Audit Specification
F. A Data Collector Set
G. A Server Audit Specification
H. A SQL Profiler Trace
Answer: E
NEW QUESTION: 3
Azure Machine Learning Studioで実験を作成します。 10,000行を含むトレーニングデータセットを追加します。最初の9,000行はクラス0（90パーセント）を表します。
残りの1,000行はクラス1（10パーセント）を表します。
トレーニングセットは、2つのクラス間の不均衡です。 5つのデータ行を使用して、クラス1のトレーニング例の数を4,000に増やす必要があります。 Synthetic Minority Oversampling Technique（SMOTE）モジュールを実験に追加します。
モジュールを構成する必要があります。
どの値を使用する必要がありますか？回答するには、回答領域のダイアログボックス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: 300
You type 300 (%), the module triples the percentage of minority cases (3000) compared to the original dataset (1000).
Box 2: 5
We should use 5 data rows.
Use the Number of nearest neighbors option to determine the size of the feature space that the SMOTE algorithm uses when in building new cases. A nearest neighbor is a row of data (a case) that is very similar to some target case. The distance between any two cases is measured by combining the weighted vectors of all features.
By increasing the number of nearest neighbors, you get features from more cases.
By keeping the number of nearest neighbors low, you use features that are more like those in the original sample.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote