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NEW QUESTION: 1
An administrator has been asked to create MQ objects on queue manager QM1 so that applications connecting to queue manager QM1 can send messages to queue PAYROLL on queue manger QM2. The following definitions have been created on QM2:
DEFINE QLOCAL (PAYROLL) REPLACE PUT (ENABLED) GET (ENABLED) +
DESCR ('Local queue for QM1 payroll details')
DEFINE CHANNEL (QM1.TO.QM2) CHLTYPE (RCVR) TRPTYPE (TCP) +
REPLACE DESCR ('Receiver channel from QM1')
What are the minimum definitions needed on QM1?
A. DEFINE QREMOTE (PAYROLL. QUERY) +
XMTIQ (QM2)RNAME (PAYROLL) RMNAME (QM2)
DEFINE QLOCAL (QM2) USAGE (XMITQ) TRIGGER+
TRIGTYPE (FIRST) TRIGDATA (QM1.TO.QM2) +
INITQ (SYSTEM.CHANNEL.INITQ)
DEFINE CHANNEL (QM1.TO.QM2) CHLTYPE (SDR) TRPTYPE (TCP) +
XMITQ (QM2) CONNAME ('9.20.9.32 (1412) ')
B. DEFINE QREMOTE (PAYROLL.QUERY) RNAME (PAYROLL) +
RQMNAME (QM2)
DEFINE CHANNEL (QM1.TO.QM2) CHLTYPE (SDR) TRPTYPE (TCP) +
XMITQ (QM2) CONNAME ('9.20.9.32 (1412) ')
C. DEFINE CHANNEL (QM1.TO.QM2) CHLTYPE (SDR) TRPTYPE (TCP) + XMITQ (QM2) CONNAME ('9.20.9.32 (1412) ')
D. DEFINE QREMOTE (PAYROLL.QUERY) RNAME (PAYROLL) +
RQMNAME (QM2)
ALTER QMGR DEFXMITQ (QM2)
DEFINE CHANNEL (QM1.TO.QM2) CHLTYPE (SDR) TRPTYPE (TCP) + XMITQ (QM2) CONNAME ('9.20.9.32 (1412) ')
Answer: A

NEW QUESTION: 2
An administrator is using the Guided Troubleshooter in the IBM Support Assistant to troubleshoot IBM WebSphere Application Server issues. After going through data collection and analysis, no reliable solution is found.
What is the administrator's best next step?
A. Open a new PMR with IBM and upload SystemOut.log from the IBM WebSphere Application Server.
B. Use the Guided Troubleshooter to open a service request with IBM, so all the troubleshooting data analyzed so far is uploaded automatically.
C. Call in a new PMR with IBM and upload Troubleshooter data to ECuREP.
D. Submit results to the IBM WebSphere Application Server forum for a quick response from other forum users.
Answer: B

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


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