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NEW QUESTION: 1
Which two statements about a hybrid network topology are true? (Choose two.)
A. It can be a bus topology in a closed loop
B. It can be a fully-connected network with all nodes interconnected
C. It can be a combination of two or more standard topology types
D. It can be a star topology built on top of another star topology
E. It eliminates the need for broadcasting
Answer: A,C

NEW QUESTION: 2
ネットワークアーキテクチャの特性を左から右のアーキテクチャのタイプにドラッグアンドドロップします。

Answer:
Explanation:

NEW QUESTION: 3
Note: This question is part of a series of questions that use the same scenario. For your convenience, the scenario is repeated in each question. Each question presents a different goal and answer choices, but the text of the scenario is exactly the same in each question in this series.
Start of repeated scenario
You plan to create a predictive analytics solution for credit risk assessment and fraud prediction in Azure Machine Learning. The Machine Learning workspace for the solution will be shared with other users in your organization. You will add assets to projects and conduct experiments in the workspace.
The experiments will be used for training models that will be published to provide scoring from web services.
The experiment for fraud prediction will use Machine Learning modules and APIs to train the models and will predict probabilities in an Apache Hadoop ecosystem.
End of repeated scenario.
The users will use different data sources that follow a standard format. The users will receive results in a standard format by using the fraud prediction web service. The results will be saved to a location specified by the users.
You need to provide the users with the ability to get results for different risk tolerances without affecting the calculation of the model.
Which three modules should be configured to use the Web Service Parameters? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. Import Data
B. Evaluate Model
C. Select Columns in Dataset
D. Time Series Anomaly Detection
E. Export Data
Answer: A,B,E
Explanation:
Explanation/Reference:
Explanation:
A: From scenario: The experiment for fraud prediction will use Machine Learning modules and APIs to train the models and will predict probabilities in an Apache Hadoop ecosystem.
BD: An Azure Machine Learning web service is created by publishing an experiment that contains modules with configurable parameters. In some cases, you may want to change the module behavior while the web service is running. Web Service Parameters allow you to do this task.
A common example is setting up the Import Data module so that the user of the published web service can specify a different data source when the web service is accessed. Or configuring the Export Data module so that a different destination can be specified References: https://docs.microsoft.com/en-us/azure/machine-learning/studio/web-service-parameters

NEW QUESTION: 4
The BEST way to determine if an anomaly-based intrusion detection system (IDS) is properly installed is to:
A. benchmark the IDS against a peer site.
B. simulate an attack and review IDS performance.
C. use a honeypot to check for unusual activity.
D. audit the configuration of the IDS.
Answer: B
Explanation:
Explanation
Simulating an attack on the network demonstrates whether the intrusion detection system (IDS) is properly tuned. Reviewing the configuration may or may not reveal weaknesses since an anomaly-based system uses trends to identify potential attacks. A honeypot is not a good first step since it would need to have already been penetrated. Benchmarking against a peer site would generally not be practical or useful.