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NEW QUESTION: 1
HOTSPOT
You have an Exchange Server 2013 organization that contains four servers. The servers are configured as shown in the following table.
You run Get-TransportConfig and receive the output shown in the exhibit. (Click the Exhibit button.)
Use the drop-down menus to select the answer choice that completes each statement.
Answer:
Explanation:
NEW QUESTION: 2
You plan to configure OTV multicast. What must you enable to use SSM?
A. IGMFv3
B. PIM-DM
C. IGMPv2
D. PIM Anycast-RP
E. PIM-SM
Answer: E
NEW QUESTION: 3
Refer to the exhibit.
The server for the finance department is not reachable consistently on the 200.30.40.0/24 network and after every second month it gets a new IP address. Which two actions must be taken to resolve this Issue? (Choose two.)
A. Configure the router to exclude a server IP address.
B. Configure the server to use DHCP on the network with default gateway 200 30.40.100.
C. Configure the server to use DHCP on the network with default gateway 200 30.30.100.
D. Configure the server with a static IP address and default gateway.
Answer: A,D
NEW QUESTION: 4
Azure Machine Learning Studioを使用して実験を作成しています。
評価のためにデータを4つのサブセットに分割する必要があります。データには高度な欠損値があります。
分析用のデータを準備する必要があります。
実験を作成するための適切な方法を選択する必要があります。
どの3つのモジュールを順番に実行する必要がありますか？回答するには、適切なアクションをアクションのリストから回答エリアに移動し、正しい順序に並べます。
注：回答の選択肢の複数の順序が正しいです。選択した正しい注文のいずれかのクレジットを受け取ります。
Answer:
Explanation:
Explanation:
The Clean Missing Data module in Azure Machine Learning Studio, to remove, replace, or infer missing values.
Incorrect Answers:
Latent Direchlet Transformation: Latent Dirichlet Allocation module in Azure Machine Learning Studio, to group otherwise unclassified text into a number of categories. Latent Dirichlet Allocation (LDA) is often used in natural language processing (NLP) to find texts that are similar. Another common term is topic modeling.
Build Counting Transform: Build Counting Transform module in Azure Machine Learning Studio, to analyze training data. From this data, the module builds a count table as well as a set of count-based features that can be used in a predictive model.
Missing Value Scrubber: The Missing Values Scrubber module is deprecated.
Feature hashing: Feature hashing is used for linguistics, and works by converting unique tokens into integers.
Replace discrete values: the Replace Discrete Values module in Azure Machine Learning Studio is used to generate a probability score that can be used to represent a discrete value. This score can be useful for understanding the information value of the discrete values.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data
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Feedbacks
Aalk - 2014-05-05 16:45:18
Plato - 2014-05-05 16:45:51
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