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NEW QUESTION: 1
Which of the following antispam filtering techniques would BEST prevent a valid, variable-length e-mail message containing a heavily weighted spam keyword from being labeled as spam?
A. Bayesian (statistical)
B. Signature-based
C. Heuristic (rule-based)
D. Pattern matching
Answer: A
Explanation:
Bayesian filtering applies statistical modeling to messages, by performing a frequency analysis on each word within the message and then evaluating the message as a whole. Therefore, it can ignore a suspicious keyword if the entire message is withinnormal bounds. Heuristic filtering is less effective, since new exception rules may need to be defined when a valid message is labeled as spam. Signature-based filtering is useless against variable-length messages, because the calculated MD5 hash changes all the time. Finally, pattern matching is actually a degraded rule-based technique, where the rules operate at the word level using wildcards, and not at higher levels.

NEW QUESTION: 2

A. Option D
B. Option C
C. Option B
D. Option A
Answer: A

NEW QUESTION: 3
When you try to delete an attribute view you receive the error message "One or more objects are dependent upon this object". Which of the following functions can you use to find out which objects depend on this view?
A. Where-Used function
B. Display XML function
C. History function
D. Auto Documentation function
Answer: A,D

NEW QUESTION: 4
テスト要件に従ってデータを分割する方法を特定する必要があります。
どのプロパティを選択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


Scenario: Testing
You must produce multiple partitions of a dataset based on sampling using the Partition and Sample module in Azure Machine Learning Studio.
Box 1: Assign to folds
Use Assign to folds option when you want to divide the dataset into subsets of the data. This option is also useful when you want to create a custom number of folds for cross-validation, or to split rows into several groups.
Not Head: Use Head mode to get only the first n rows. This option is useful if you want to test a pipeline on a small number of rows, and don't need the data to be balanced or sampled in any way.
Not Sampling: The Sampling option supports simple random sampling or stratified random sampling. This is useful if you want to create a smaller representative sample dataset for testing.
Box 2: Partition evenly
Specify the partitioner method: Indicate how you want data to be apportioned to each partition, using these options:
Partition evenly: Use this option to place an equal number of rows in each partition. To specify the number of output partitions, type a whole number in the Specify number of folds to split evenly into text box.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/partition-and-sample