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NEW QUESTION: 1
Which dynamic view displays the status of block-change tracking?
A. V$BLOCK_CHANGE_TRACKING
B. V$BLOCK_CHANGE
C. V$BLOCK_TRACKING
D. V$BLOCKCHANGE
Answer: A
Explanation:
Explanation/Reference:
Explanation:
V$BLOCK_CHANGE_TRACKING displays the status of block change tracking for the database.
NEW QUESTION: 2
ワイヤレス環境の2.4 GHz周波数帯域の特性である2つのステートメントはどれですか（2つ選択してください）
A. 14の使用可能なチャネル
B. FCCはチャンネル1から15を許可します
C. FCCはチャネル1〜14を許可します
D. 11の使用可能なチャネル
E. FCCはチャンネル1から11を許可します
Answer: A,E
Explanation:
Explanation
There is a total of fourteen channels defined for use by Wi-Fi 802.11 for the 2.4 GHz ISM band. Not all of the channels are allowed in all countries: 11 are allowed by the FCC and used in what is often termed the North American domain, and 13 are allowed in Europe where channels have been defined by ETSI. The WLAN / Wi-Fi channels are spaced 5 MHz apart (with the exception of a 12 MHz spacing between the last two channels).
https://www.radio-electronics.com/info/wireless/wi-fi/80211-channels-number-frequencies-ban dwidth.php
NEW QUESTION: 3
A. Option A
B. Option D
C. Option B
D. Option C
Answer: A,D
NEW QUESTION: 4
You are building a regression model tot estimating the number of calls during an event.
You need to determine whether the feature values achieve the conditions to build a Poisson regression model.
Which two conditions must the feature set contain? I ach correct answer presents part of the solution. NOTE: Each correct selection is worth one point.
A. The label data must be non discrete.
B. The data must be whole numbers.
C. The label data must be a negative value.
D. The label data can be positive or negative,
E. The label data must be a positive value
Answer: B,E
Explanation:
Poisson regression is intended for use in regression models that are used to predict numeric values, typically counts. Therefore, you should use this module to create your regression model only if the values you are trying to predict fit the following conditions:
The response variable has a Poisson distribution.
Counts cannot be negative. The method will fail outright if you attempt to use it with negative labels.
A Poisson distribution is a discrete distribution; therefore, it is not meaningful to use this method with non-whole numbers.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/poisson-regression
