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NEW QUESTION: 1
Refer to the exhibit.
What is the result of the SPAN configuration on a Cisco switch?
A. Configure a SPAN session to monitor the received traffic on interface g0/5 only for VLAN 3.
B. Configure a SPAN session to monitor the received traffic on interface g0/4 for all VLANs except VLAN 3.
C. Configure a SPAN session to monitor the received traffic on interface g0/5 for all VLANs except VLAN 3.
D. Configure a SPAN session to monitor the received traffic on interface g0/4 only for VLAN 3.
Answer: D
NEW QUESTION: 2
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You are analyzing a numerical dataset which contains missing values in several columns.
You must clean the missing values using an appropriate operation without affecting the dimensionality of the feature set.
You need to analyze a full dataset to include all values.
Solution: Replace each missing value using the Multiple Imputation by Chained Equations (MICE) method.
Does the solution meet the goal?
A. Yes
B. NO
Answer: A
Explanation:
Explanation
Replace using MICE: For each missing value, this option assigns a new value, which is calculated by using a method described in the statistical literature as "Multivariate Imputation using Chained Equations" or
"Multiple Imputation by Chained Equations". With a multiple imputation method, each variable with missing data is modeled conditionally using the other variables in the data before filling in the missing values.
Note: Multivariate imputation by chained equations (MICE), sometimes called "fully conditional specification" or "sequential regression multiple imputation" has emerged in the statistical literature as one principled method of addressing missing data. Creating multiple imputations, as opposed to single imputations, accounts for the statistical uncertainty in the imputations. In addition, the chained equations approach is very flexible and can handle variables of varying types (e.g., continuous or binary) as well as complexities such as bounds or survey skip patterns.
References:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3074241/
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data
NEW QUESTION: 3
A. Option B
B. Option D
C. Option C
D. Option A
Answer: A
NEW QUESTION: 4
You wish to configure a task flow that allows a user to create an account and add one or more contacts. Which three steps must appear in the task flow? (Choose three.)
A. A commit step to save each contact record entered before the user enters a new one.
B. A task view step that displays a view containing, at minimum, a task applet.
C. A task view step that is bound to a summary view.
D. A Siebel operation step to create a new account record.
E. A Siebel operation step to create a transient business component.
F. A decision step that controls flow to a Siebel operation step to create a new contact.
Answer: B,D,F
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