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NEW QUESTION: 1
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. NO
B. Yes
Answer: B
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: 2
Given:

And:

What is the result?
A. ns = 100 s =125
ns = 0 s = 125
ns = 125 s = 125
B. ns = 50 s = 50
ns = 125 s = 125
ns = 0 s = 125
C. ns = 50 s = 50 ns = 125 s =125
ns = 100 s =100
D. ns = 50 s = 125 ns = 125 s = 125
ns = 0 s = 125
Answer: D

NEW QUESTION: 3
What is the key component of a Next-Best-Action strategy?
A. Work flow
B. Decision table
C. Predictive model
D. Strategy
Answer: D

NEW QUESTION: 4

A. Option C
B. Option B
C. Option D
D. Option A
Answer: B
Explanation:
Explanation
During the AMI-creation process, Amazon CC2 creates snapshots of your instance's root volume and any other CBS volumes attached to your instance New CBS volumes receive their maximum performance the moment that they are available and do not require initialization (formerly known as pre-warming).
However, storage blocks on volumes that were restored from snapshots must to initialized (pulled down from Amazon S3 and written to the volume) before you can access the block. This preliminary action takes time and can cause a significant increase in the latency of an I/O operation the first time each block is accessed. For most applications, amortizing this cost over the lifetime of the volume is acceptable.
Option A is invalid because block sizes are predetermined and should not be randomly selected.
Option C is invalid because this is part of continuous integration and hence volumes can be destroyed after the test and hence there should not be snapshots created unnecessarily Option D is invalid because the encryption is a security feature and not part of load tests normally.
For more information on CBS initialization please refer to the below link:
* http://docs.aws.amazon.com/AWSCC2/latest/UserGuide/ebs-initialize.html

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