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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:
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
Who determines if a Technical and Delivery Assessment (TDA) is required and how in-depth it should be?
A. The TDA council uses the Designated Products List to determine if a review is needed and its depth.
B. The customer initiates the process and determines the depth of the solution review.
C. The IBM manufacturing requires confirmation that a pre-install TDA was completed before assembly and shipping.
D. The opportunity owner follows the Solution Assurance Trigger Criteria to determine need and depth.
Answer: D

NEW QUESTION: 3
A business analyst must understand the integration options available to identify the most appropriate solution for the customer.
Your customer has a large number of systems that need to be integrated with one another. They have been struggling with their integration because all of the systems have slightly different data structures. Now they are adding Siebel applications to their enterprise and are becoming overwhelmed with the challenge of integrating all of their systems. Which solution would you recommend to your customer?
A. Run a batch process every night to get all data into the Siebel application and then peer-to-peer to all other systems.
B. Use EAI to bring all of the data from all of the systems into the Siebel application.
C. Use an Integration Server to connect the systems.
D. Create a peer to peer connection between each system.
Answer: B

NEW QUESTION: 4
During an iteration review, the agile team asserts that a product is complete because development and review were finished. The product owner disagrees, since the product has not been tested.
What should the agile practitioner do?
A. Plan another review after the product has been tested
B. Work with the agile team and product owner to agree on the definition of done
C. Add testing to the backlog, and have the product owner reprioritize
D. Ask the project sponsor to determine whether the product is completed
Answer: B