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D206 DTAN 5201 Data Cleaning FA (Qns & Ans) 2025 WGU. $12.49
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D206 DTAN 5201 Data Cleaning FA (Qns & Ans) 2025 WGU.

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D206 DTAN 5201 Data Cleaning FD206 DTAN 5201 Data Cleaning FA (Qns & Ans) 2025 WGU.D206 DTAN 5201 Data Cleaning FA (Qns & Ans) 2025 WGU.A (Qns & Ans) 2025 WGU.

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  • December 16, 2024
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  • 2024/2025
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D206 DTAN 5201

Data Cleaning

Final Assessment (Qns & Ans)

2025
General Instructions

1. Read All Questions Carefully: Make sure you understand each question.
2. Time Management: You have a specific amount of time to complete the exam.
Keep an eye on the clock and pace yourself.
3. Allowed Materials: Only use materials that are explicitly allowed. Unauthorized
materials can lead to disqualification.
4. ANS Format: Follow the required format for your ANS. For example, multiple-
choice questions might need you to select the best ANS, while essay questions
require detailed responses.
5. Academic Integrity: Adhere to the university's honor code. Any form of cheating or
plagiarism is strictly prohibited.
6. Technical Requirements: Ensure your computer and internet connection are
stable. For online exams, you might need a webcam and microphone for proctoring
purposes.
7. Submission: Submit your ANS before the time expires. Late submissions might
not be accepted.




©2024/2025

,1. Which of the following is NOT a common technique for
handling missing data?
a) Mean Imputation
b) K-Nearest Neighbors Imputation
c) Duplicate Removal
d) Last Observation Carried Forward
ANS : c) Duplicate Removal
Rationale : Duplicate removal is a technique used for
identifying and eliminating duplicate records, not specifically for
handling missing data.


2. What is the primary purpose of data normalization?
a) To summarize data
b) To scale features to a common range
c) To convert categorical data to numerical
d) To fill in missing values
ANS : b) To scale features to a common range
Rationale : Normalization scales the data to ensure that each
feature contributes equally to the distance computations that
might affect the model performance.
©2024/2025

, 3. In the context of data cleaning, what does the term "outlier"
refer to?
a) Data that is missing
b) Data that is duplicated
c) Values that differ significantly from other observations
d) Any erroneous entry
ANS : c) Values that differ significantly from other
observations
Rationale : Outliers are extreme values that can skew the results
and are often investigated and treated during data cleaning.


4. Which method is commonly used to detect outliers?
a) Standard deviation method
b) Direct observation
c) K-means clustering
d) Linear regression
ANS : a) Standard deviation method
Rationale : The standard deviation method helps in identifying
data points that lie beyond a certain threshold from the mean,
indicating potential outliers.


©2024/2025

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