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IIBA CBDA Exam - Topic 2 Question 51 Discussion

While creating a dataset for analysis, the analyst reviews the data collected and finds a large percentage of records are missing values. Which activity would the analyst perform in order to use this dataset?
C) Weighting
A) Clustering
B) Scale validation
D) Factor analysis

IIBA CBDA Exam - Topic 2 Question 51 Discussion

Actual exam question for IIBA's CBDA exam
Question #: 51
Topic #: 2
[All CBDA Questions]

While creating a dataset for analysis, the analyst reviews the data collected and finds a large percentage of records are missing values. Which activity would the analyst perform in order to use this dataset?

Show Suggested Answer Hide Answer
Suggested Answer: C

Weighting is a technique that assigns different values or weights to different records or variables in a dataset, based on their importance or relevance. Weighting can be used to handle missing values by giving them a lower weight or imputing them with a weighted average of other values. Weighting can also help to adjust for sampling bias or non-response bias in the data collection process. Reference:

* Understanding the Guide to Business Data Analytics, page 16

* Business Analysis Certification in Data Analytics, CBDA | IIBA, CBDA Competencies, Domain 3: Analyze Data

* CERTIFICATION IN BUSINESS DATA ANALYTICS HANDBOOK - IIBA, page 8, CBDA Exam Sample Questions and Self-Assessment, Question 4


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Valentin
4 days ago
Wait, can you really use a dataset with that many missing values?
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Maryann
9 days ago
I think weighting could help adjust for those gaps.
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Vi
14 days ago
Definitely not clustering with so many missing values!
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Evangelina
19 days ago
Missing values need to be addressed before analysis.
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Lavonda
24 days ago
Factor analysis sounds familiar, but I think it’s more about reducing dimensions rather than fixing missing data.
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Theron
29 days ago
I feel like weighting could be relevant here, but I’m not entirely confident about how it applies to missing values specifically.
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Thersa
1 month ago
I remember practicing a question where we had to decide on methods for dealing with missing data, but I can't recall if clustering was the right choice.
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Alease
1 month ago
I think the analyst might need to handle the missing values first, but I'm not sure which option directly addresses that.
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