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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


by Louisa at May 29, 2026, 12:09 PM

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Alpha
7 days ago
Right! It’s crucial to handle missing data before analysis.
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Carla
12 days ago
Exactly! Weighting adjusts for those gaps effectively.
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Nathalie
18 days ago
True, but without addressing missing values, validation won't help much.
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Art
23 days ago
But what about B) Scale validation? It checks data quality too.
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Cathern
28 days ago
Weighting seems best. It can make the dataset more reliable.
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Carla
1 month ago
I agree, but A) Clustering could also work if they group similar data.
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Alpha
1 month ago
I think the analyst should go for C) Weighting. It helps adjust for missing values.
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Christiane
1 month ago
Factor analysis might still work if the missing data is random.
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Valentin
2 months ago
Wait, can you really use a dataset with that many missing values?
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Maryann
2 months ago
I think weighting could help adjust for those gaps.
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Vi
2 months ago
Definitely not clustering with so many missing values!
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Evangelina
2 months ago
Missing values need to be addressed before analysis.
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Lavonda
2 months ago
Factor analysis sounds familiar, but I think it’s more about reducing dimensions rather than fixing missing data.
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Theron
2 months 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
3 months 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
3 months 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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