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IAPP Exam CIPM Topic 9 Question 45 Discussion

Actual exam question for IAPP's CIPM exam
Question #: 45
Topic #: 9
[All CIPM Questions]

What are you doing if you succumb to "overgeneralization" when analyzing data from metrics?

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Suggested Answer: D

The first step to mitigate further risks when a systems audit uncovers a shared drive folder containing sensitive employee data with no access controls is to restrict access to the folder. This can be done by implementing appropriate access controls, such as user authentication, role-based access, and permissions, to ensure that only authorized individuals can view and access the sensitive data.


https://www.sans.org/cyber-security-summit/archives/file/summit-archive-1492158151.pdf

https://www.itgovernance.co.uk/blog/5-reasons-why-employees-dont-report-data-breaches/

https://www.ncsc.gov.uk/guidance/report-cyber-incident

by Markus at Mar 31, 2023, 02:31 PM

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Ellen
3 days ago
Hah, I almost picked B. 'Possessing too many types of data' - that's a new one! Gotta watch out for that data overload.
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Lemuel
4 days ago
I was gonna go with D, but now I'm not so sure. Overgeneralization can definitely happen when you try to measure too many things at once.
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Shawnda
13 days ago
Hmm, I was leaning towards A, but C makes a lot of sense too. Gotta be careful not to draw big conclusions from a small data set.
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Antonio
A) Using data that is too broad to capture specific meanings.
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Dell
28 days ago
I think C is the correct answer. Overgeneralizing from limited data is a common mistake in data analysis.
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Rosalyn
12 days ago
It's important to ensure we have enough data to draw meaningful insights.
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Narcisa
13 days ago
I agree, using limited data to support broad conclusions can lead to inaccurate analysis.
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Callie
1 months ago
I believe option C is the correct answer. Using limited data can lead to inaccurate conclusions.
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Paris
1 months ago
I agree with Alysa. It's important to avoid overgeneralization and make sure our data is specific enough.
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Alysa
1 months ago
I think if you succumb to overgeneralization, you're using limited data to support broad conclusions.
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a