Dama CDMP-RMD Exam - Topic 4 Question 19 Discussion

Actual exam question for Dama's CDMP-RMD exam

Question #: 19
Topic #: 4

[All CDMP-RMD Questions]

Choosing unreliable sources for data, which can cause data quality issues, is a result of:

AToo much data

BImmature data architecture

CWeak Master Data Management

DToo little data

ENo chance controls

Show Suggested Answer

Suggested Answer: C

Choosing unreliable sources for data can lead to significant data quality issues. This problem is often a symptom of underlying issues in data management practices.

Too Much Data:

While having excessive data can create challenges, it is not directly related to the reliability of data sources.

Immature Data Architecture:

An immature data architecture can contribute to various data issues, but it specifically relates to the overall design and infrastructure rather than the selection of data sources.

Weak Master Data Management (MDM):

MDM is crucial for ensuring data quality and consistency. Weak MDM practices can lead to poor data governance, lack of standardization, and the use of unreliable data sources.

Effective MDM involves establishing strong governance policies, data stewardship, and validation processes to ensure data is sourced from reliable and authoritative sources.

Too Little Data:

Insufficient data can be problematic but is not directly related to choosing unreliable data sources.

No Chance Controls:

This option is not a standard term in data management and does not directly address the issue of data source reliability.

DAMA-DMBOK (Data Management Body of Knowledge) Framework

CDMP (Certified Data Management Professional) Exam Study Materials

by Nikita at Nov 19, 2024, 06:32 PM

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Walton

3 months ago

I disagree with D, too little data isn't the main problem here.

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Nida

3 months ago

E makes sense too, no chance controls lead to chaos.

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Ivan

4 months ago

Surprised that people think it's A, too much data can actually help!

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Alysa

4 months ago

I think it's more about C, weak Master Data Management.

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Kaycee

4 months ago

Definitely B, immature data architecture is a big issue.

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Quentin

4 months ago

No chance controls sounds familiar, but I wonder if it really fits this scenario. We had a case study that touched on this, but I’m a bit confused.

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Emerson

5 months ago

I feel like too much data could also contribute to this, but I can't recall the specifics from our study sessions.

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Rosina

5 months ago

I think weak Master Data Management might be the key factor. We practiced a similar question where it was highlighted as a major cause of data quality problems.

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Hyman

5 months ago

I remember we discussed how immature data architecture can lead to unreliable sources, but I'm not entirely sure if that's the main issue here.

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Lai

5 months ago

Ah, I see now. It's about the data architecture and governance processes, not the volume of data. I think I've got it - option C is the way to go.

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Latrice

5 months ago

Too much data? That doesn't seem right. I'm leaning towards either B or C as the best answer, but I'll review the question again to be sure.

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Holley

5 months ago

Weak Master Data Management seems like the most likely culprit here. I'll double-check the other options, but that's the one that stands out to me.

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Buck

5 months ago

Hmm, I'm a bit unsure about this one. I'll need to think it through step-by-step to make sure I understand the concepts before selecting an answer.

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Brynn

5 months ago

This question seems straightforward. I'll carefully read through the options and think about which one best describes the root cause of using unreliable data sources.

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