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CertNexus AIP-210 Exam - Topic 2 Question 45 Discussion

Which of the following principles supports building an ML system with a Privacy by Design methodology?
C) Understanding, documenting, and displaying data lineage.
A) Avoiding mechanisms to explain and justify automated decisions.
B) Collecting and processing the largest amount of data possible.
D) Utilizing quasi-identifiers and non-unique identifiers, alone or in combination.

CertNexus AIP-210 Exam - Topic 2 Question 45 Discussion

Actual exam question for CertNexus's AIP-210 exam
Question #: 45
Topic #: 2
[All AIP-210 Questions]

Which of the following principles supports building an ML system with a Privacy by Design methodology?

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Suggested Answer: C

Data lineage is the process of tracking the origin, transformation, and usage of data throughout its lifecycle. It helps to ensure data quality, integrity, and provenance. Data lineage also supports the Privacy by Design methodology, which is a framework that aims to embed privacy principles into the design and operation of systems, processes, and products that involve personal data.By understanding, documenting, and displaying data lineage, an ML system can demonstrate how it collects, processes, stores, and deletes personal data in a transparent and accountable manner3.


by Ezekiel at Jul 13, 2025, 02:23 PM

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Anisha
7 months ago
Collecting more data (B) seems counterintuitive to privacy.
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Marylin
8 months ago
Wow, I didn't realize data lineage was so important!
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Barbra
8 months ago
I think A is more relevant, honestly.
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Vicki
8 months ago
Not sure if D really fits with Privacy by Design.
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Kenneth
8 months ago
C is definitely the right choice!
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Shantell
8 months ago
I think D is definitely not the right choice. Using quasi-identifiers seems like it would go against the idea of Privacy by Design.
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Colette
9 months ago
I feel like A could be a trick option. Avoiding explanations sounds counterintuitive to privacy, but I can't recall the exact principle.
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Pearlene
9 months ago
I remember practicing a question about data collection, and it emphasized that collecting too much data can be risky. So, I don't think B is right.
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Marquetta
9 months ago
I think the answer might be C, but I'm not entirely sure. It seems like understanding data lineage is really important for privacy.
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Marica
9 months ago
A and B seem like the opposite of what Privacy by Design is about. I'm leaning towards C or D, but I'll need to think it through.
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Lai
9 months ago
Option D sounds like it could be the right answer, but I'm not 100% confident. I'll need to double-check my understanding of quasi-identifiers.
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Delmy
10 months ago
I think the answer is C. Understanding and documenting data lineage is key to ensuring privacy in an ML system.
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Herman
10 months ago
Hmm, I'm not sure about this one. I'll have to review my notes on Privacy by Design to figure out which principle is most relevant.
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Cristy
10 months ago
This one seems tricky. I'll need to think carefully about the principles of Privacy by Design and how they relate to the options provided.
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Catarina
11 months ago
Ooh, D sounds interesting. Using non-unique identifiers could be a clever way to protect privacy. I'll have to look into that more.
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Arlyne
10 months ago
I agree, option D has some potential!
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Rasheeda
10 months ago
But isn't it risky? Non-unique identifiers might still expose data.
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Candida
12 months ago
Ha! Avoiding explanations for automated decisions? That's the opposite of what we want. Privacy by Design is all about transparency and accountability.
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Vernell
10 months ago
User 2: Definitely, we need to understand and document data lineage.
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Adell
11 months ago
User 1: I agree, transparency is key for Privacy by Design.
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Pearly
1 year ago
That's a good point, Leonora. Both C) and D) could be valid answers for building a privacy-focused ML system.
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Leonora
1 year ago
I'm not sure about that. I think D) Utilizing quasi-identifiers and non-unique identifiers is also important for privacy.
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Margot
1 year ago
I'm not sure, but B sounds like a terrible idea for a privacy-focused system. Collecting massive amounts of data without regard for privacy is a big no-no.
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Mignon
10 months ago
D) Utilizing quasi-identifiers and non-unique identifiers, alone or in combination.
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Eladia
12 months ago
C) Understanding, documenting, and displaying data lineage.
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Marta
1 year ago
A) Avoiding mechanisms to explain and justify automated decisions.
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Oliva
1 year ago
I agree with Pearly. Data lineage is crucial for ensuring privacy by design in ML systems.
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Pearly
1 year ago
I think the answer is C) Understanding, documenting, and displaying data lineage.
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Aretha
1 year ago
C definitely seems like the right answer here. Understanding and documenting data lineage is crucial for a privacy-focused ML system.
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Jacinta
12 months ago
Yeah, it's important to know where your data comes from.
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Paris
12 months ago
I agree, data lineage is key for privacy.
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