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CertNexus Exam AIP-210 Topic 2 Question 31 Discussion

Actual exam question for CertNexus's AIP-210 exam
Question #: 31
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: D

Neural network models are suitable for classification problems with a large number of features, because they can learn complex and non-linear patterns from high-dimensional data. They can also handle image data, which is likely to be the input for the human face detection problem. Neural networks can also be trained using transfer learning, which can leverage pre-trained models on similar tasks and improve the accuracy and efficiency of the model. Reference: [Neural network - Wikipedia], [Transfer Learning - Machine Learning's Next Frontier]


by Lavonda at Sep 18, 2024, 07:55 AM

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Carin
1 months ago
I'm gonna have to go with C on this one. Hiding the decision-making process is the opposite of what Privacy by Design is all about.
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Jennie
1 months ago
D is an interesting choice, but using quasi-identifiers could still lead to privacy breaches. C is the safest bet here.
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Mickie
2 months ago
Haha, Option B is the classic 'more data is better' approach. Nice try, but that's not very privacy-friendly!
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Dana
5 days ago
D) Utilizing quasi-identifiers and non-unique identifiers, alone or in combination.
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Carmelina
9 days ago
C) Understanding, documenting, and displaying data lineage.
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Vanna
1 months ago
A) Avoiding mechanisms to explain and justify automated decisions.
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Domonique
2 months ago
I agree, C is the way to go. Privacy by Design means being transparent about data usage, not just collecting as much as possible.
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Francoise
4 days ago
User 3: Definitely, understanding and documenting data lineage is essential for accountability.
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Terrilyn
5 days ago
User 2: I agree, transparency is key in building ML systems with Privacy by Design.
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Kristeen
8 days ago
User 1: I think C is the best option. Data lineage is crucial for privacy.
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Hassie
2 months ago
Option C seems to be the correct answer. Understanding and documenting data lineage is crucial for maintaining privacy in an ML system.
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Jame
2 months ago
I'm not sure, but I don't think it's A) Avoiding mechanisms to explain and justify automated decisions.
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Bethanie
2 months ago
I agree with Floyd. Data lineage is important for transparency and accountability in ML systems.
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Floyd
2 months ago
I think the answer is C) Understanding, documenting, and displaying data lineage.
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Shizue
2 months ago
I'm not sure, but I think C could also be a good option. Understanding data lineage is important for privacy.
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Selma
2 months ago
I agree with Joaquin. Using quasi-identifiers and non-unique identifiers helps protect privacy.
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Joaquin
3 months ago
I think the answer is D.
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