NCA-GENL Trustworthy AI Practice Question
Which of the following best describes the principle of 'Interpretability' in the context of Trustworthy AI?
⚠ Common exam trap
Candidates confuse interpretability with 'transparency' or 'accuracy,' focusing on the model's performance metrics rather than the ability to explain the specific logic behind an individual prediction.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The capability to explain the internal decision-making process in human-understandable terms.
Interpretability refers to the degree to which a human can understand the cause of a decision or prediction made by an AI model. In high-stakes domains like healthcare or finance, knowing 'why' a model arrived at a conclusion is as important as the conclusion itself. This transparency is crucial for building trust, debugging errors, and ensuring that the model complies with regulatory requirements regarding fairness and decision-making accountability.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The ability of the model to perform multiple tasks simultaneously without loss of accuracy.
Why it's wrong here
Multitasking capability is a measure of model versatility, not interpretability. While important for efficiency, it does not explain how the model reaches specific decisions or internalize the reasoning process. A multi-tasking model can be just as much of a 'black box' as a single-task model.
- ✓
The capability to explain the internal decision-making process in human-understandable terms.
Why this is correct
Interpretability is defined by the transparency of the model's reasoning. By providing insights into which features or input patterns drove a specific output, stakeholders can verify that the model is operating logically and ethically, which is a fundamental requirement for establishing user trust in complex AI systems.
- ✗
The speed at which a model can process training data during the fine-tuning phase.
Why it's wrong here
Training speed is a performance metric related to infrastructure and algorithmic efficiency. It has no correlation with the ability of a human to interpret or audit the model's logic. A fast-training model may still be entirely opaque, offering no explanation for its specific predictions or generative choices.
- ✗
The process of removing all personal identifiers from the training dataset.
Why it's wrong here
This describes data anonymization or privacy protection, not interpretability. While privacy is a pillar of Trustworthy AI, it is distinct from the requirement that the AI's internal logic and decision-making pathways must be accessible and understandable to developers, auditors, and end-users who need to verify performance.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official NVIDIA exam blueprint
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