AI0-001 AI Concepts and Foundations Practice Question
A logistics company is building a model to estimate delivery times. The team has a dataset with 120,000 labeled historical deliveries, but the labels for arrival times are noisy because some drivers manually entered them hours later. The team wants to improve label quality without discarding the dataset. Which approach best addresses the noisy-label problem?
⚠ Common exam trap
The trap here is treating noisy labels as outliers to delete, when label noise can be distributed throughout the dataset and requires a modeling or robust-loss strategy rather than simple filtering.
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
✓
Model the label noise explicitly, for example with a noise-robust loss or a probabilistic noise model, and train with the noisy labels.
Noise-robust training methods explicitly account for corrupted labels, allowing the team to use the full dataset while reducing the impact of late manual entries. Unlike trimming or self-relabeling, this approach models the noise process and preserves legitimate variation, which is essential for accurate delivery-time estimation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Remove all records whose arrival times fall outside two standard deviations from the mean.
Why it's wrong here
Trimming outliers assumes that extreme values are errors, but late manual entries can occur near the mean as well, and genuine long deliveries may be legitimate. This filter would discard valid difficult cases and still leave many noisy labels untouched. It reduces dataset size without addressing the underlying noise mechanism, and it biases the model against unusual but real delivery conditions.
- ✗
Train a model on the noisy labels, then use its predictions to relabel the most confident examples and retrain.
Why it's wrong here
Self-training on confident predictions can reinforce the model's own biases and propagate systematic label errors, especially when noise is not random. Without an independent source of ground truth or a noise-robust loss, the model may simply memorize the same mistakes. This approach risks confirmation bias and does not reliably improve label quality for the delivery-time task.
- ✓
Model the label noise explicitly, for example with a noise-robust loss or a probabilistic noise model, and train with the noisy labels.
Why this is correct
Noise-robust losses and probabilistic noise models are designed to learn from corrupted labels by down-weighting or modeling the error process. They allow the team to retain all 120,000 records while reducing the influence of late manual entries. This directly targets the stated problem of noisy arrival-time labels without discarding data, making it the most appropriate and technically grounded solution.
- ✗
Apply a clustering algorithm to the delivery records and relabel each cluster with its centroid value.
Why it's wrong here
Clustering groups similar records but does not correct individual noisy labels; assigning each cluster's centroid as the label would replace genuine variation with an aggregate value and introduce systematic error. Delivery times legitimately differ by route, weather, and distance, so collapsing them into cluster centroids destroys valid signal. This method addresses grouping, not label noise.
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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 CompTIA exam blueprint
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