AIF-C01 Fundamentals of AI and ML Practice Question

A logistics company wants to use machine learning to predict delivery times. A data scientist is preparing the project and must identify which characteristics describe supervised learning rather than unsupervised learning. (Choose two.)

⚠ Common exam trap

Watch out — candidates often confuse clustering-style structure discovery with supervised prediction because both can operate on the same features.

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 goal is to learn a mapping from input features to a known output so the model can predict on new data.

Supervised learning is defined by training on labeled examples and learning a mapping from inputs to a known output so the model can predict on new data. In the delivery-time scenario, historical trips with actual durations provide those labels. The remaining characteristics describe unsupervised learning, which discovers structure or groups without predefined outputs or ground truth.

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 training process requires no historical outcomes and relies only on feature similarity.
Why it's wrong here
Relying only on feature similarity without historical outcomes is characteristic of unsupervised techniques such as nearest-neighbor clustering. Supervised learning depends on historical outcomes to learn the mapping. For delivery-time prediction, ignoring past actual durations would remove the signal needed to train a predictive model.
✓
The goal is to learn a mapping from input features to a known output so the model can predict on new data.
Why this is correct
Supervised learning explicitly learns a function that maps input features to a known output, enabling predictions on unseen examples. For delivery times, the model learns from features such as distance, traffic, and time of day to predict duration for new orders, which is the core objective of supervised learning.
✗
The algorithm discovers hidden structure in data without any predefined output values.
Why it's wrong here
Discovering hidden structure without predefined outputs describes unsupervised learning, such as clustering or anomaly detection. In the delivery-time scenario, this would mean grouping similar routes or flagging unusual trips rather than learning to predict a numeric duration. This characteristic does not describe supervised learning.
✓
The training dataset includes a target label for each example, such as actual delivery duration.
Why this is correct
Supervised learning trains on examples where the desired output is known, so a labeled target such as actual delivery duration lets the algorithm learn a mapping from inputs to that outcome. This is the defining characteristic of supervised learning and directly supports predicting delivery times from historical completed deliveries.
✗
The model is evaluated primarily by how well it separates data into groups with no ground truth.
Why it's wrong here
Evaluating separation of groups without ground truth is an unsupervised clustering concern, often measured with silhouette or Davies-Bouldin scores. Supervised learning is instead evaluated against known labels using metrics such as mean absolute error for regression. This option describes unsupervised evaluation, not supervised learning.

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AIF-C01 Fundamentals of AI and ML Practice Question

Why each option matters

Option-by-option breakdown

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