This MLS-C01 practice question tests your understanding of modeling. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Refer to the exhibit.
```
{
"predicted_label": "spam",
"predicted_probability": 0.95,
"label": "ham"
}
```Refer to the exhibit. A data scientist is evaluating a binary classification model for spam detection. The exhibit shows a single prediction instance. What is the model's prediction for this instance?
Refer to the exhibit.
```
{
"predicted_label": "spam",
"predicted_probability": 0.95,
"label": "ham"
}
```Ham
Why wrong: 'Ham' is the actual label, not the prediction.
0.95
Why wrong: 0.95 is the probability, not the predicted label.
The model is unsure because probability is not 1.0
Why wrong: The model still outputs a predicted label.
Spam
The 'predicted_label' is 'spam'.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Spam
The model's prediction is 'Spam' because the prediction instance shows a probability of 0.95 for the 'Spam' class, which exceeds the typical decision threshold of 0.5 used in binary classification. Since the probability for 'Spam' is higher than for 'Ham' (0.05), the model assigns the instance to the class with the highest probability, which is Spam.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Ham
Why it's wrong here
'Ham' is the actual label, not the prediction.
0.95
Why it's wrong here
0.95 is the probability, not the predicted label.
The model is unsure because probability is not 1.0
Why it's wrong here
The model still outputs a predicted label.
Spam
Why this is correct
The 'predicted_label' is 'spam'.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Cisco often tests the distinction between a model's probability output and its final class prediction, leading candidates to mistakenly select the probability value (0.95) as the prediction instead of the class label (Spam).
Trap categories for this question
Command / output trap
The model still outputs a predicted label.
Detailed technical explanation
In binary classification models like logistic regression or neural networks, the output layer typically uses a sigmoid activation function to produce a probability between 0 and 1 for the positive class. The decision threshold is a hyperparameter that can be tuned (e.g., to 0.5 by default), but the model's prediction is always the class whose probability exceeds the threshold; a probability of 0.95 is well above 0.5, so the prediction is Spam. In real-world spam detection, thresholds are often adjusted to balance precision and recall, but the question assumes the default threshold of 0.5.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Spam — The model's prediction is 'Spam' because the prediction instance shows a probability of 0.95 for the 'Spam' class, which exceeds the typical decision threshold of 0.5 used in binary classification. Since the probability for 'Spam' is higher than for 'Ham' (0.05), the model assigns the instance to the class with the highest probability, which is Spam.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.
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