- A
The model outputs are not being ranked correctly.
Why wrong: Ranking error would likely produce some correct recommendations, not zero consistently.
- B
The model is overfitting.
Why wrong: Overfitting would yield high training metrics but still some precision on test.
- C
The test set contains only positive interactions.
Correct: Without negative examples, precision is undefined or zero if no test items are in the recommendation list.
- D
The k value is too large.
Why wrong: Larger k increases the chance of including test items, making zero less likely.
Question 728 of 1,000
easyMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: Building a recommendation system and has trained…
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. 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.
A company is building a recommendation system and has trained a matrix factorization model using SageMaker. They want to evaluate the model's performance using precision at k (P@k) and recall at k (R@k). They have a test set of user-item interactions. The data scientist implements a custom evaluation script that computes these metrics, but the precision values are consistently zero. What is the most likely cause?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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 test set contains only positive interactions.
Option C is correct because if the test set contains only positive interactions (i.e., every user-item pair in the test set is a ground-truth positive), then precision at k will be zero unless the model recommends exactly those items. Since the model's top-k recommendations are unlikely to perfectly match the test set's positive items for every user, precision (the fraction of recommended items that are relevant) will be zero. This is a known pitfall when evaluating implicit feedback models without negative samples.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 model outputs are not being ranked correctly.
Why it's wrong here
Ranking error would likely produce some correct recommendations, not zero consistently.
- ✗
The model is overfitting.
Why it's wrong here
Overfitting would yield high training metrics but still some precision on test.
- ✓
The test set contains only positive interactions.
Why this is correct
Correct: Without negative examples, precision is undefined or zero if no test items are in the recommendation list.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
The k value is too large.
Why it's wrong here
Larger k increases the chance of including test items, making zero less likely.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume precision at k can be computed directly from a test set of positive interactions, overlooking that without negative labels, the metric becomes meaningless because the denominator (k) will always yield zero unless the model's top-k exactly matches the test positives.
Detailed technical explanation
How to think about this question
In matrix factorization for implicit feedback, the test set often consists only of positive interactions (e.g., clicks, purchases) because negative interactions are unobserved. Precision at k is defined as the number of relevant items in the top-k recommendations divided by k. If the test set has no negative examples, the model cannot be evaluated for precision in the standard way because there is no way to know if a non-recommended item is truly irrelevant. A common workaround is to use ranking metrics like mean average precision (MAP) or to sample negative items for evaluation.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: The test set contains only positive interactions. — Option C is correct because if the test set contains only positive interactions (i.e., every user-item pair in the test set is a ground-truth positive), then precision at k will be zero unless the model recommends exactly those items. Since the model's top-k recommendations are unlikely to perfectly match the test set's positive items for every user, precision (the fraction of recommended items that are relevant) will be zero. This is a known pitfall when evaluating implicit feedback models without negative samples.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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