The answer is that the test set contains only positive interactions, which is the most likely cause of precision at k being zero in SageMaker recommendation models. Precision at k measures the proportion of recommended items among the top k that are relevant, but if the test set lacks any negative examples—meaning every user-item pair is a positive interaction—then the metric has no baseline for irrelevant items. When the model’s top k recommendations do not exactly match the sparse set of positive test interactions, precision drops to zero because no recommended item is counted as a “hit.” On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your understanding of evaluation data construction for recommendation systems; a common trap is assuming the model is broken rather than recognizing that precision at k requires both positive and negative labels to be meaningful. Remember the mnemonic “No negatives, no precision”—if your test set is all positives, precision at k will always be zero unless you get a perfect match.
MLA-C01 ML Model Development Practice Question
This MLA-C01 practice question tests your understanding of ml model development. 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 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 (items the user interacted with), then there are no negative examples. In precision at k, if the recommended items do not exactly match the test set items (which is likely), precision will be zero. Options A and B are incorrect because ranking or k value would not cause consistent zero unless no overlap. Option D is incorrect because overfitting would cause high training accuracy, not zero precision.
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
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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 MLA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
ML Model Development — This question tests ML Model Development — 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 (items the user interacted with), then there are no negative examples. In precision at k, if the recommended items do not exactly match the test set items (which is likely), precision will be zero. Options A and B are incorrect because ranking or k value would not cause consistent zero unless no overlap. Option D is incorrect because overfitting would cause high training accuracy, not zero precision.
What should I do if I get this MLA-C01 question wrong?
Identify which MLA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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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Question Discussion
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