Quick Answer
The answer is Recall@k, Precision@k, and Mean Average Precision@k (MAP@k). These three evaluation metrics are appropriate for implicit feedback recommender systems because they focus on ranking quality rather than prediction accuracy, which is essential when only positive interactions like clicks are observed and negative feedback is absent. Recall@k measures how many relevant items are captured in the top-k recommendations, Precision@k evaluates the proportion of recommended items that are relevant, and MAP@k averages precision across multiple recall levels to assess overall ranking effectiveness. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this topic tests your understanding that implicit feedback requires ranking metrics, not regression metrics like RMSE or MAE, which are used for explicit ratings. A common trap is choosing metrics like AUC-ROC, which are less meaningful when negative samples are artificially generated. Memory tip: think “Rank, don’t rate” — for clicks, you care about the order of the top-k, not the exact score.
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.
A data scientist is building a recommender system using Amazon SageMaker. The dataset contains user-item interactions with implicit feedback (clicks). Which THREE evaluation metrics are appropriate for this use case?
Root Mean Squared Error (RMSE)
Why wrong: RMSE is for explicit ratings, not implicit feedback.
Precision@k
Precision@k measures relevance of top-k recommendations.
Mean Average Precision (MAP)
MAP summarizes precision across different recall levels.
Recall@k
Recall@k measures coverage of relevant items in top-k.
Area Under the ROC Curve (AUC-ROC)
Why wrong: AUC-ROC is for binary classification, not ranking.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Precision@k
Precision@k is appropriate for implicit feedback (clicks) because it measures the proportion of relevant items among the top-k recommendations, focusing on the accuracy of the ranked list. In recommender systems with implicit feedback, where only positive interactions are observed, ranking metrics like Precision@k are standard as they evaluate the quality of the top recommendations without requiring explicit ratings.
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.
Root Mean Squared Error (RMSE)
Why it's wrong here
RMSE is for explicit ratings, not implicit feedback.
Precision@k
Why this is correct
Precision@k measures relevance of top-k recommendations.
Related concept
Read the scenario before looking for a memorised answer.
Mean Average Precision (MAP)
Why this is correct
MAP summarizes precision across different recall levels.
Related concept
Read the scenario before looking for a memorised answer.
Recall@k
Why this is correct
Recall@k measures coverage of relevant items in top-k.
Related concept
Read the scenario before looking for a memorised answer.
Area Under the ROC Curve (AUC-ROC)
Why it's wrong here
AUC-ROC is for binary classification, not ranking.
Common exam traps
The trap here is that candidates often confuse regression metrics (RMSE) or binary classification metrics (AUC-ROC) as applicable to implicit feedback, not realizing that recommender systems with implicit feedback require ranking-based metrics that handle only positive observations and no explicit negative labels.
Detailed technical explanation
Precision@k, Recall@k, and MAP are ranking metrics that evaluate the relevance of the top-k recommendations, which is critical for implicit feedback scenarios where the goal is to predict which items a user will interact with. MAP averages precision across multiple recall levels, providing a single-figure measure of ranking quality, while Recall@k measures the fraction of relevant items retrieved in the top-k. In Amazon SageMaker, these metrics are commonly used with factorization machines or neural collaborative filtering models to tune hyperparameters and select the best model for click-based recommendation tasks.
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: Precision@k — Precision@k is appropriate for implicit feedback (clicks) because it measures the proportion of relevant items among the top-k recommendations, focusing on the accuracy of the ranked list. In recommender systems with implicit feedback, where only positive interactions are observed, ranking metrics like Precision@k are standard as they evaluate the quality of the top recommendations without requiring explicit ratings.
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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