- A
Convert user ID and item ID to integer indices for matrix factorization
Matrix factorization algorithms (e.g., in SageMaker's built-in Factorization Machines) require user and item IDs as integers.
- B
Use target encoding on user ID based on average rating
Why wrong: Target encoding can cause data leakage in recommendation contexts and is not typical for matrix factorization.
- C
Normalize ratings using StandardScaler
Why wrong: Normalizing ratings is not standard and may not improve performance; the model can learn the scale naturally.
- D
One-hot encode user ID and item ID
Why wrong: One-hot encoding user and item IDs leads to extremely high-dimensional sparse features; matrix factorization expects integer indices.
- E
Sort the data by timestamp and use a time-based split for training and validation
Time-based split ensures that the model does not use future interactions to predict past ones, which is critical for time-series recommendation.
MLA-C01 Practice Question: Building a recommender system using implicit…
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 recommender system using implicit feedback (clicks) and explicit feedback (ratings). They plan to use Amazon SageMaker to train a model. The data includes user ID, item ID, timestamp, and rating (if any). Which TWO data preparation steps should the team perform? (Choose TWO.)
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
Convert user ID and item ID to integer indices for matrix factorization
Option A is correct because matrix factorization algorithms in Amazon SageMaker (e.g., the built-in Factorization Machines algorithm or the Apache Spark-based collaborative filtering) require user and item identifiers to be converted to contiguous integer indices starting from 0. This is necessary for efficient embedding lookup and to avoid memory blowup from sparse categorical features. SageMaker's implementation expects the input data in recordIO-wrapped protobuf format with integer-encoded user and item columns.
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.
- ✓
Convert user ID and item ID to integer indices for matrix factorization
Why this is correct
Matrix factorization algorithms (e.g., in SageMaker's built-in Factorization Machines) require user and item IDs as integers.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use target encoding on user ID based on average rating
Why it's wrong here
Target encoding can cause data leakage in recommendation contexts and is not typical for matrix factorization.
- ✗
Normalize ratings using StandardScaler
Why it's wrong here
Normalizing ratings is not standard and may not improve performance; the model can learn the scale naturally.
- ✗
One-hot encode user ID and item ID
Why it's wrong here
One-hot encoding user and item IDs leads to extremely high-dimensional sparse features; matrix factorization expects integer indices.
- ✓
Sort the data by timestamp and use a time-based split for training and validation
Why this is correct
Time-based split ensures that the model does not use future interactions to predict past ones, which is critical for time-series recommendation.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse one-hot encoding (which is common in linear models) with the integer indexing required for embedding-based models like matrix factorization, leading them to select Option D instead of Option A.
Detailed technical explanation
How to think about this question
Matrix factorization decomposes the user-item interaction matrix into two low-rank matrices (user factors and item factors). Converting IDs to integer indices allows the model to map each ID to a dense embedding vector via an embedding layer, which is far more memory-efficient than one-hot encoding. In SageMaker's Factorization Machines algorithm, the data must be in protobuf format with integer indices; the algorithm then learns latent factors that capture patterns like user preferences and item similarities. A subtle behavior: if IDs are not zero-indexed and contiguous, the embedding layer may waste memory on unused indices or cause out-of-bounds errors.
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.
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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: Convert user ID and item ID to integer indices for matrix factorization — Option A is correct because matrix factorization algorithms in Amazon SageMaker (e.g., the built-in Factorization Machines algorithm or the Apache Spark-based collaborative filtering) require user and item identifiers to be converted to contiguous integer indices starting from 0. This is necessary for efficient embedding lookup and to avoid memory blowup from sparse categorical features. SageMaker's implementation expects the input data in recordIO-wrapped protobuf format with integer-encoded user and item columns.
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.
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
This MLA-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 MLA-C01 exam.
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