The answer is feature hashing, which is the correct choice because it maps a high cardinality categorical feature like UserID with over 1 million unique values into a fixed number of hash buckets, drastically reducing dimensionality and thus cutting both training time and model size while preserving predictive signal. This technique directly addresses the core challenge of high cardinality categorical feature hashing in SageMaker, where one-hot encoding would explode the feature space and label encoding would impose false ordinal relationships. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of feature engineering trade-offs for tree-based models, with a common trap being to choose one-hot encoding or to drop the feature entirely. Remember the memory tip: when cardinality is sky-high, hash it to keep it lean—think “hash to stash” the dimensionality explosion.
MLS-C01 Modeling Practice Question
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 using Amazon SageMaker to train a gradient boosting model on a dataset with categorical features. The dataset contains a column 'UserID' with over 1 million unique values. The training is taking very long and the model size is large. Which technique would MOST effectively reduce training time and model size while maintaining accuracy?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Apply feature hashing to UserID.
Option B is correct because hashing reduces the number of distinct categories to a fixed number of buckets, controlling dimensionality. Option A is wrong because one-hot encoding would explode the feature space. Option C is wrong because removing UserID likely loses important signal. Option D is wrong because label encoding creates ordinal relationships that may mislead the model.
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.
✗
Use one-hot encoding on UserID.
Why it's wrong here
One-hot encoding would create over 1 million binary columns, increasing training time and model size dramatically.
✓
Apply feature hashing to UserID.
Why this is correct
Feature hashing maps user IDs to a fixed number of buckets (e.g., 2^14), reducing dimensionality and preserving some signal.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use label encoding for UserID.
Why it's wrong here
Label encoding assigns integers 0 to N-1, which the tree model may misinterpret as ordinal, causing splits that are not meaningful.
✗
Remove UserID from the dataset.
Why it's wrong here
Removing UserID may discard important user-specific patterns, likely reducing accuracy.
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
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Identify which MLS-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.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Apply feature hashing to UserID. — Option B is correct because hashing reduces the number of distinct categories to a fixed number of buckets, controlling dimensionality. Option A is wrong because one-hot encoding would explode the feature space. Option C is wrong because removing UserID likely loses important signal. Option D is wrong because label encoding creates ordinal relationships that may mislead the model.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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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.
Question Discussion
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