- A
Use target encoding to replace each ID with the average target per ID
Target encoding reduces cardinality to a single numeric column while capturing the relationship with the target.
- B
Drop the 'CustomerID' column entirely
Why wrong: Dropping may discard useful information; a better approach is to transform the feature.
- C
Apply ordinal encoding by assigning a unique integer to each ID
Why wrong: Ordinal encoding implies an order that does not exist, potentially misleading the model.
- D
Apply one-hot encoding to create a sparse binary representation
Why wrong: One-hot encoding with 10,000+ categories would create an extremely high-dimensional sparse matrix, leading to poor model performance and memory issues.
Question 212 of 1,000
mediumMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A data scientist is using Amazon SageMaker Data…
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 data scientist is using Amazon SageMaker Data Wrangler to prepare a dataset that contains a column 'CustomerID' with high cardinality (over 10,000 unique values). The column will be used as a feature in a model predicting customer churn. What is the recommended approach to handle this high-cardinality feature?
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
✓
Use target encoding to replace each ID with the average target per ID
Target encoding (also known as mean encoding) replaces each category with the mean of the target for that category. This is a common technique for high-cardinality features in supervised learning, though it requires careful handling to avoid data leakage.
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 target encoding to replace each ID with the average target per ID
Why this is correct
Target encoding reduces cardinality to a single numeric column while capturing the relationship with the target.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Drop the 'CustomerID' column entirely
Why it's wrong here
Dropping may discard useful information; a better approach is to transform the feature.
- ✗
Apply ordinal encoding by assigning a unique integer to each ID
Why it's wrong here
Ordinal encoding implies an order that does not exist, potentially misleading the model.
- ✗
Apply one-hot encoding to create a sparse binary representation
Why it's wrong here
One-hot encoding with 10,000+ categories would create an extremely high-dimensional sparse matrix, leading to poor model performance and memory issues.
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 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
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.
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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: Use target encoding to replace each ID with the average target per ID — Target encoding (also known as mean encoding) replaces each category with the mean of the target for that category. This is a common technique for high-cardinality features in supervised learning, though it requires careful handling to avoid data leakage.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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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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