The answer is label encoding, target encoding, and one-hot encoding. Label encoding directly maps ordinal categories like High School, Bachelor, Master, and PhD to ascending integers (0, 1, 2, 3), preserving the natural order that is critical for ordinal variables. Target encoding replaces each category with the mean of the continuous target variable, capturing the relationship between education level and the outcome without losing ordinality. One-hot encoding, while typically used for nominal data, remains a safe fallback when you want to avoid imposing an arbitrary order, though it expands feature space. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of encoding ordinal categorical variables and the trade-offs between preserving order, interpretability, and model performance. A common trap is assuming binary encoding works for ordinal data—it treats categories as independent bits, destroying the rank. Remember the mnemonic LOT: Label preserves Order, Target captures Trend, One-hot is a safe Option.
MLS-C01 Exploratory Data Analysis Practice Question
This MLS-C01 practice question tests your understanding of exploratory data analysis. 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 evaluating feature engineering options for a dataset containing a categorical variable 'education_level' with values: High School, Bachelor, Master, PhD. The target variable is continuous. Which THREE encoding methods are appropriate for this ordinal categorical variable? (Choose 3)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
One-hot encoding
Options A, B, and E are correct because label encoding preserves ordinality, target encoding captures the relationship with the target, and one-hot encoding is a safe fallback. Option C is wrong because binary encoding assumes nominal categories. Option D is wrong because hash encoding loses interpretability and may cause collisions.
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.
✓
One-hot encoding
Why this is correct
One-hot encoding is a safe option that does not assume any order, though it increases dimensionality.
Related concept
Read the scenario before looking for a memorised answer.
✓
Target encoding (mean of target per category)
Why this is correct
Target encoding can capture the effect of each category on the target.
Related concept
Read the scenario before looking for a memorised answer.
✗
Hash encoding (using feature hashing)
Why it's wrong here
Hash encoding is for high cardinality nominal variables and loses ordinal information.
✓
Label encoding (e.g., High School=0, Bachelor=1, Master=2, PhD=3)
Why this is correct
Label encoding preserves the ordinal relationship.
Related concept
Read the scenario before looking for a memorised answer.
✗
Binary encoding (convert to binary representation)
Why it's wrong here
Binary encoding is for nominal variables and does not preserve ordinality.
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 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.
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: One-hot encoding — Options A, B, and E are correct because label encoding preserves ordinality, target encoding captures the relationship with the target, and one-hot encoding is a safe fallback. Option C is wrong because binary encoding assumes nominal categories. Option D is wrong because hash encoding loses interpretability and may cause collisions.
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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Question Discussion
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