- A
Binary encoding
Why wrong: Binary encoding reduces dimensionality but reduces interpretability.
- B
Target encoding
Why wrong: Target encoding can leak target information and reduce interpretability.
- C
Label encoding
Why wrong: Label encoding imposes an ordinal relationship, inappropriate for nominal categories.
- D
One-hot encoding with dropping the first category
Avoids multicollinearity and keeps interpretability.
Question 261 of 1,000
mediumMultiple ChoiceObjective-mapped
Dummy Variable Trap in One-Hot Encoding for Linear Regression
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 working with a dataset containing a categorical feature 'country' with 200 unique values. They plan to use a linear regression model. Which encoding method is most suitable to avoid the dummy variable trap while maintaining interpretability?
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
✓
One-hot encoding with dropping the first category
One-hot encoding with dropping the first category is the most suitable method because it creates binary columns for each category except one, avoiding perfect multicollinearity (the dummy variable trap) while preserving interpretability. Each coefficient directly represents the effect of that category relative to the dropped reference category, which is intuitive for linear regression.
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.
- ✗
Binary encoding
Why it's wrong here
Binary encoding reduces dimensionality but reduces interpretability.
- ✗
Target encoding
Why it's wrong here
Target encoding can leak target information and reduce interpretability.
- ✗
Label encoding
Why it's wrong here
Label encoding imposes an ordinal relationship, inappropriate for nominal categories.
- ✓
One-hot encoding with dropping the first category
Why this is correct
Avoids multicollinearity and keeps interpretability.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates often mistakenly think one-hot encoding must include all categories, overlooking the dummy variable trap, or assume label encoding is acceptable for nominal data in linear models due to its simplicity.
Detailed technical explanation
How to think about this question
The dummy variable trap occurs when one-hot encoding creates a linear dependency among the dummy columns (e.g., the sum of all dummies equals the intercept column), causing the design matrix to be singular and preventing ordinary least squares from computing unique coefficients. Dropping the first category breaks this dependency, and the reference category becomes the baseline against which all other category effects are measured. In practice, this encoding is standard for linear models where interpretability of category-level effects is required, such as in econometric or clinical regression analyses.
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
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 exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Related practice questions
Related MLA-C01 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
ML Model Development practice questions
Practise MLA-C01 questions linked to ML Model Development.
Data Preparation for Machine Learning practice questions
Practise MLA-C01 questions linked to Data Preparation for Machine Learning.
Deployment and Orchestration of ML Workflows practice questions
Practise MLA-C01 questions linked to Deployment and Orchestration of ML Workflows.
ML Solution Monitoring, Maintenance, and Security practice questions
Practise MLA-C01 questions linked to ML Solution Monitoring, Maintenance, and Security.
ML Solution Monitoring, Maintenance and Security practice questions
Practise MLA-C01 questions linked to ML Solution Monitoring, Maintenance and Security.
MLA-C01 fundamentals practice questions
Practise MLA-C01 questions linked to MLA-C01 fundamentals.
MLA-C01 scenario practice questions
Practise MLA-C01 questions linked to MLA-C01 scenario.
MLA-C01 troubleshooting practice questions
Practise MLA-C01 questions linked to MLA-C01 troubleshooting.
Practice this exam
Start a free MLA-C01 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
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: One-hot encoding with dropping the first category — One-hot encoding with dropping the first category is the most suitable method because it creates binary columns for each category except one, avoiding perfect multicollinearity (the dummy variable trap) while preserving interpretability. Each coefficient directly represents the effect of that category relative to the dropped reference category, which is intuitive for linear regression.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
1 more ways this is tested on MLA-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data scientist needs to encode a categorical feature with 6 distinct values for a linear regression model. The feature has no ordinal relationship. Which encoding method should they use?
easy- A.Ordinal encoding
- B.Label encoding
- ✓ C.One-hot encoding
- D.Target encoding
Why C: One-hot encoding is the correct choice because it creates binary columns for each of the 6 distinct values, avoiding the implication of any ordinal relationship. This prevents the linear regression model from incorrectly interpreting numerical order as meaningful, which would introduce bias. The feature has no ordinal relationship, so one-hot encoding ensures each category is treated as an independent predictor.
Keep practising
More MLA-C01 practice questions
- A team is using SageMaker Pipelines to train a model. The pipeline has multiple steps: data processing, training, evalua…
- A machine learning team deploys a custom container image for an Amazon SageMaker training job. The container needs to ac…
- A machine learning engineer sees the above error in Amazon CloudWatch Logs for a SageMaker endpoint. What is the most li…
- A data scientist has trained a model that achieves 95% accuracy on the training set but only 70% on the test set. Which…
- Refer to the exhibit. A data scientist reviews the output of a SageMaker training job. The model has 95% training accura…
- A team is using Amazon SageMaker to train a neural network. They want to minimize training time while effectively explor…
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.