The answer is to normalize or standardize numerical features and apply one-hot encoding to categorical variables. These two preprocessing steps for linear regression are critical because the model assumes all features are numerical and equally scaled; without scaling, a feature like square footage with large numeric values can disproportionately influence the coefficients, while categorical variables like neighborhood must be converted into binary dummy columns to avoid implying an ordinal relationship. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your understanding of linear regression’s sensitivity to feature magnitude and data type, often appearing in scenario-based questions where a candidate might overlook encoding or scaling. A common trap is assuming tree-based models need the same treatment—they do not—so remember that linear regression requires both steps. Memory tip: “Scale the numbers, encode the names” to keep the two preprocessing steps straight.
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 building a regression model to predict housing prices. The dataset includes numerical features such as square footage, number of bedrooms, and year built, as well as categorical features such as neighborhood and roof type. Which TWO preprocessing steps are most important to apply before training a linear regression model?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
One-hot encode categorical features
Linear regression requires numerical features and is sensitive to feature scales. Encoding categorical variables as numerical is necessary, and scaling numerical features ensures that no single feature dominates the model.
Key principle: OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Apply principal component analysis (PCA) for dimensionality reduction
Why it's wrong here
PCA is not typically necessary and may remove interpretability; it's not a required preprocessing step.
✓
One-hot encode categorical features
Why this is correct
One-hot encoding converts categorical variables into numerical form suitable for linear regression.
Common exam trap: OSPF can fail even when IP connectivity looks correct
OSPF neighbour formation depends on matching areas, timers, network type, authentication and passive-interface behaviour. Do not choose an answer only because the devices can ping.
Detailed technical explanation
How to think about this question
OSPF questions usually test the details that control adjacency and route selection. Read the neighbour state, area, router ID and interface configuration before deciding what is wrong.
KKey Concepts to Remember
OSPF neighbours must agree on key parameters.
Router ID selection can affect neighbour relationships and LSDB output.
OSPF cost influences the preferred path.
A route can appear in OSPF information but not become the installed route.
TExam Day Tips
→Check area mismatch first when OSPF adjacency fails.
→Review passive interfaces when a network is advertised but no neighbour forms.
→Use show ip ospf neighbor and show ip route clues carefully.
Key takeaway
OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough.
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.
Review OSPF neighbour requirements — matching area type, hello and dead timers, network type, stub flags, and authentication. Study show ip ospf neighbor states (INIT, 2-WAY, FULL). Then practise related MLS-C01 OSPF questions on adjacency and route selection.
Modeling — This question tests Modeling — OSPF neighbours must agree on key parameters..
What is the correct answer to this question?
The correct answer is: One-hot encode categorical features — Linear regression requires numerical features and is sensitive to feature scales. Encoding categorical variables as numerical is necessary, and scaling numerical features ensures that no single feature dominates the model.
What should I do if I get this MLS-C01 question wrong?
Review OSPF neighbour requirements — matching area type, hello and dead timers, network type, stub flags, and authentication. Study show ip ospf neighbor states (INIT, 2-WAY, FULL). Then practise related MLS-C01 OSPF questions on adjacency and route selection.
What is the key concept behind this question?
OSPF neighbours must agree on key parameters.
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Question Discussion
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