The correct answer is to cap 'SquareFootage' at the 99th percentile value. This approach, known as robust outlier treatment by capping at percentiles, is the most effective because it limits the influence of extreme values—likely data entry errors like 50,000 sq ft—without discarding the entire row, preserving the dataset’s size and structure. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this tests your understanding of data preprocessing for regression models, where the goal is to handle outliers while maintaining data integrity; a common trap is choosing to remove rows with any outlier, which can discard valuable information, or using log transformation, which doesn’t fix erroneous values. The key insight is that capping is a robust technique because it sets a hard boundary at a high percentile, neutralizing extreme noise while keeping the rest of the distribution intact. Remember the mnemonic “Cap, Don’t Chop” to recall that capping preserves data points, unlike deletion or imputation.
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 team is building a model to predict house prices. They have a dataset with features like 'SquareFootage', 'Bedrooms', 'YearBuilt', and 'Neighborhood'. They notice that 'SquareFootage' has a few extreme values (e.g., 50,000 sq ft) that are likely data entry errors. They want to handle these outliers without losing all the data. Which of the following approaches is most robust?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Cap 'SquareFootage' at the 99th percentile value.
Option B is correct because capping at percentiles (e.g., 99th) limits extreme values while keeping the data points. Option A is wrong because removing rows with any outlier may discard useful data. Option C is wrong because log transformation does not fix errors. Option D is wrong because imputing with mean distorts the distribution.
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.
✓
Cap 'SquareFootage' at the 99th percentile value.
Why this is correct
Capping limits extremes while retaining the records.
Replace extreme values with the mean of 'SquareFootage'.
Why it's wrong here
Mean imputation can distort the distribution and is not robust.
✗
Apply log transformation to 'SquareFootage'.
Why it's wrong here
Log transformation reduces skew but does not fix erroneous extreme values.
✗
Remove rows where 'SquareFootage' is above 3 standard deviations from the mean.
Why it's wrong here
This removes data points that could be legitimate large houses.
Common exam traps
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
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. OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough. 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.
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.
Exploratory Data Analysis — This question tests Exploratory Data Analysis — OSPF neighbours must agree on key parameters..
What is the correct answer to this question?
The correct answer is: Cap 'SquareFootage' at the 99th percentile value. — Option B is correct because capping at percentiles (e.g., 99th) limits extreme values while keeping the data points. Option A is wrong because removing rows with any outlier may discard useful data. Option C is wrong because log transformation does not fix errors. Option D is wrong because imputing with mean distorts the distribution.
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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