The answer is mutual information-based feature selection and L1-regularized logistic regression. These two methods are appropriate for supervised dimensionality reduction with a binary target because they directly leverage the target variable to select or weight features, preserving predictive information while reducing the feature space. Mutual information quantifies the dependency between each feature and the binary target, ranking features by their relevance, while L1-regularized logistic regression applies a penalty that drives irrelevant feature coefficients to zero, effectively performing feature selection. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your ability to distinguish supervised from unsupervised techniques in dimensionality reduction—a common trap is choosing PCA or autoencoders, which are unsupervised and may discard target-related variance. Remember: when the target is binary and you need to reduce dimensions while keeping target information, always look for methods that use the target label, like mutual information or L1 regularization. A helpful memory tip is “Label-Lasso” for supervised selection.
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 performing EDA on a dataset with 1,000 features and 10,000 rows. The target is binary. The scientist wants to reduce dimensionality while preserving information related to the target. Which TWO methods are appropriate?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
L1-regularized logistic regression
Options A and D are correct. Mutual information selection selects features with highest dependency on target, and L1-regularized logistic regression can drive coefficients to zero for feature selection. Option B is wrong because PCA is unsupervised and may discard target-related variance. Option C is wrong because t-SNE is for visualization only. Option E is wrong because Autoencoders are unsupervised.
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.
✗
Principal Component Analysis (PCA)
Why it's wrong here
Unsupervised; may lose target information.
✗
Autoencoders
Why it's wrong here
Unsupervised; not directly target-aware.
✓
L1-regularized logistic regression
Why this is correct
Can perform feature selection by shrinking coefficients to zero.
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: L1-regularized logistic regression — Options A and D are correct. Mutual information selection selects features with highest dependency on target, and L1-regularized logistic regression can drive coefficients to zero for feature selection. Option B is wrong because PCA is unsupervised and may discard target-related variance. Option C is wrong because t-SNE is for visualization only. Option E is wrong because Autoencoders are unsupervised.
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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