MLS-C01 Exploratory Data Analysis Practice Question
Which THREE of the following are common issues that can be identified during exploratory data analysis? (Select THREE.)
⚠ Common exam trap
The MLS-C01 exam often tests the boundary between data-level issues (EDA) and model training issues, so candidates mistakenly select gradient vanishing (a deep learning optimization problem) or API latency (an operational concern) as EDA findings.
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
✓
Multicollinearity between features
Multicollinearity occurs when two or more features in a dataset are highly correlated, meaning they contain redundant information. During exploratory data analysis (EDA), correlation matrices and variance inflation factor (VIF) calculations can reveal this issue, which can destabilize linear regression models and inflate coefficient standard errors.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Multicollinearity between features
Why this is correct
High correlation between features can be detected via correlation matrix.
- ✗
High latency in API endpoints
Why it's wrong here
Latency is a performance metric, not a data characteristic.
- ✗
Gradient vanishing in neural networks
Why it's wrong here
Gradient vanishing is a training issue, not a data issue.
- ✓
Class imbalance in the target variable
Why this is correct
Imbalanced classes are identified by examining target distribution.
- ✓
Missing values in features
Why this is correct
Missing data is a common data quality issue detected during EDA.
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