20+ practice questions focused on Exploratory Data Analysis — one of the most tested topics on the AWS Certified Machine Learning Specialty MLS-C01 exam. Each question includes a detailed explanation so you learn why the right answer is correct.
Start Exploratory Data Analysis PracticeA data scientist is exploring a dataset of customer transactions. The dataset has 1 million rows and 50 columns. The target variable is a binary flag indicating whether a customer churned. The data scientist runs a correlation matrix on all numerical features and finds that two features have a correlation coefficient of 0.98. Which action should be taken to improve model performance?
Explanation: Two features with a correlation coefficient of 0.98 are nearly perfectly multicollinear. This inflates the variance of coefficient estimates in linear models, making them unstable and reducing interpretability. Removing one of the highly correlated features is a standard dimensionality reduction technique that mitigates multicollinearity without significant information loss, as the remaining feature captures almost the same variance.
A team is building a regression model to predict house prices. The dataset includes a column 'zip_code' with 100 unique values. The data scientist one-hot encodes this column, resulting in 100 new binary columns. The model shows poor performance on a validation set. What is the most likely cause?
Explanation: One-hot encoding 'zip_code' with 100 unique values creates 100 binary features. With only 100 features, the dataset is not high-dimensional enough to cause the curse of dimensionality, which typically requires thousands of features. The poor performance is more likely due to other issues like overfitting or data leakage, not the curse of dimensionality. Option D is incorrect because the curse of dimensionality is not the most likely cause in this scenario.
During exploratory data analysis, a data scientist plots the distribution of a numerical feature and observes a heavy right skew. The feature has many outliers at the high end. Which transformation is most appropriate to reduce skewness?
Explanation: A log transformation compresses the range of the data, reducing the impact of extreme values and pulling in the long tail of a right-skewed distribution. This makes the feature more normally distributed, which is often required for linear models and many statistical tests. It is the standard technique for handling positive-valued features with heavy right skew.
A data scientist is analyzing a dataset with missing values in 30% of the rows for the 'age' column. The data scientist decides to impute the missing values with the median of the observed 'age' values. What is a potential drawback of this approach?
Explanation: Imputing missing values with the median of the observed data artificially concentrates imputed values around the center of the distribution. This reduces the overall variance of the 'age' column because the imputed values do not reflect the natural spread of the data, potentially distorting downstream analyses like regression or clustering that rely on variance structure.
A data scientist is exploring a dataset with 500 features and 10,000 samples. The data scientist computes the pairwise correlation matrix and finds that many features have correlations above 0.9. The data scientist wants to reduce the dataset to 50 features while preserving as much variance as possible. Which technique should be used?
Explanation: Principal Component Analysis (PCA) is the correct technique because it performs an orthogonal linear transformation that projects the original 500 features into a new coordinate system where the axes (principal components) are ordered by the variance they capture. By keeping the top 50 principal components, the data scientist retains the maximum possible variance in the reduced 50-dimensional space, directly addressing the goal of preserving variance while handling high multicollinearity.
+15 more Exploratory Data Analysis questions available
Practice all Exploratory Data Analysis questions1. Baseline your knowledge
Start with 10 questions to gauge your current understanding of Exploratory Data Analysis. This tells you whether you need a concept refresher or just practice.
2. Review every explanation
For each question — right or wrong — read the full explanation. Understanding why an answer is correct is more valuable than knowing the answer itself.
3. Focus on exam traps
Exploratory Data Analysis questions on the MLS-C01 frequently use trap wording. Look for subtle differences in answers that test your precision, not just general knowledge.
4. Reach 80% consistently
Do repeated sessions until you score 80%+ three times in a row. Then move to mixed-mode practice to test cross-topic recall under realistic conditions.
The exact number varies per candidate. Exploratory Data Analysis is tested as part of the AWS Certified Machine Learning Specialty MLS-C01 blueprint. Practicing with targeted Exploratory Data Analysis questions ensures you can handle any format or difficulty that appears.
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