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 machine learning engineer is working on a customer churn prediction project. The dataset contains 100,000 records with 15 features, including customer demographics, account information, and usage patterns. The target variable 'churned' is binary with 15% positive examples. During EDA, the engineer notices that the feature 'tenure' (number of months the customer has been with the company) has a multimodal distribution with peaks at 1, 12, 24, and 36 months. Also, the feature 'monthly_charges' has a strong positive correlation with 'total_charges' (correlation coefficient = 0.95). The engineer wants to build a logistic regression model. Which preprocessing steps should the engineer take to address these issues? (Select TWO.)
Explanation: Binning the 'tenure' feature into categorical groups (e.g., 0-6, 7-12, 13-24, 25-36, 36+) captures the multimodal distribution and non-linear relationship with churn, which logistic regression (a linear model) cannot model directly. This transforms the feature into a format that allows the model to learn different churn probabilities for each tenure segment without imposing a linear assumption. Option B is correct because 'monthly_charges' and 'total_charges' have a very high positive correlation (0.95), leading to multicollinearity. Removing one of these features (e.g., 'total_charges') reduces multicollinearity, which improves the stability and interpretability of logistic regression coefficients.
A company has a dataset with a large number of missing values in several columns. The data scientist wants to impute missing values without introducing bias. Which approach should be used?
Explanation: Multiple Imputation by Chained Equations (MICE) models each variable with missing values as a function of other variables, reducing bias compared to simpler methods. Option A is wrong because removing rows with missing values leads to data loss and potential bias if missingness is not random. Option C is wrong because replacing with the mode ignores relationships between variables and can introduce bias. Option D is wrong because mean imputation reduces variance and can distort relationships.
Which TWO of the following are valid reasons to use a sample of the data during exploratory data analysis instead of the full dataset? (Select TWO.)
Explanation: Sampling is used during exploratory data analysis to reduce memory usage and computation time (Option D) and to enable interactive data visualization with large datasets (Option E). Option A is incorrect because sampling does not remove bias; it can introduce bias if not done properly. Option B is incorrect because sampling often misses rare events rather than ensuring they are captured. Option C is incorrect because reducing the amount of data typically reduces model accuracy, not improves it.
A company has customer feedback data stored in CSV files in S3. The data includes a 'feedback_text' column. Which AWS service is best suited for performing sentiment analysis as part of exploratory data analysis?
Explanation: Amazon Comprehend is a natural language processing (NLP) service that can perform sentiment analysis directly on text data. Option B is wrong because Amazon Rekognition is for image and video analysis, not text sentiment. Option C is wrong because Amazon Textract is for extracting text from documents, not analyzing sentiment. Option D is wrong because Amazon Lex is for building conversational interfaces (chatbots), not for sentiment analysis.
During EDA, a data scientist finds that a feature has a skewed distribution. They want to apply a log transformation to make it more Gaussian-like. Which Amazon SageMaker feature is most appropriate for this transformation?
Explanation: Amazon SageMaker Data Wrangler is the most appropriate feature for applying a log transformation to make a skewed distribution more Gaussian-like. It provides a visual interface that allows data scientists to easily apply transformations like log scaling without writing code. Other options are not suitable: SageMaker Ground Truth is for data labeling, SageMaker JumpStart provides pre-built models and solutions, and SageMaker Clarify is used for bias detection and model explainability.
+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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Difficulty is subjective, but Exploratory Data Analysis is a high-priority exam concept tested in multiple ways — direct recall, scenario analysis, and command-output interpretation. Consistent practice is the best way to build confidence.
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