MLS-C01 Exploratory Data Analysis Practice Test 15 — 15 Questions

Question 1

A data analyst is using Amazon SageMaker Studio to perform exploratory data analysis on a dataset stored in S3. The analyst wants to generate summary statistics and visualizations quickly. Which built-in feature of SageMaker Studio should the analyst use?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

SageMaker Data Wrangler. SageMaker Data Wrangler is the built-in Studio feature for exploratory data analysis, offering 300+ prebuilt transforms, quick summary statistics, histograms, scatter plots, and a visual data flow — all without writing code. It imports directly from S3, Athena, Redshift, and Snowflake, making it the correct tool for fast EDA on an S3 dataset. The other services serve labeling, AutoML, and bias detection, not exploratory visualization.

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SageMaker Ground Truth

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SageMaker Autopilot

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SageMaker Clarify

Question 2

A data scientist is working with a dataset containing geospatial coordinates (latitude and longitude) of customer locations. The scientist wants to engineer features such as distance to the nearest store, and cluster customers into regions. Which AWS service is best suited for performing geospatial analysis and clustering during exploratory data analysis?

Accepted Answer

Amazon SageMaker with custom Python scripts using scikit-learn and Geopy. Amazon SageMaker with custom Python scripts using scikit-learn and Geopy is the best fit because it gives the data scientist full programmatic control to compute distances (Geopy) and run clustering algorithms like K-Means or DBSCAN (scikit-learn) during exploratory data analysis. SageMaker provides the managed notebook and compute environment, while the libraries handle the geospatial math and clustering. This is the most flexible option for custom feature engineering.

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Amazon Athena with PostGIS extensions

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AWS Glue with geospatial transforms

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Amazon Location Service

Question 3

A machine learning engineer is analyzing a dataset with a mix of categorical and numerical features. The engineer wants to understand the correlation between categorical features and the target variable. Which statistical test is most appropriate for measuring association between a categorical feature and a binary target?

Accepted Answer

Chi-squared test of independence. The chi-squared test of independence is the standard statistical test for measuring association between two categorical variables, including a categorical feature and a binary target. It compares observed frequencies in a contingency table against expected frequencies under the null hypothesis of independence. A significant result indicates the feature and target are associated.

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Pearson correlation coefficient

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ANOVA (Analysis of Variance)

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Mutual information

Question 4

A data analyst is exploring a dataset and wants to identify outliers in a numerical feature. Which visualization technique is most effective for detecting outliers?

Accepted Answer

Box plot. A box plot is specifically designed to visualize the distribution of a numerical feature and highlight outliers using the interquartile range (IQR) rule — points beyond 1.5×IQR from the quartiles are plotted as individual outliers. It provides an immediate, quantitative view of outliers without needing additional computation. This makes it the most effective single visualization for outlier detection.

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Line chart

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Histogram

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Scatter plot

Question 5

A team is performing exploratory data analysis on a dataset containing 10 million records stored in Amazon S3. They want to sample the data efficiently to build a representative subset for initial modeling. Which sampling method should they use to minimize bias and ensure the sample reflects the population distribution?

Accepted Answer

Stratified random sampling. Stratified random sampling divides the population into homogeneous subgroups (strata) based on a relevant characteristic and then samples proportionally from each stratum. This ensures the sample mirrors the population's distribution across key dimensions, minimizing bias when the dataset has heterogeneous subgroups. For 10 million records with potentially skewed classes or categories, stratified sampling produces a more representative subset than simple random sampling.

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Simple random sampling

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Systematic sampling

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Reservoir sampling

Question 6

A data scientist is using Amazon SageMaker to perform exploratory data analysis on a dataset with missing values and outliers. Which TWO actions should the scientist take to understand the data quality? (Choose TWO.)

Accepted Answer

Use histograms to visualize the distribution of each numerical feature. Histograms show the distribution of numerical features, helping to identify skewness and outliers. Option E is correct because summary statistics like df.describe() provide count, mean, min, max, and quartiles, which reveal missing values (via count) and outliers (via min/max). Option A is incorrect because a scatterplot matrix visualizes pairwise relationships but does not directly show missing values or outliers. Option C is incorrect because a confusion matrix is used for evaluating classification model performance, not for data exploration. Option D is incorrect because a correlation matrix shows relationships between features but does not highlight missing values or outliers.

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Build a scatterplot matrix to visualize pairwise relationships

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Plot a confusion matrix to assess class separation

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Create a correlation matrix to identify redundant features

Question 7

A data analyst is using AWS Glue to catalog datasets for exploratory analysis. The analyst wants to understand the schema and data types. Which TWO tools can the analyst use to view the schema of a table in the AWS Glue Data Catalog? (Choose TWO.)

Accepted Answer

Amazon Athena. Amazon Athena can query the AWS Glue Data Catalog using SQL, including viewing table schemas via the INFORMATION_SCHEMA or by running DESCRIBE statements on tables. The AWS Glue console directly displays the schema of tables in the Data Catalog under the 'Tables' section. Amazon Redshift query editor is for querying Redshift data warehouses, not for directly viewing Glue Catalog schemas unless a federated query is set up. Amazon QuickSight is a business intelligence tool for visualizing data, not for schema exploration. Amazon S3 console only shows objects in S3 buckets, not the schema of Glue tables.

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Amazon Redshift query editor

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Amazon QuickSight

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Amazon S3 console

Question 8

A data scientist is analyzing a dataset with 10,000 rows and 50 columns. The target variable is binary. Which technique is most appropriate for identifying the most important features for predicting the target?

Accepted Answer

Train a Random Forest classifier and use feature_importances_. A Random Forest classifier provides a direct, model-based measure of feature importance via the feature_importances_ attribute, which quantifies each feature's contribution to reducing impurity across all trees. Because the target is binary, a classifier is appropriate, and Random Forest handles mixed feature types and non-linear relationships well. This gives an interpretable ranking of the 50 features for predicting the target.

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Use t-SNE to reduce dimensionality and inspect clusters

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Run K-means clustering and examine cluster centroids

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Apply PCA and select components with highest variance

Question 9

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?

Accepted Answer

Use iterative imputation (MICE) to model missing values. Iterative imputation (MICE) models each feature with missing values as a function of the other features, iterating until convergence. This preserves relationships between variables and avoids the bias introduced by simple single-value substitutions. It is the recommended approach when the goal is to impute without distorting the underlying distribution.

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Remove rows with any missing values

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Replace missing values with the mode of each column

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Replace missing values with the mean of each column

Question 10

A data scientist wants to visualize the correlation between a continuous feature and a binary target variable. Which plot is most appropriate?

Accepted Answer

Box plot of the feature grouped by target class. A box plot displays the distribution of the continuous feature for each category of the binary target, allowing easy comparison of medians, spreads, and outliers. This reveals correlation: if the distributions differ notably between classes, the feature is likely correlated with the target. Option A is wrong because a scatter plot is typically used for two continuous variables, not a binary target. Option B is wrong because a histogram shows the distribution of a single continuous variable, ignoring the target. Option D is wrong because a bar chart of target class counts only shows class frequency, not the relationship with the feature.

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Scatter plot with feature on x-axis and target on y-axis

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Histogram of the feature

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Bar chart of target class counts

Question 11

A data scientist is exploring a dataset and finds that the variance of a feature is 0. What should be done with this feature?

Accepted Answer

Remove the feature from the dataset. A feature with zero variance has the same value in every row, so it carries no information for distinguishing between observations. It cannot contribute to a model's predictive power and can even cause issues in algorithms that divide by standard deviation (e.g., standardization produces division by zero). The correct EDA action is to identify and remove such constant features.

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Create interaction terms with other features

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Apply Min-Max scaling to normalize the feature

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Impute missing values using the mean

Question 12

A data scientist is analyzing a dataset and notices that the distribution of a continuous feature is heavily right-skewed. Which transformation is most likely to make the distribution more symmetric?

Accepted Answer

Log transformation (natural log). A log transformation (natural log) compresses the upper tail of a right-skewed distribution, pulling in large values and making the distribution more symmetric. It is the standard remedy for positive right-skewed data because it reduces the influence of extreme high values. Min-Max scaling only rescales values to a fixed range without changing the shape of the distribution. One-hot encoding is for categorical variables, and squaring a right-skewed distribution would amplify the skew, not reduce it.

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Min-Max scaling

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One-hot encoding

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Square transformation

Question 13

During EDA, a data scientist finds that two features have a Pearson correlation coefficient of 0.95. What is the primary concern when using these features together in a linear regression model?

Accepted Answer

Multicollinearity will make coefficient estimates unstable. A Pearson correlation coefficient of 0.95 indicates strong multicollinearity between the two features. Multicollinearity inflates the variance of coefficient estimates, making them unstable and difficult to interpret. Option A is wrong because redundant information leads to multicollinearity, not underfitting; underfitting occurs when the model is too simple. Option B is wrong because heteroscedasticity refers to non-constant variance of errors, not correlation between features. Option C is wrong because overfitting is more associated with model complexity and variance, not directly with redundant features; in fact, redundant features can cause numerical instability but not necessarily overfitting.

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The model will underfit because of redundant information

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Heteroscedasticity will be introduced

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The model will overfit due to redundant features

Question 14

Which THREE of the following are common issues that can be identified during exploratory data analysis? (Select THREE.)

Accepted Answer

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.

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High latency in API endpoints

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Gradient vanishing in neural networks

Question 15

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.)

Accepted Answer

Reduce memory usage and computation time. Option D is correct because working with a sample reduces the volume of data loaded into memory and the number of computations required, which lowers memory usage and speeds up processing during exploratory data analysis. Option E is correct because sampling makes it feasible to render and interact with visualizations in real time when the full dataset is too large for interactive plotting tools to handle smoothly. Options A, B, and C are not valid reasons: sampling does not remove bias from the original dataset (it can even introduce sampling bias), it does not guarantee that rare events are captured (rare events may be missed unless specialized sampling is used), and it does not inherently improve model accuracy by reducing noise, since sampling discards data rather than cleaning it.

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Remove bias from the original dataset

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Ensure rare events are captured in the analysis

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Improve model accuracy by reducing noise