MLS-C01 Exploratory Data Analysis Practice Test 21 — 15 Questions

Question 1

During exploratory data analysis, a data scientist notices that the target variable is highly imbalanced. Which technique should be used to address this issue before training a classification model?

Accepted Answer

Use SMOTE to generate synthetic samples for the minority class. SMOTE (Synthetic Minority Over-sampling Technique) is a popular method for handling imbalanced datasets by generating synthetic samples for the minority class. Option A (PCA) is wrong because dimensionality reduction does not address class imbalance. Option B (removing outliers) is wrong because it may worsen imbalance and is not a standard technique for imbalance. Option C (cross-validation) is a model evaluation technique, not a solution for imbalance. Option D (feature scaling) does not affect class distribution.

Answer

Apply PCA to reduce dimensionality

Answer

Remove outliers from the majority class

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Use cross-validation to evaluate the model

Answer

Apply feature scaling to all features

Question 2

A data scientist is exploring a dataset stored in an Amazon S3 bucket. The dataset contains both numerical and categorical features. The scientist wants to compute summary statistics (mean, median, standard deviation) for all numerical features and count the distinct values for categorical features. Which AWS service is most appropriate for this task with minimal coding?

Accepted Answer

AWS Glue DataBrew. AWS Glue DataBrew is purpose-built for visual data profiling and transformation with minimal coding. It provides built-in profiling that automatically computes summary statistics (mean, median, standard deviation) for numerical columns and distinct value counts for categorical columns directly from S3 sources. DataBrew's profile view surfaces these statistics without writing any code, matching the 'minimal coding' requirement precisely.

Answer

Amazon Athena

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AWS Glue ETL jobs

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Amazon SageMaker Data Wrangler

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Amazon EMR

Question 3

A data scientist is performing EDA on a time-series dataset and observes a strong upward trend and seasonal patterns. The scientist needs to make the data stationary for modeling. Which transformation should be applied?

Accepted Answer

Apply differencing to the series. Differencing is a technique that removes trends and seasonality by subtracting the previous observation from the current one, making the time series stationary. One-hot encoding (A) is used for categorical variables. PCA (B) reduces dimensionality but does not address stationarity. Min-max scaling (C) normalizes the range of data but does not remove trend or seasonality. Logarithmic transformation (E) stabilizes variance but does not eliminate trends. Therefore, differencing (D) is the correct choice.

Answer

Apply one-hot encoding

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Apply PCA

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Apply min-max scaling

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Apply logarithmic transformation

Question 4

A data scientist is analyzing a dataset with high cardinality categorical features (e.g., user IDs with millions of unique values). They want to visualize the relationship between these categorical features and a continuous target variable. Which approach is most effective for EDA?

Accepted Answer

Group rare categories into an 'Other' category and use box plots. For high-cardinality categorical features, plotting every category is impractical. Grouping rare categories into an 'Other' bucket reduces the number of categories to a manageable set, and box plots then show the distribution of the continuous target variable across each category, revealing median, spread, and outliers. This is the most effective EDA approach because it balances information retention with visual clarity.

Answer

Apply one-hot encoding and use scatter plots

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Use a bar chart with all categories on x-axis

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Remove the categorical features from analysis

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Apply feature hashing and visualize the hashed values

Question 5

During EDA, a data scientist discovers that two numerical features have a Pearson correlation coefficient of 0.95. Which action should the scientist take to avoid multicollinearity in a linear regression model?

Accepted Answer

Remove one of the features. A Pearson correlation of 0.95 indicates very high linear correlation between the two features, which causes multicollinearity in linear regression — inflating coefficient variance and making estimates unstable. The most direct and standard remediation is to remove one of the correlated features, preserving model interpretability and eliminating the redundancy. While PCA and Ridge can mitigate multicollinearity, they change the model's interpretability or introduce bias, and the question asks for the action to avoid multicollinearity in a linear regression context.

Answer

Apply PCA to the two features

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Use Ridge regression to penalize coefficients

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Create polynomial features from the correlated pair

Answer

Apply min-max scaling to both features

Question 6

A data scientist is exploring a dataset and wants to check for missing values. Which method is most appropriate to identify the percentage of missing values per column?

Accepted Answer

Use pandas .isnull().sum() in a SageMaker notebook. In a SageMaker notebook, pandas' .isnull().sum() (often divided by len(df) for a percentage) is the standard, direct way to count missing values per column. It operates on the in-memory DataFrame, handles NaN/None uniformly, and gives per-column counts in one line — exactly what the question asks for.

Answer

Use Amazon S3 Select to query missing values

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Use Amazon Athena to run a SELECT COUNT(*) query

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Use Amazon QuickSight to create a missing value dashboard

Answer

Use AWS Glue Crawler to detect missing values

Question 7

A data scientist is performing EDA on a dataset with many features. They suspect some features are redundant due to high pairwise correlations. Which technique can help identify groups of correlated features?

Accepted Answer

Create a correlation matrix and visualize with a heatmap. A correlation matrix visualized as a heatmap directly shows pairwise correlation coefficients between all features, making it easy to spot groups of highly correlated (redundant) features. This is the standard EDA technique for identifying multicollinearity.

Answer

Use t-SNE to visualize feature relationships

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Apply PCA and examine the loadings

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Compute mutual information between each feature and the target

Answer

Use chi-square test for each pair

Question 8

A data scientist is performing EDA on a dataset with mixed data types (numerical and categorical). Which TWO visualizations are most appropriate for understanding the distribution of categorical features?

Accepted Answer

Pie chart. A pie chart (C) is appropriate because it displays the proportion or percentage share of each category relative to the whole, making it easy to see how a categorical feature's levels are distributed when the number of categories is small. A bar chart (E) is also appropriate because it plots the frequency or count of each category as separate bars, clearly revealing the distribution, dominance, and relative sizes of categorical values. Histograms (A) are designed for continuous numerical data by binning values, so they do not represent discrete categories meaningfully. Box plots (B) summarize numerical distributions via quartiles, median, and outliers, which is not applicable to categorical data. Scatter plots (D) show relationships between two numerical variables and cannot meaningfully plot categorical distributions.

Answer

Histogram

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Box plot

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Scatter plot

Question 9

A data scientist is analyzing a dataset and suspects the presence of outliers that could affect the mean and standard deviation. Which TWO methods are robust to outliers for measuring central tendency and dispersion?

Accepted Answer

Interquartile range (IQR). The interquartile range (IQR) is correct because it measures dispersion using the difference between the 75th percentile (Q3) and the 25th percentile (Q1), so extreme values in the tails do not affect it. The median is correct because it is the middle value of an ordered dataset, and its position depends only on rank rather than magnitude, making it resistant to outliers. By contrast, the range is not robust because it is defined as maximum minus minimum, so a single extreme value directly inflates it. Standard deviation is not robust because it squares deviations from the mean, heavily weighting outliers. The mean is not robust because it sums all values and divides by n, so outliers pull it toward themselves.

Answer

Range

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Standard deviation

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Mean

Question 10

A data scientist is performing EDA on a dataset with a binary target variable. Which THREE techniques can help assess the relationship between a continuous feature and the target?

Accepted Answer

KDE plot grouped by target. Option B (KDE plot grouped by target) is correct because kernel density estimation overlays the distribution of the continuous feature for each class of the binary target, revealing shifts, overlaps, or separation that indicate the strength of the relationship. Option C (Histogram colored by target) is correct because binning the continuous feature and stacking/coloring counts by target class shows how the class-conditional distributions differ across the feature's range. Option E (Box plot grouped by target) is correct because it summarizes each class's median, IQR, and outliers for the continuous feature, making between-group differences easy to compare. Option A is not appropriate because a scatter plot of the continuous feature against another continuous feature does not involve the binary target, so it cannot assess the feature-target relationship. Option D is not appropriate because a bar chart of feature values is suited to categorical or discrete data and does not meaningfully display the distribution of a continuous feature relative to a binary target.

Answer

Scatter plot against another continuous feature

Answer

Bar chart of feature values

Question 11

A data scientist is performing EDA on a dataset that contains customer demographics and purchase history. The dataset has a column 'age' with some values that are negative or unreasonably high (e.g., 200). The scientist wants to identify and handle these outliers. The scientist is using a SageMaker notebook with pandas. Which approach should the scientist take to effectively handle these outliers?

Accepted Answer

Define reasonable bounds based on domain knowledge and filter or cap the outliers. The most appropriate approach is to define reasonable bounds based on domain knowledge (e.g., 0-120) and filter out or cap the outliers. Option A is incorrect because standard scaling does not handle outliers; it will still be influenced by extreme values. Option B is incorrect because imputing with the mean can distort the distribution when outliers are present. Option D is incorrect because removing the entire column discards valuable information.

Answer

Apply standard scaling to the 'age' column

Answer

Impute the outlier values with the mean of the column

Answer

Remove the 'age' column entirely

Question 12

A data scientist is analyzing a dataset with 500 features and 100,000 observations. The target variable is binary. The dataset contains highly correlated features and some categorical variables with high cardinality. Which combination of techniques should the data scientist use to reduce dimensionality while preserving interpretability for EDA?

Accepted Answer

Apply correlation-based feature selection to remove highly correlated pairs, then use target encoding for high-cardinality categorical variables.. Correlation-based feature selection removes highly correlated features, reducing redundancy without distorting the original feature space, and target encoding converts high-cardinality categorical variables into numeric values based on the target mean, which preserves interpretability and avoids dimensionality explosion. Option A is incorrect because PCA reduces interpretability by transforming features into principal components and does not handle categorical variables directly. Option B is incorrect because mutual information is a feature selection method, but label encoding for high-cardinality categoricals can impose arbitrary ordinal relationships. Option C is incorrect because chi-squared test requires categorical features and is not suitable for high-dimensional numerical data; also, one-hot encoding high-cardinality categoricals leads to a drastic increase in dimensionality.

Answer

Apply Principal Component Analysis (PCA) to all features and then train a model on the top 50 components.

Answer

Use mutual information to select top features and apply label encoding to categorical variables.

Answer

Use chi-squared test to select top features and one-hot encode categorical variables.

Question 13

An organization stores streaming data in Amazon Kinesis Data Streams. A data analyst wants to perform real-time exploratory data analysis on the incoming data to detect anomalies. Which AWS service should the analyst use to run SQL queries on the streaming data?

Accepted Answer

Amazon Kinesis Data Analytics. Amazon Kinesis Data Analytics (now Amazon Managed Service for Apache Flink) is purpose-built to run continuous SQL queries over streaming data in Kinesis Data Streams, enabling real-time anomaly detection without provisioning servers. It ingests the stream, applies SQL transformations or windowed aggregations, and emits results to downstream destinations.

Answer

Amazon SageMaker

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AWS Glue

Answer

Amazon Athena

Question 14

A data scientist is using Amazon SageMaker Data Wrangler for exploratory data analysis. The dataset contains a column with missing values that are encoded as 'NA' strings. The data scientist wants to treat these as missing values during the import. Which step should the data scientist take?

Accepted Answer

Configure a custom missing value symbol 'NA' in the import settings of Data Wrangler.. In SageMaker Data Wrangler, missing value symbols like 'NA' can be configured during import so they are treated as nulls from the start. This ensures subsequent transforms and analyses correctly recognize them as missing rather than as valid string values.

Answer

Use the 'Impute' transform to fill 'NA' with the mean of the column.

Answer

Use the 'Replace missing' transform to replace 'NA' with null after import.

Answer

Use the 'Drop missing' transform to remove rows containing 'NA'.

Question 15

A data engineer is performing EDA on a dataset with 1 million rows and 200 columns. The dataset is stored in S3 as CSV files. The engineer notices that some columns have a high proportion of zeros. What is the best approach to determine if these zeros represent missing data or actual zero values?

Accepted Answer

Consult the data source documentation or domain experts to understand the meaning of zero values.. Domain knowledge and documentation are the most reliable ways to understand the meaning of zeros. Option A is wrong because statistical methods cannot distinguish missing vs actual zero without context. Option B is wrong because metadata may not have this detail. Option C is wrong because comparing to other columns might be misleading.

Answer

Check correlation of zero columns with other features; if low, assume zeros are missing.

Answer

Calculate the percentage of zeros and compare with other columns; if unusually high, treat as missing.

Answer

Use AWS Glue Data Catalog to view column statistics and infer missing values.