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

Question 1

A data scientist is exploring a dataset with 50 features and a binary target. The data scientist computes the correlation matrix and finds that two features, X1 and X2, have a correlation coefficient of 0.95. Which TWO actions should the data scientist consider? (Choose 2.)

Accepted Answer

Remove one of the highly correlated features from the dataset.. Option A is correct because high correlation indicates multicollinearity; removing one feature reduces redundancy. Option C is correct because PCA can create uncorrelated components. Option B is wrong because adding interaction terms would increase multicollinearity. Option D is wrong because correlation does not imply missing values. Option E is wrong because log transformation does not address correlation between features.

Answer

Apply a log transformation to X1 and X2.

Answer

Create an interaction term between X1 and X2.

Answer

Impute missing values for X1 and X2.

Question 2

A data scientist is performing EDA on a dataset stored in Amazon S3 using Amazon Athena. The dataset is partitioned by date, and each partition contains CSV files. The data scientist notices that some queries return zero rows for partitions that should have data. Which THREE steps should the data scientist take to troubleshoot? (Choose 3.)

Accepted Answer

Verify that the CSV files exist in the S3 bucket for the specific partition.. Option A is correct because manually checking files confirms data presence. Option B is correct because MSCK REPAIR TABLE adds partitions not yet registered. Option C is correct because incorrect data types can cause filters to exclude rows. Option D is wrong because the query does not automatically update partitions. Option E is wrong because converting to Parquet is not a troubleshooting step.

Answer

Convert the CSV files to Parquet format.

Answer

Re-run the query with a LIMIT clause to force partition discovery.

Question 3

A data scientist is analyzing a dataset with a mix of numerical and categorical features. The target variable is binary. The data scientist wants to visualize the distribution of a numerical feature across the two target classes. Which TWO visualization techniques are appropriate? (Choose 2.)

Accepted Answer

Overlapping histograms with transparency. Option A is correct because side-by-side boxplots show distribution and outliers across categories. Option C is correct because overlapping histograms with transparency show distribution shapes. Option B is wrong because scatter plots require two numerical variables. Option D is wrong because heatmaps are for correlation or contingency tables. Option E is wrong because bar charts are for categorical data, not numerical distributions.

Answer

Heatmap of the correlation matrix

Answer

Stacked bar chart of the feature binned

Answer

Scatter plot with color-coded classes

Question 4

A data scientist runs the AWS CLI command shown in the exhibit to list objects larger than 100 KB in an S3 bucket. The data scientist wants to understand the size distribution of these files. What is the most significant limitation of this approach for EDA?

Accepted Answer

The command may return incomplete results if there are more than 1000 objects.. Option D is correct because the CLI command may not return all objects if there are more than 1000, as S3 list-objects paginates by default. Option A is wrong because the command returns size and key, which are sufficient. Option B is wrong because the command does include objects exactly 100 KB. Option C is wrong because the query syntax is correct for filtering.

Answer

The command only returns objects larger than 100 KB, not equal to.

Answer

The command uses the wrong query syntax and will fail.

Answer

The command does not return the file names, only sizes.

Question 5

A data scientist is granted the IAM policy shown in the exhibit. The data scientist can query the 'data-lake-bucket' using Athena and get results. However, when the data scientist tries to run a CTAS (CREATE TABLE AS SELECT) query in Athena to write results to a new S3 location, the query fails. What is the most likely reason?

Accepted Answer

The policy does not grant s3:PutObject permission on the bucket.. Option B is correct because the policy allows s3:GetObject and s3:ListBucket, but not s3:PutObject, which is required for CTAS queries. Option A is wrong because the policy uses resource-level permissions for S3. Option C is wrong because Athena does not require Glue Data Catalog permissions for CTAS if the table metadata is already stored. Option D is wrong because the policy does not restrict Athena resource ARNs.

Answer

The policy does not grant athena:CreateTable permission.

Answer

The policy does not grant permissions to the Glue Data Catalog.

Answer

The policy uses a wildcard for Athena actions, which is not allowed.

Question 6

A data scientist is investigating an application that logs errors to Amazon CloudWatch Logs. The data scientist runs the CloudWatch Logs Insights query shown in the exhibit. The query returns no results, even though the data scientist knows errors have occurred. What is the most likely cause?

Accepted Answer

The filter pattern is case-sensitive and the log messages use a different case for 'error'.. Option A is correct because the query is case-sensitive; 'ERROR' may not match 'error' or 'Error'. Option B is wrong because the sort order does not affect whether results are returned. Option C is wrong because the query uses correct syntax. Option D is wrong because bin(5m) is valid if there are logs within the time range.

Answer

The stats count() function is misspelled.

Answer

The query sorts by timestamp descending, which hides results.

Answer

The bin(5m) function is not supported in CloudWatch Logs Insights.

Question 7

A data scientist is analyzing a dataset with a target variable that is highly imbalanced (only 1% positive class). The goal is to build a binary classifier. During exploratory data analysis, which metric is MOST appropriate to evaluate the performance of different sampling strategies before model training?

Accepted Answer

Area Under the Receiver Operating Characteristic Curve (AUC ROC). Option D is correct because the AUC ROC curve is independent of class distribution and provides a robust measure of separability between classes. Option A is wrong because accuracy is misleading for imbalanced data. Option B is wrong because RMSE is for regression. Option C is wrong because F1 score depends on a threshold and can be affected by sampling.

Answer

Root Mean Squared Error (RMSE)

Answer

F1 score

Answer

Accuracy

Question 8

A data engineer is performing exploratory data analysis on a dataset stored in Amazon S3 using AWS Glue DataBrew. The dataset contains a column 'age' with missing values. DataBrew's profile shows that the column has 5% missing values, a mean of 45, and a standard deviation of 15. Which imputation strategy should the engineer recommend to minimize bias if the missing data is Missing at Random (MAR)?

Accepted Answer

Use multiple imputation to generate several plausible values and combine results. Option C is correct because multiple imputation provides unbiased estimates under MAR by accounting for uncertainty. Option A is wrong because mean imputation reduces variance and can bias relationships. Option B is wrong because median imputation is robust but still single imputation. Option D is wrong because dropping rows reduces sample size and may introduce bias if missingness is related to other variables.

Answer

Replace missing values with the mean (45)

Answer

Remove rows with missing 'age' values

Answer

Replace missing values with the median

Question 9

During exploratory data analysis, a data scientist notices that the Pearson correlation coefficient between two continuous variables is 0.85. What does this indicate?

Accepted Answer

A strong positive linear relationship. Option B is correct because a correlation of 0.85 indicates a strong positive linear relationship. Option A is wrong because 0.85 is far from 1. Option C is wrong because correlation does not imply causation. Option D is wrong because 0.85 is not weak.

Answer

A causal relationship between the two variables

Answer

A weak negative linear relationship

Answer

No relationship between the variables

Question 10

A machine learning engineer is analyzing a dataset that contains a categorical feature 'country' with 200 unique values. The target variable is binary. The engineer wants to use this feature in a linear model. Which encoding method should be applied during EDA to prepare the data for modeling, considering the high cardinality?

Accepted Answer

Target encoding with cross-validation. Option D is correct because target encoding (or mean encoding) replaces each category with the mean of the target, which is suitable for high cardinality in linear models but requires careful validation to avoid overfitting. Option A is wrong because one-hot encoding would create 199 dummy variables, leading to high dimensionality. Option B is wrong because label encoding imposes an arbitrary ordinal relationship. Option C is wrong because frequency encoding may not capture the relationship with the target.

Answer

Label encoding

Answer

Frequency encoding

Answer

One-hot encoding

Question 11

A data analyst is performing exploratory data analysis on a dataset with 100 features. The analyst wants to identify which features contribute most to the variance in the data. Which technique should the analyst use?

Accepted Answer

Principal Component Analysis (PCA). Option A is correct because PCA is a dimensionality reduction technique that identifies the directions (principal components) that maximize variance. Option B is wrong because t-SNE is for visualization and does not provide variance contributions. Option C is wrong because LDA is supervised and requires labels. Option D is wrong because K-means is clustering, not variance analysis.

Answer

K-means clustering

Answer

t-Distributed Stochastic Neighbor Embedding (t-SNE)

Answer

Linear Discriminant Analysis (LDA)

Question 12

During EDA, a data scientist discovers that a numerical feature 'income' has a skewness of 3.5. Which transformation should the scientist apply to make the distribution more symmetric?

Accepted Answer

Log transformation. Option D is correct because a log transformation is commonly used for right-skewed positive data to reduce skewness. Option A is wrong because StandardScaler does not change skewness. Option B is wrong because Min-Max scaling does not change shape. Option C is wrong because a square transformation would increase skewness.

Answer

Standardization (Z-score)

Answer

Square transformation

Answer

Min-Max scaling

Question 13

A data scientist is exploring a dataset with a large number of features. The scientist suspects that some features are redundant because they are highly correlated with each other. Which technique should the scientist use during EDA to identify and remove such redundant features?

Accepted Answer

Variance Inflation Factor (VIF). Option B is correct because VIF quantifies multicollinearity by measuring how much the variance of a coefficient is inflated due to correlation with other features. Option A is wrong because PCA creates new features, not identification of redundant ones. Option C is wrong because correlation matrix shows pairwise correlations but VIF is more comprehensive. Option D is wrong because Chi-square is for categorical features.

Answer

Chi-square test

Answer

Principal Component Analysis (PCA)

Answer

Correlation matrix heatmap

Question 14

In exploratory data analysis, a data scientist notices that the distribution of a continuous variable is bimodal. The scientist suspects that the two modes correspond to two different groups in the data. Which visualization is MOST appropriate to confirm this suspicion?

Accepted Answer

Histogram with overlaid densities by group. Option A is correct because a histogram with hue (color) for each group can show the separate distributions. Option B is wrong because a box plot shows summary statistics but not the shape. Option C is wrong because a scatter plot is for two continuous variables. Option D is wrong because a bar chart is for categorical data.

Answer

Box plot

Answer

Bar chart

Answer

Scatter plot

Question 15

A data scientist is analyzing a dataset with 1 million rows and 50 features. The scientist wants to detect outliers in a numerical feature 'transaction_amount' which has a long right tail. The scientist suspects that outliers are due to data entry errors and should be removed. Which outlier detection method is MOST robust for this scenario?

Accepted Answer

Interquartile range (IQR) with multiplier 1.5. Option C is correct because the IQR method is robust to skewed distributions and does not assume normality. Option A is wrong because Z-score assumes normality. Option B is wrong because Mahalanobis distance assumes multivariate normality. Option D is wrong because DBSCAN is computationally expensive on 1 million rows and may not be practical for univariate outlier detection.

Answer

Mahalanobis distance

Answer

Z-score with threshold 3

Answer

DBSCAN clustering