MLS-C01 Exploratory Data Analysis Practice Test 27 — 15 Questions

Question 1

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. A log transformation is commonly applied to right-skewed positive data to reduce skewness and make the distribution more symmetric. Option A is wrong because standardization (Z-score) centers and scales the data but does not change the shape of the distribution. Option B is wrong because a square transformation would increase skewness for right-skewed data. Option D is wrong because min-max scaling rescales the data to a fixed range but does not alter the distribution's skewness.

Answer

Standardization (Z-score)

Answer

Square transformation

Answer

Min-Max scaling

Question 2

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. The most appropriate because a histogram with overlaid densities, colored by group, directly shows the distribution of each group and can reveal whether the two modes correspond to different groups. Option A (box plot) displays summary statistics but not the shape or modality. Option B (bar chart) is for categorical data, not continuous. Option D (scatter plot) is for two continuous variables, not for examining a single distribution.

Answer

Box plot

Answer

Bar chart

Answer

Scatter plot

Question 3

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. The Interquartile Range (IQR) method with a multiplier of 1.5 is robust to skewed distributions because it uses quartiles, which are less affected by extreme values than mean and standard deviation. For a feature with a long right tail, IQR effectively identifies outliers beyond the upper fence (Q3 + 1.5*IQR) without assuming normality. This makes it the most robust choice for detecting data entry errors in a skewed numerical feature.

Answer

Mahalanobis distance

Answer

Z-score with threshold 3

Answer

DBSCAN clustering

Question 4

During EDA, a data scientist generates a pairplot of the dataset and observes that two features have a Pearson correlation coefficient of 0.95. Which TWO conclusions can the scientist draw from this observation? (Choose 2)

Accepted Answer

The two features may be multicollinear. Option A is correct because a Pearson correlation coefficient of 0.95 between two features indicates a very high degree of linear association, which is a classic sign that the two features may be multicollinear, meaning they carry largely redundant information and can destabilize models such as linear regression. Option B is correct because the Pearson correlation coefficient directly measures the strength and direction of a linear relationship, and a value of 0.95 (close to +1) indicates a strong positive linear relationship. Option C is incorrect because a coefficient of 0.95 is positive, so the features move in the same direction, not opposite directions; a negative value near -1 would indicate an inverse relationship. Option D is incorrect because statistical independence would correspond to a correlation near 0, whereas 0.95 shows a strong dependence. Option E is incorrect because correlation does not imply causation; the high coefficient only shows association, not that one feature causes the other.

Answer

The two features move in opposite directions

Answer

The two features are statistically independent

Answer

One feature causes the other

Question 5

Refer to the exhibit. A data scientist is setting up an IAM policy for EDA on a data lake. The scientist needs to run exploratory SQL queries using Amazon Athena and save results to a new S3 bucket. What is a critical missing permission in this policy?

Accepted Answer

s3:PutObject on the output S3 bucket. Athena writes query results to an S3 output bucket, which requires the s3:PutObject permission. Option A is incorrect because s3:ListBucket on the output bucket is not needed to write results; s3:PutObject is the necessary write permission. Option C is incorrect because glue:GetDatabase is not required for the query execution; the policy likely already includes glue:GetTable. Option D is incorrect because athena:StopQueryExecution is not directly related to saving results; the missing permission for saving results is s3:PutObject.

Answer

s3:ListBucket on the output bucket

Answer

glue:GetDatabase

Answer

athena:StopQueryExecution

Question 6

A data scientist is analyzing a dataset with 50 features and 10,000 samples. After generating a correlation matrix, they notice several pairs of features have correlation coefficients above 0.95. What should the data scientist do to prepare the data for linear regression?

Accepted Answer

Remove one feature from each highly correlated pair.. When two features have a correlation coefficient above 0.95, they carry nearly identical information, which causes multicollinearity in linear regression. Multicollinearity inflates the variance of coefficient estimates, making them unstable and hard to interpret. Removing one feature from each highly correlated pair preserves predictive information while eliminating redundancy, which is the standard, targeted fix.

Answer

Apply PCA to reduce dimensionality to 10 components.

Answer

Drop all features with correlation above 0.95.

Answer

Standardize all features using StandardScaler.

Question 7

A machine learning engineer notices that the target variable in a regression dataset has a long-tailed distribution. Which visualization technique is most appropriate to assess the distribution before applying a log transformation?

Accepted Answer

Histogram with density curve. (Histogram with density curve) is the most appropriate visualization for assessing a long-tailed distribution because it clearly shows the shape, spread, and tail behavior of the target variable. A histogram with an overlaid density curve helps identify skewness and the need for a log transformation. Option A (Bar chart) is for categorical data, not continuous distributions. Option C (Box plot) provides quartiles and outliers but does not fully reveal the distribution shape, especially the length of tails. Option D (Scatter plot) visualizes relationships between two variables, not a univariate distribution.

Answer

Bar chart

Answer

Box plot

Answer

Scatter plot

Question 8

A data analyst is using Amazon Athena to query a partitioned dataset in S3. They notice that queries are scanning more data than expected. Which step should they take during exploratory data analysis to optimize query performance?

Accepted Answer

Check the partition metadata to ensure queries are pruning partitions.. Athena uses Hive-style partition metadata from the AWS Glue Data Catalog to prune partitions at query planning time. If partitions are missing, misregistered, or use a type/location mismatch, Athena falls back to scanning the entire table prefix in S3, inflating bytes scanned. Verifying partition metadata (via SHOW PARTITIONS, MSCK REPAIR TABLE, or checking Glue Catalog entries) confirms pruning is actually happening before optimizing further.

Answer

Convert the data to Parquet format.

Answer

Use S3 Select to filter data before querying.

Answer

Increase the number of workers in Athena.

Question 9

A data scientist is performing exploratory data analysis on a dataset with mixed data types: numerical, categorical, and text. They want to use Amazon SageMaker Data Wrangler to create a quick visualization dashboard. Which set of transformations should they apply in Data Wrangler to handle all data types appropriately?

Accepted Answer

Use the built-in analysis: summary statistics for numerical, word cloud for text, and frequency for categorical.. Amazon SageMaker Data Wrangler provides built-in visualizations tailored to data types: summary statistics for numerical, word cloud for text, and frequency/category plots for categorical. This approach handles mixed data types appropriately without forcing inappropriate transformations. It allows quick exploratory analysis before modeling.

Answer

Convert all features to numerical using one-hot encoding and then create a scatter matrix.

Answer

Apply TF-IDF vectorization to text and then run k-means clustering.

Answer

Use PCA to reduce dimensionality and then visualize the first two components.

Question 10

During exploratory data analysis, a machine learning engineer finds that a dataset has a significant number of missing values in a categorical feature with 10 levels. Which approach should they take to handle these missing values before modeling?

Accepted Answer

Create a new category labeled 'Missing' for missing values.. Creating a separate 'Missing' category preserves the missingness pattern and avoids data loss or bias from imputation for categorical features. Option A is incorrect because mean imputation is for numerical features, not categorical. Option C is incorrect because dropping all rows with missing values may discard valuable data and reduce sample size. Option D is incorrect because mode imputation may introduce bias if missingness is not random.

Answer

Impute missing values with the mean of the feature.

Answer

Drop all rows with missing values.

Answer

Impute missing values with the mode of the feature.

Question 11

A data engineer is using AWS Glue to catalog a dataset with 200 columns. During exploratory data analysis, they run a crawler and then view the table schema in the AWS Glue Data Catalog. They notice that many columns are inferred as 'string' even though they contain numeric values. What is the most likely cause?

Accepted Answer

The crawler sample size is too small, and the sampled rows contain non-numeric values.. AWS Glue crawlers sample a subset of data (default 1 MB or a configurable sample size) to infer schema. If the sampled rows contain non-numeric values (e.g., empty strings, 'N/A', or text) in columns that are mostly numeric, the crawler will infer the column as string to accommodate the mixed types.

Answer

The data is stored in JSON format, which only supports string types.

Answer

The data is stored in Parquet format, which does not support numeric types.

Answer

The column names contain special characters that prevent type inference.

Question 12

Which TWO actions are appropriate during exploratory data analysis when you discover that a categorical feature has 50 unique values (high cardinality)?

Accepted Answer

Group rare categories into a single 'Other' category.. Option A is correct because grouping infrequent categories into a single 'Other' bucket reduces the effective cardinality of the feature, limiting the number of dummy variables and preventing rare levels from adding noise or causing overfitting during exploratory data analysis. Option E is correct because feature hashing (the hashing trick) maps the 50 categories into a fixed, lower-dimensional space via a hash function, which controls dimensionality and handles high-cardinality categoricals efficiently without needing to enumerate every level. Option B is not appropriate here because one-hot encoding 50 categories creates 50 sparse dummy variables, which is exactly the dimensionality problem high cardinality causes. Option C is not ideal because label encoding imposes an arbitrary ordinal relationship on nominal categories, which can mislead models that interpret integers as ordered. Option D is not appropriate because dropping the feature discards potentially useful predictive information rather than addressing the cardinality issue.

Answer

Apply one-hot encoding to create 50 dummy variables.

Answer

Apply label encoding to assign integers to each category.

Answer

Drop the feature entirely.

Question 13

Which THREE techniques are commonly used to detect multicollinearity in a dataset during exploratory data analysis?

Accepted Answer

Eigenvalue analysis from PCA. Eigenvalue analysis from PCA (B) is correct because multicollinearity manifests as very small eigenvalues in the correlation/covariance matrix, indicating that some linear combinations of predictors explain almost no variance. A correlation matrix (C) is correct because high pairwise correlation coefficients (e.g., |r| > 0.8 or 0.9) between independent variables are a direct, common indicator of multicollinearity. Variance Inflation Factor (VIF) (D) is correct because VIF quantifies how much the variance of an estimated regression coefficient is inflated by collinearity, with values above 5 or 10 typically signaling problematic multicollinearity. A heatmap of missing values (A) only visualizes nullity patterns and says nothing about linear dependence among features, and a scatter matrix of all features (E) is a general pairwise visualization that may hint at relationships but is not a standard quantitative multicollinearity detection technique like VIF, correlation coefficients, or PCA eigenvalues.

Answer

Heatmap of missing values

Answer

Scatter matrix of all features

Question 14

A data analyst is exploring a dataset with a binary target variable. Which TWO visualizations are most useful for understanding the relationship between a numerical feature and the target?

Accepted Answer

Histogram with overlaid target classes. Option C, a histogram with overlaid target classes, is correct because it shows the distribution of the numerical feature separately for each binary target value, making it easy to see whether the classes differ in location, spread, or shape. Option D, a box plot grouped by target class, is also correct because it directly compares the median, interquartile range, and outliers of the numerical feature across the two target groups, which is ideal for assessing the feature-target relationship. Option A, a pie chart of the feature, is not appropriate because pie charts are for showing proportions of categorical parts of a whole, not for a numerical feature. Option B, a bar chart of the feature, is not suitable because bar charts are typically used for categorical counts or aggregated values, not for exploring a continuous numerical distribution. Option E, a scatter plot of the feature versus target, is not the best choice here because the target is binary, so the plot would collapse into two horizontal bands and provide limited insight compared with a grouped histogram or box plot.

Answer

Pie chart of the feature

Answer

Bar chart of the feature

Answer

Scatter plot of the feature versus target

Question 15

A data scientist is analyzing a dataset with 1,000 features. They suspect many features are redundant and want to reduce dimensionality before training a model. Which technique is most appropriate for identifying the most important features?

Accepted Answer

Use L1 regularization (Lasso) to shrink coefficients to zero. L1 regularization (Lasso) adds a penalty equal to the absolute value of the coefficients to the loss function, which drives less important feature coefficients exactly to zero. This makes Lasso inherently a feature-selection technique: the features with non-zero coefficients after training are the ones the model retained as important, directly addressing the goal of identifying the most important features among 1,000.

Answer

Apply principal component analysis (PCA) and select the top components

Answer

Train a random forest and remove features with low importance

Answer

Compute the correlation matrix and remove features with high correlation