MLS-C01 Exploratory Data Analysis Practice Test 22 — 15 Questions

Question 1

A data scientist is performing EDA on a dataset with 500,000 rows and 20 columns. The dataset contains missing values in some columns. Which TWO approaches are appropriate for handling missing data during EDA? (Choose 2)

Accepted Answer

Create an indicator column to flag whether the value was missing, then impute with a placeholder. Options C and D are correct because imputation with median/mode is robust, and flagging missingness with an indicator variable preserves information. Option A is wrong because listwise deletion can introduce bias and reduce sample size. Option B is wrong because mean imputation is sensitive to outliers. Option E is wrong because forward fill is for time series, not general EDA.

Answer

Use forward fill to propagate the last observed value

Answer

Remove all rows with any missing value (listwise deletion)

Answer

Impute missing values with the mean of each column

Question 2

A data scientist is evaluating feature engineering options for a dataset containing a categorical variable 'education_level' with values: High School, Bachelor, Master, PhD. The target variable is continuous. Which THREE encoding methods are appropriate for this ordinal categorical variable? (Choose 3)

Accepted Answer

One-hot encoding. Options A, B, and E are correct because label encoding preserves ordinality, target encoding captures the relationship with the target, and one-hot encoding is a safe fallback. Option C is wrong because binary encoding assumes nominal categories. Option D is wrong because hash encoding loses interpretability and may cause collisions.

Answer

Hash encoding (using feature hashing)

Answer

Binary encoding (convert to binary representation)

Question 3

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. Options B and C are correct because a high correlation indicates a strong linear relationship and suggests multicollinearity. Option A is wrong because correlation does not imply causation. Option D is wrong because a high positive correlation means they move together, not opposite. Option E is wrong because correlation measures linear relationship, not independence.

Answer

The two features move in opposite directions

Answer

The two features are statistically independent

Answer

One feature causes the other

Question 4

Refer to the exhibit. A data scientist plans to read this CSV file into memory for exploratory data analysis using pandas. The instance has 8 GB of RAM. What is the MOST likely issue the scientist will encounter?

Accepted Answer

The file is too large to load into memory on this instance. Option B is correct because the file size is approximately 1 GB (1073741824 bytes = 1 GB), and pandas typically requires 3-5x the file size in memory for CSV parsing, which would exceed the 8 GB RAM. Option A is wrong because the file is accessible (HTTP 200). Option C is wrong because the content type is text/csv, which is correct. Option D is wrong because the metadata indicates 10 million rows, but the file size is the main issue.

Answer

The file contains too many rows for pandas to handle

Answer

The file is not in CSV format despite the ContentType

Answer

The file is not accessible because of insufficient permissions

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. Option B is correct because Athena writes query results to an S3 bucket, which requires s3:PutObject permission on the output bucket. Option A is wrong because the policy includes necessary Athena permissions. Option C is wrong because glue:GetTable is already included. Option D is wrong because s3:ListBucket is included.

Answer

s3:ListBucket on the output bucket

Answer

glue:GetDatabase

Answer

athena:StopQueryExecution

Question 6

Refer to the exhibit. A data scientist examines a sample of data and notices that all columns are numeric. The scientist wants to check for multicollinearity. Which statistic should be computed from this sample?

Accepted Answer

Correlation matrix (Pearson). Option A is correct because the correlation matrix shows pairwise Pearson correlations, which can indicate high collinearity. Option B is wrong because VIF requires more variables than observations. Option C is wrong because chi-square is for categorical. Option D is wrong because covariance alone is scale-dependent.

Answer

Chi-square test of independence

Answer

Variance Inflation Factor (VIF)

Answer

Covariance matrix

Question 7

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.. Option B is correct because high correlation between features indicates multicollinearity, which can destabilize linear regression coefficients. Removing one feature from each highly correlated pair reduces redundancy. Option A is wrong because dropping all correlated features may discard useful information. Option C is wrong because standardizing does not address multicollinearity. Option D is wrong because PCA creates new features that lose interpretability.

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 8

A team is using Amazon SageMaker Data Wrangler to perform exploratory data analysis on a large dataset stored in S3. The dataset contains missing values, outliers, and categorical variables with high cardinality. The team wants to understand data distributions and relationships before modeling. Which combination of Data Wrangler features should they use?

Accepted Answer

Generate a data quality report, view histograms, and create scatter plots for selected features.. Option D is correct because Data Wrangler's data quality report provides summary statistics and missing value analysis, and the histogram visualization shows distributions. Scatter plots reveal relationships between variables. Option A is incorrect because Data Wrangler does not include correlation heatmaps directly. Option B is incorrect because imputation and one-hot encoding are transformations, not EDA steps. Option C is incorrect because handling missing values is part of data preparation, not initial EDA.

Answer

Drop rows with missing values and visualize box plots for numerical features.

Answer

Use imputation to handle missing values and one-hot encoding for categorical features.

Answer

Generate a data quality report and a correlation heatmap.

Question 9

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. Option C is correct because a histogram or density plot clearly shows the shape and spread of the distribution, including long tails. Option A is incorrect because box plots show quartiles and outliers but not the full distribution shape. Option B is incorrect because scatter plots show relationships between two variables, not univariate distribution. Option D is incorrect because bar charts are for categorical data.

Answer

Bar chart

Answer

Box plot

Answer

Scatter plot

Question 10

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.. Option B is correct because checking the partition metadata using SHOW PARTITIONS or querying the information_schema helps verify that the partition structure is correct and that queries are using partition pruning. Option A is incorrect because compressing data reduces storage but does not directly affect partition pruning. Option C is incorrect because converting to Parquet improves columnar scanning but does not address partition misuse. Option D is incorrect because increasing workers does not fix incorrect partition usage.

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 11

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.. Option D is correct because Data Wrangler's built-in analysis includes summary statistics for numerical features, word clouds for text, and frequency counts for categorical features. These are appropriate for initial EDA. Option A is incorrect because PCA is for dimensionality reduction, not EDA. Option B is incorrect because TF-IDF is a feature engineering step, not EDA. Option C is incorrect because clustering is a modeling step, not EDA.

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 12

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.. Option C is correct because 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 dropping rows with missing values may discard valuable data. Option B is incorrect because mean imputation is for numerical features, not categorical. 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 13

A data scientist uses Amazon QuickSight to visualize a dataset and observes that a numerical feature has a skewness of 2.5 and a kurtosis of 8. Which transformation should they apply to make the distribution more normal?

Accepted Answer

Apply a log transformation.. Option B is correct because a skewness of 2.5 indicates right skew, and a log transformation is commonly used to reduce skewness. Option A is incorrect because standardization does not change distribution shape. Option C is incorrect because Min-Max scaling does not change skewness. Option D is incorrect because a Box-Cox transformation requires positive data and is a more general solution, but log is simpler and often sufficient; however, Box-Cox is also valid. In the context of this question, log is the most direct answer.

Answer

Standardize the feature using Z-score normalization.

Answer

Apply a Box-Cox transformation with lambda=0.5.

Answer

Apply Min-Max scaling to the range [0,1].

Question 14

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.. Option D is correct because the crawler samples data and may not see enough numeric values if the sample size is small or if the first few rows contain non-numeric values (e.g., headers or missing values). Option A is incorrect because the crawler does not rely on column names for type inference. Option B is incorrect because Parquet files store schema, but if the data is CSV, the crawler infers types. Option C is incorrect because JSON files also have type information, but the crawler can still infer incorrectly.

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 15

A data scientist wants to identify outliers in a dataset with 1,000 samples and 5 numerical features. Which technique is most appropriate for univariate outlier detection?

Accepted Answer

Interquartile range (IQR) method. Option C is correct because the IQR method identifies outliers as points outside 1.5*IQR from the quartiles, which is robust to non-normal distributions. Option A is incorrect because Z-score assumes normality and is sensitive to extreme outliers. Option B is incorrect because Mahalanobis distance is for multivariate outliers. Option D is incorrect because PCA is for dimensionality reduction, not outlier detection.

Answer

Principal component analysis (PCA)

Answer

Mahalanobis distance

Answer

Z-score with a threshold of 3