MLS-C01 Exploratory Data Analysis Practice Test 23 — 15 Questions

Question 1

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?

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

Accepted Answer

Check the partition metadata to ensure queries are pruning partitions.. Checking partition metadata (e.g., using SHOW PARTITIONS or querying information_schema) ensures that queries are applying partition pruning, which reduces the amount of data scanned. Option A is incorrect: converting to Parquet improves columnar scan efficiency but does not directly address partition misuse. Option B is incorrect: S3 Select filters data at the object level, but Athena already pushes down filters; this does not fix a lack of partition pruning. Option C is incorrect: increasing workers improves parallelism but does not reduce scanned data if partitions are not pruned.

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 2

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 analysis types that are appropriate for EDA with mixed data types: summary statistics for numerical features, word clouds for text, and frequency counts for categorical features. These allow quick visualization without complex transformations. Option B is incorrect because one-hot encoding and scatter matrix are not suitable for mixed types, and Data Wrangler does not offer a scatter matrix as a built-in analysis. Option C is incorrect because TF-IDF vectorization and k-means clustering are feature engineering and modeling steps, not EDA. Option D is incorrect because PCA is for dimensionality reduction and not a standard EDA visualization technique.

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 3

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 4

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.. Apply a log transformation. A skewness of 2.5 indicates a strong right skew (positive skew), and a kurtosis of 8 indicates heavy tails (leptokurtic). Log transformation is effective in reducing right skewness and making the distribution more symmetric, which is a common step toward normality. Option A (Z-score normalization) standardizes the data but does not change the shape of the distribution. Option B (Box-Cox with lambda=0.5) is a square root transformation, which is less effective than log for high skewness; Box-Cox typically requires choosing an optimal lambda, and lambda=0 would be a log transform. Option C (Min-Max scaling) rescales the range but does not affect skewness or kurtosis.

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 5

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.. The AWS Glue crawler samples a subset of rows to infer schema. If the sample size is too small or the sampled rows contain non-numeric values (e.g., headers, missing data, or text entries), the crawler may default to 'string' type for columns that are actually numeric. Options A, C, and D are incorrect: JSON files can contain numeric types, Parquet files support numeric types, and special characters in column names do not affect type inference.

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 6

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. The IQR method, where outliers are defined as points below Q1 - 1.5*IQR or above Q3 + 1.5*IQR, is appropriate for univariate outlier detection as it does not assume a specific distribution and is robust to extreme values. PCA (A) is a dimensionality reduction technique, not for outlier detection. Mahalanobis distance (C) is for multivariate outliers. Z-score with threshold 3 (D) assumes normality and is sensitive to extreme outliers.

Answer

Principal component analysis (PCA)

Answer

Mahalanobis distance

Answer

Z-score with a threshold of 3

Question 7

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.. Options A and E are correct. A: Grouping rare categories into an 'Other' category reduces cardinality while preserving information, which is appropriate for high-cardinality categorical features. E: Feature hashing (hashing trick) transforms high-cardinality features into a fixed-size vector, reducing dimensionality. Option B is incorrect because one-hot encoding with 50 categories creates many sparse columns, which can be problematic for model performance and memory. Option C is incorrect because label encoding implies an ordinal relationship, which may not exist, and can mislead models. Option D is incorrect because dropping the feature may lose important information; other techniques like grouping or hashing are preferable.

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 8

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

Accepted Answer

Eigenvalue analysis from PCA. Options B, C, and D are correct. B: Eigenvalue analysis from PCA can detect multicollinearity; if some eigenvalues are near zero, it indicates high multicollinearity. C: Correlation matrix shows pairwise correlations between features; high correlation coefficients (e.g., >0.8) indicate collinearity. D: Variance Inflation Factor (VIF) quantifies how much a feature's variance is inflated due to multicollinearity; VIF >10 is often considered problematic. Option A is incorrect because a heatmap of missing values visualizes missing data, not relationships between features. Option E is incorrect because a scatter matrix shows pairwise scatter plots, which can reveal linear relationships but is not a quantitative measure for multicollinearity.

Answer

Heatmap of missing values

Answer

Scatter matrix of all features

Question 9

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. Options C and D are correct. A histogram with overlaid target classes (C) allows viewing the distribution of the feature for each class, highlighting separability. A box plot grouped by target class (D) shows median, spread, and outliers per class, useful for comparing distributions. Option A (pie chart) is inappropriate for numerical features and binary targets. Option B (bar chart) is for categorical features, not numerical. Option E (scatter plot) requires two numerical variables; here the target is binary, so it would produce overlapping points and is less informative.

Answer

Pie chart of the feature

Answer

Bar chart of the feature

Answer

Scatter plot of the feature versus target

Question 10

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) is the most appropriate technique for identifying the most important features because it performs feature selection by shrinking the coefficients of less important features to zero, effectively selecting a subset of original features. This directly identifies which features are most relevant. PCA, while a dimensionality reduction technique, creates new components that are linear combinations of original features and does not identify the importance of original features. Random forest feature importance and correlation matrix methods can identify redundant features but are less direct for selecting the most important subset.

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

Question 11

A data scientist is working on a binary classification problem with a highly imbalanced dataset (1% positive class). They have applied oversampling using SMOTE and trained a logistic regression model. The model achieves 99% accuracy on the test set, but the recall for the positive class is only 5%. What is the most likely cause?

Accepted Answer

SMOTE was applied before splitting the data into training and test sets. Applying SMOTE before splitting the data causes data leakage. SMOTE generates synthetic samples based on the entire dataset, including the test set, so synthetic versions of test samples can appear in the training set. This inflates training accuracy artificially but does not improve the model's ability to generalize to unseen data, leading to poor recall on the true test set. Option B is incorrect because while overfitting due to lack of regularization can cause poor generalization, the specific pattern of high accuracy but very low recall is characteristic of data leakage from preprocessing before splitting. Option C is incorrect because accuracy is indeed a poor metric for imbalanced data, but the low recall (5%) indicates a fundamental issue with the model's ability to detect positives, which goes beyond metric choice. Option D is incorrect because logistic regression can be effective for imbalanced datasets when properly handled (e.g., with class weights or resampling); the problem here stems from the improper application of SMOTE, not the algorithm itself.

Answer

The model is overfitting due to lack of regularization

Answer

Accuracy is not a suitable metric for imbalanced data

Answer

Logistic regression is inappropriate for imbalanced datasets

Question 12

A data scientist needs to analyze a dataset stored in Amazon S3 as CSV files. The dataset contains 100 columns, and the data scientist wants to quickly understand the distribution of each column, including missing values, data types, and basic statistics. Which AWS service is best suited for this task?

Accepted Answer

AWS Glue DataBrew. AWS Glue DataBrew (Option A) is correct because it provides visual data profiling and preparation without writing code, allowing users to quickly understand distributions, missing values, data types, and basic statistics. Option B (Amazon SageMaker Data Wrangler) is designed for data preparation and feature engineering within the SageMaker ecosystem, but it is more focused on transforming data for machine learning rather than initial exploratory analysis. Option C (Amazon QuickSight) is a business intelligence tool for creating visualizations and dashboards, not for profiling raw datasets. Option D (Amazon Athena) is an interactive query service that can analyze data in S3 using SQL, but it does not offer built-in data profiling capabilities for quick exploration.

Answer

Amazon SageMaker Data Wrangler

Answer

Amazon QuickSight

Answer

Amazon Athena

Question 13

A data scientist is using Amazon SageMaker to train a model. The training dataset contains missing values in several features. The data scientist wants to impute missing values using the median of each feature. Which approach is most appropriate?

Accepted Answer

Compute the median of each feature on the training set only, then impute both training and test sets using that median. Computing the median on the training set only avoids data leakage, and applying that median to both training and test sets ensures consistent imputation without using test set information. Option A is incorrect because dropping rows with missing values discards potentially useful data and is not imputation. Option B is incorrect because computing the median on the entire dataset before splitting introduces data leakage, as the test set influences the imputation values. Option C is incorrect because imputing with zero is arbitrary and does not use the median; also, doing it before splitting would use the entire dataset, causing leakage.

Answer

Drop all rows that contain missing values

Answer

Compute the median on the entire dataset, then split into training and test sets

Answer

Impute missing values with zero for all features before splitting

Question 14

A company has a large dataset of customer transactions stored in Amazon Redshift. A data scientist wants to perform EDA using Python libraries like pandas and matplotlib. The dataset is too large to fit into memory on a single EC2 instance. What is the most efficient approach?

Accepted Answer

Use Amazon Athena Federated Query to run SQL queries against Redshift and retrieve aggregated results. Amazon Athena Federated Query can query data in Amazon Redshift directly, allowing the data scientist to run SQL queries that aggregate the data before returning results. This avoids moving the entire dataset and reduces memory usage. Option A is wrong because even with a large EBS volume, the data must still be loaded into memory (pandas DataFrame) on the notebook instance, which may not fit. Option C is wrong because using SQLAlchemy to read the entire table into a pandas DataFrame would require loading all data into memory, causing an out-of-memory error. Option D is wrong because exporting to S3 and then reading with pandas still requires loading the entire dataset into memory, which is inefficient for large datasets.

Answer

Launch an Amazon SageMaker notebook instance with an attached EBS volume large enough to hold the data

Answer

Use a SQLAlchemy connection to read the entire table into a pandas DataFrame and sample it

Answer

Export the Redshift table to Amazon S3 in Parquet format, then use pandas to read the Parquet files

Question 15

During exploratory data analysis, a data scientist notices that a feature has a highly skewed distribution. Which transformation is most likely to make the distribution approximately normal?

Accepted Answer

Log transformation. Log transformation is commonly used to reduce right skewness and make the distribution approximately normal. Option B (min-max scaling) is incorrect because it does not change the shape of the distribution. Option C (one-hot encoding) is incorrect because it is used for categorical variables, not for transforming continuous skewed data. Option D (standardization) is incorrect because it does not change the shape of the distribution.

Answer

Min-max scaling

Answer

One-hot encoding

Answer

Standardization (z-score)