MLS-C01 Exploratory Data Analysis Practice Test 26 — 15 Questions

Question 1

A data scientist is building a model to predict housing prices using a dataset with 100,000 records and 50 features. The features include 'sqft_living', 'sqft_lot', 'bedrooms', 'bathrooms', 'floors', 'waterfront', 'view', 'condition', 'grade', etc. The data scientist uses Amazon SageMaker Data Wrangler for EDA. Upon reviewing the data, the data scientist finds that 'sqft_living' has a correlation of 0.7 with 'sqft_above' (square footage above ground) and 0.6 with 'sqft_basement'. Also, 'grade' (overall grade of the house) is highly correlated with 'condition' (0.8). The target variable 'price' is right-skewed. The data scientist plans to use a linear regression model. Which set of actions should the data scientist take to improve model performance?

Accepted Answer

Apply log transformation to the target variable 'price' to reduce skewness, and remove either 'sqft_above' or 'sqft_living' and either 'grade' or 'condition' to handle multicollinearity.. Option B is correct because log-transforming the target addresses skewness, and removing or combining highly correlated features reduces multicollinearity. Option A is wrong because removing all correlated features may discard predictive power. Option C is wrong because standard scaling does not fix skewness or multicollinearity. Option D is wrong because PCA on all features may lose interpretability and does not target the specific issues.

Answer

Apply standard scaling to all numeric features and use the data as is, since linear regression is robust to multicollinearity.

Answer

Remove all features that have correlation >0.5 with any other feature to eliminate multicollinearity, and apply standard scaling to all numeric features.

Answer

Apply principal component analysis (PCA) to all features to reduce dimensionality, and then fit linear regression on the principal components.

Question 2

A data scientist is analyzing a dataset of online retail transactions. The dataset contains 500,000 rows and 10 columns: 'TransactionID', 'CustomerID', 'ProductID', 'Quantity', 'UnitPrice', 'TransactionDate', 'PaymentMethod', 'ShippingAddress', 'Country', and 'TotalAmount'. The data scientist loads the data into a SageMaker notebook and performs initial EDA. The data scientist finds that 'UnitPrice' has a range from $0.01 to $10,000, with a mean of $50 and a median of $20. 'Quantity' ranges from -10 to 100, with negative values indicating returns. 'TotalAmount' is calculated as Quantity * UnitPrice. The data scientist also notices that 2% of the 'CustomerID' values are missing, and 1% of 'ProductID' values are missing. There are no missing values in other columns. The data scientist wants to clean the data and prepare it for customer segmentation. Which course of action is most appropriate?

Accepted Answer

Remove rows with negative 'Quantity' to focus on purchases. Impute missing 'CustomerID' and 'ProductID' with a placeholder such as 'Unknown'.. Option A is correct because negative quantities are returns and should be removed if the goal is to model purchase behavior, and missing CustomerID and ProductID can be imputed with 'Unknown' to avoid data loss. Option B is wrong because mean imputation for CustomerID is not valid (categorical). Option C is wrong because removing all rows with any missing values would discard 3% of data. Option D is wrong because negative quantities are meaningful as returns, not errors.

Answer

Impute missing 'CustomerID' with the mean of 'CustomerID' and missing 'ProductID' with the mode.

Answer

Remove all rows with any missing values.

Answer

Keep negative 'Quantity' and treat them as errors; replace them with the median of positive quantities.

Question 3

A data scientist is analyzing a dataset with 500 features and notices that many features are highly correlated. Which AWS service can be used to automatically reduce dimensionality by identifying and removing redundant features before training a model?

Accepted Answer

Amazon SageMaker Data Wrangler. Amazon SageMaker Data Wrangler provides built-in transformations including correlation analysis and dimensionality reduction. Option A is wrong because QuickSight is for visualization, not feature reduction. Option B is wrong because Glue is for ETL but lacks automatic dimensionality reduction. Option D is wrong because Athena is a query service.

Answer

AWS Glue

Answer

Amazon QuickSight

Answer

Amazon Athena

Question 4

A data scientist is performing exploratory data analysis on a dataset stored in Amazon S3 using Amazon SageMaker Studio. The dataset has missing values in several columns. Which approach is the MOST efficient way to handle missing values within SageMaker Studio?

Accepted Answer

Use SageMaker Data Wrangler to impute missing values with mean, median, or mode.. SageMaker Data Wrangler provides a visual interface to handle missing values. Option B is wrong because writing a custom script is less efficient. Option C is wrong because Glue is external. Option D is wrong because running a notebook from the local machine is not efficient.

Answer

Run a Jupyter notebook on a local machine to clean the data and upload back to S3.

Answer

Use AWS Glue to run a find-and-replace operation.

Answer

Write a custom Python script using pandas to drop rows with missing values.

Question 5

A data scientist is analyzing a dataset with a target variable that is highly imbalanced (99% negative class, 1% positive class). The dataset has 10 million rows. The goal is to train a binary classifier. Which technique should be applied during exploratory data analysis to best address the imbalance?

Accepted Answer

Random undersampling of the majority class. For large datasets, undersampling the majority class is feasible and can be effective. Option A is wrong because SMOTE generates synthetic samples but may be computationally expensive for 10M rows. Option B is wrong because class weights are set during training, not EDA. Option D is wrong because collecting more data is not guaranteed to fix imbalance.

Answer

Assign higher class weights to the minority class

Answer

Synthetic Minority Oversampling Technique (SMOTE)

Answer

Collect more data for the minority class

Question 6

During exploratory data analysis, a data scientist notices that the distribution of a continuous feature is heavily right-skewed. Which transformation should be applied to make the distribution more symmetric for linear regression?

Accepted Answer

Log transformation. Log transformation is commonly used for right-skewed data. Option A is wrong because min-max scaling does not change distribution shape. Option B is wrong because standardization does not fix skewness. Option D is wrong because one-hot encoding is for categorical features.

Answer

Standardization (z-score)

Answer

One-hot encoding

Answer

Min-max scaling

Question 7

A data scientist is performing EDA on a dataset with 1,000 features. The goal is to select the most important features for a regression model. Which technique can be used to rank feature importance quickly?

Accepted Answer

Calculate the correlation coefficient of each feature with the target. Correlation analysis with the target variable is a quick way to rank features. Option B is wrong because PCA is unsupervised and does not rank features by importance to target. Option C is wrong because t-SNE is for visualization. Option D is wrong because k-means is clustering.

Answer

Use t-SNE to visualize feature relationships

Answer

Run k-means clustering and use cluster centroids

Answer

Apply Principal Component Analysis (PCA) and examine component loadings

Question 8

A data scientist is analyzing a dataset with a timestamp column and several numeric measurements. The goal is to detect seasonality and trends. Which AWS service can be used directly from SageMaker Studio to perform this analysis without writing code?

Accepted Answer

Amazon SageMaker Data Wrangler. SageMaker Data Wrangler includes built-in time series analysis capabilities such as seasonality detection. Option A is wrong because QuickSight ML Insights requires data to be in QuickSight. Option B is wrong because Glue DataBrew is for data preparation but not specifically for time series analysis. Option D is wrong because Forecast is for forecasting, not exploratory analysis.

Answer

Amazon Forecast

Answer

Amazon QuickSight ML Insights

Answer

AWS Glue DataBrew

Question 9

A data scientist is working with a dataset that contains both numerical and categorical features. During EDA, they want to understand the relationship between a categorical feature with 10 unique values and the target variable. Which visualization is most appropriate?

Accepted Answer

Box plot. A box plot shows the distribution of the target across categories. Option A is wrong because scatter plots are for two numerical features. Option B is wrong because histogram shows distribution of a single numerical feature. Option D is wrong because heatmaps typically show correlation between numerical features.

Answer

Heatmap

Answer

Histogram

Answer

Scatter plot

Question 10

A data scientist needs to profile a large dataset in Amazon S3 to understand its schema, data types, and quality. Which AWS service can automatically generate a data profile with statistics and visualizations?

Accepted Answer

AWS Glue DataBrew. AWS Glue DataBrew provides data profiling capabilities. Option B is wrong because Athena is a query service. Option C is wrong because Redshift is a data warehouse. Option D is wrong because QuickSight is for visualization after data is prepared.

Answer

Amazon Athena

Answer

Amazon QuickSight

Answer

Amazon Redshift

Question 11

A data scientist is performing EDA on a dataset with 1 million rows and 50 features. The dataset includes a column 'user_id' with unique identifiers, a column 'event_date' with timestamps, and other columns. Which TWO actions should the data scientist take to understand data quality issues?

Accepted Answer

Analyze missing value patterns across columns. Checking for duplicate rows and analyzing missing values are fundamental steps in EDA. Option B is wrong because dropping user_id before analysis may lose information. Option C is wrong because training a model is not part of EDA. Option D is wrong because PCA is not for data quality.

Answer

Drop the 'user_id' column to reduce dimensionality

Answer

Use PCA to reduce dimensions and visualize

Answer

Train a random forest model to identify feature importance

Question 12

A data scientist is analyzing a dataset with a binary target variable. The dataset has 50,000 rows and 200 features. The data scientist wants to identify which features are most predictive. Which TWO methods are appropriate for feature selection during EDA?

Accepted Answer

Mutual information. Chi-squared test is for categorical features and mutual information can handle both. Option C is wrong because PCA is unsupervised. Option D is wrong because k-means is clustering. Option E is wrong because LSTM is a model, not for EDA.

Answer

LSTM neural network

Answer

K-means clustering

Answer

Principal Component Analysis (PCA)

Question 13

A data scientist is performing EDA on a dataset with 10 million rows. The dataset has a column 'income' with outliers. The data scientist wants to detect and handle outliers. Which THREE approaches are appropriate?

Accepted Answer

Calculate z-scores and flag values beyond 3 standard deviations. IQR method, z-score, and visualization like box plots are standard for outlier detection. Option D is wrong because min-max scaling does not handle outliers. Option E is wrong because one-hot encoding is for categorical data.

Answer

Apply min-max scaling to the column

Answer

Convert the column to one-hot encoding

Question 14

Refer to the exhibit. A data scientist is using an IAM role with this policy to run a SageMaker processing job that reads data from S3. The job fails with an access error. What is the most likely cause?

Accepted Answer

The policy does not allow s3:ListBucket. The processing job needs both s3:GetObject and s3:ListBucket to read objects. The policy lacks s3:ListBucket. Option A is wrong because sagemaker:CreateProcessingJob is allowed. Option B is wrong because the policy allows s3:GetObject. Option D is wrong because s3:PutObject is not needed for reading.

Answer

The policy does not allow sagemaker:CreateProcessingJob

Answer

The policy does not allow s3:PutObject

Answer

The policy does not allow s3:GetObject

Question 15

A data scientist is analyzing a dataset stored in Amazon S3 (100 GB, CSV format) using Amazon SageMaker Studio. The dataset contains 500 columns and 10 million rows. The data scientist wants to understand the distribution of each column, detect missing values, and identify outliers. However, the SageMaker Studio notebook instance runs out of memory when loading the entire dataset into a pandas DataFrame. The data scientist needs to complete the EDA efficiently without modifying the source data. What should the data scientist do?

Accepted Answer

Use Amazon SageMaker Data Wrangler to create a data flow that samples and profiles the data.. Using SageMaker Data Wrangler allows processing data in a distributed manner without loading everything into memory. Option A is wrong because increasing instance size may still not be enough and is costly. Option B is wrong because Glue ETL is more complex and not integrated with Studio. Option D is wrong because sampling may miss important data.

Answer

Write a script that loads only a random 10% sample of rows to reduce memory usage.

Answer

Use AWS Glue ETL to transform the data into Parquet format and then load into pandas.

Answer

Launch a larger notebook instance with more memory (e.g., ml.r5.24xlarge) and reload the data.