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

Question 1

Refer to the exhibit. A data scientist runs the AWS CLI command shown to explore the contents of an S3 bucket. The command returns an empty array. However, the data scientist knows there are objects larger than 1000 bytes in the bucket. What is the most likely reason for the empty result?

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

Accepted Answer

The AWS CLI is not configured with the correct region for the bucket. The command syntax is correct: `--query 'Contents[?Size > `1000`]'` uses backticks for the numeric literal as per JMESPath. The empty result indicates the CLI could not find matching objects. Since the data scientist knows objects larger than 1000 bytes exist, the most likely cause is that the CLI is configured with a default region different from the bucket's region. Running the command without an explicit `--region` parameter causes it to query the bucket in the wrong region, returning no results. Option A is incorrect because the backticks are valid. Option B is incorrect because `list-objects-v2` supports `--query`. Option C is incorrect because `--query` is supported. Option D is correct because a region mismatch would cause the CLI to look for the bucket in the wrong location, resulting in an empty array even though the bucket contains objects.

Answer

The query syntax is incorrect; backticks should not be used

Answer

The command should use list-objects instead of list-objects-v2

Answer

The --query parameter is not supported by list-objects-v2

Question 2

A data scientist is working on a project to predict customer churn for a telecom company. The dataset includes 50,000 records with 20 features, including customer demographics, account information, and service usage. The data scientist uses Amazon SageMaker Studio and loads the data into a pandas DataFrame. During EDA, the data scientist notices that the target variable 'churn' has only 10% positive cases. Additionally, several features have missing values: 'income' has 5% missing, 'age' has 2% missing, and 'total_charges' has 1% missing. The data scientist also observes that 'income' is highly skewed with a long right tail, and 'age' is moderately skewed. The data scientist wants to handle missing values and prepare the data for modeling. Which course of action is most appropriate?

Accepted Answer

Impute 'income' with median, 'age' with median, 'total_charges' with median, and use SMOTE to handle class imbalance after splitting the data.. Median imputation is robust to skewness (particularly for income and age), and SMOTE is applied after splitting to avoid data leakage and handle class imbalance. Option B is wrong because removing rows with missing values would discard roughly 8% of the data (5%+2%+1% with potential overlap), which is a significant loss of information; additionally, random oversampling may lead to overfitting. Option C is wrong because mode imputation is appropriate for categorical data, not for continuous features like income, age, and total_charges. Option D is wrong because mean imputation is sensitive to outliers and skewness, especially for income with a long right tail; also, stratified sampling only ensures proportional representation in train/test splits, it does not generate synthetic samples to address imbalance.

Answer

Remove all rows with any missing values, and use random oversampling to handle class imbalance.

Answer

Impute 'income' with mode, 'age' with mode, 'total_charges' with mode, and use SMOTE after splitting.

Answer

Impute all missing values with the mean of each column, and use stratified sampling to handle class imbalance.

Question 3

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.. Log-transforming the right-skewed target variable 'price' helps meet the normality assumption of linear regression residuals. Additionally, removing either 'sqft_above' or 'sqft_living' (correlated 0.7) and either 'grade' or 'condition' (correlated 0.8) reduces multicollinearity, which can destabilize coefficient estimates. Option A is incorrect because standard scaling does not address skewness or multicollinearity. Option B is incorrect because removing all features with correlation >0.5 is too aggressive and may discard useful information. Option C is incorrect because PCA reduces dimensionality but the components may be less interpretable, and log transformation is still needed for the target.

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 4

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'.. The most appropriate approach. Negative quantities represent returns, which should be removed when analyzing purchase behavior for customer segmentation. Imputing missing 'CustomerID' and 'ProductID' with a placeholder like 'Unknown' retains data without guessing categorical values. Option A is incorrect because mean imputation is not valid for categorical 'CustomerID'. Option B is incorrect because removing all rows with missing values would discard valuable data. Option C is incorrect because negative quantities are meaningful returns, not errors, and replacing them distorts the data.

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 5

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 efficiently within SageMaker Studio, allowing imputation with mean, median, or mode without writing custom code. Option A is inefficient because it requires moving data out of SageMaker. Option C uses an external service (AWS Glue) which adds complexity and overhead. Option D, while possible, is less efficient than using Data Wrangler's built-in capabilities.

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 6

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. Random undersampling of the majority class is a practical approach for large datasets like 10M rows to reduce class imbalance during EDA. Option A (assign higher class weights) is a modeling technique applied during training, not during EDA. Option C (SMOTE) generates synthetic samples but can be computationally expensive for 10M rows. Option D (collect more data) does not guarantee a balanced distribution and may not be feasible.

Answer

Assign higher class weights to the minority class

Answer

Synthetic Minority Oversampling Technique (SMOTE)

Answer

Collect more data for the minority class

Question 7

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 to reduce right skewness and make the distribution more symmetric. Standardization (z-score) does not change the shape of the distribution; it only centers and scales. One-hot encoding is for categorical features, not continuous. Min-max scaling also does not affect skewness; it rescales the range but preserves shape.

Answer

Standardization (z-score)

Answer

One-hot encoding

Answer

Min-max scaling

Question 8

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 (t-SNE) is used for visualization, not feature ranking. Option C (k-means clustering) is an unsupervised clustering method and does not provide feature importance. Option D (PCA) component loadings show variance contribution but are not a direct ranking of feature importance to the target.

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 9

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 is the correct choice because it integrates directly with SageMaker Studio and includes built-in time series analysis capabilities such as seasonality detection and trend analysis, all without writing code. Option A (Amazon Forecast) is a forecasting service that requires a separate workflow and is not directly usable for exploratory analysis from Studio. Option C (Amazon QuickSight ML Insights) is for visualization and anomaly detection but not for time series decomposition within SageMaker Studio. Option D (AWS Glue DataBrew) is a data preparation tool that does not provide native time series analysis features.

Answer

Amazon Forecast

Answer

Amazon QuickSight ML Insights

Answer

AWS Glue DataBrew

Question 10

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 is appropriate here because it displays the distribution of a numerical target variable across different categories of a categorical feature, allowing comparison of medians, quartiles, and outliers for each of the 10 categories. Option A is incorrect because a heatmap is typically used to show the correlation between numerical variables or the intensity of two categorical variables, not the relationship between a categorical feature and a target. Option C is incorrect because a histogram shows the distribution of a single numerical variable and cannot incorporate categorical groupings. Option D is incorrect because a scatter plot visualizes the relationship between two numerical variables, making it unsuitable when one variable is categorical.

Answer

Heatmap

Answer

Histogram

Answer

Scatter plot

Question 11

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, automatically generating a data profile with statistics and visualizations. Option A (Amazon Athena) is an interactive query service for analyzing data in S3 using standard SQL, but it does not generate data profiles. Option C (Amazon QuickSight) is a business analytics service for creating visualizations and dashboards, but it does not profile data automatically. Option D (Amazon Redshift) is a data warehouse, not a data profiling service.

Answer

Amazon Athena

Answer

Amazon QuickSight

Answer

Amazon Redshift

Question 12

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. Analyzing missing value patterns (A) is a fundamental EDA step to identify data quality issues such as incomplete records. Checking for duplicate rows based on 'user_id' and 'event_date' (B) helps ensure data integrity, as duplicates can skew analysis. Option C (dropping 'user_id') is premature; identifier columns can be useful for deduplication and merging. Option D (PCA) is a dimensionality reduction technique used later, not for initial data quality checks. Option E (training a model) is part of modeling, not EDA.

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 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. The correct approaches for detecting outliers in a dataset with 10 million rows are calculating z-scores (A), using IQR (E), and visualization with box plots (D). Z-scores flag values beyond 3 standard deviations, IQR identifies outliers as points below Q1-1.5*IQR or above Q3+1.5*IQR, and box plots provide a visual summary of the distribution. Min-max scaling (B) only transforms the data range and does not detect outliers. One-hot encoding (C) is for categorical variables, not outlier detection. Thus options A, D, and E are correct.

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 s3:PutObject is not needed for reading. Option D is wrong because the policy allows s3:GetObject.

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.. SageMaker Data Wrangler is purpose-built for EDA on large datasets; it automatically samples data and profiles columns without requiring the entire dataset to be loaded into memory. Option A (sampling 10% of rows) could work but risks missing critical patterns or outliers, and is less integrated than Data Wrangler. Option B (converting to Parquet with AWS Glue) adds complexity and still requires memory to load into pandas. Option C (larger instance) may still be insufficient and is more expensive. Data Wrangler provides a seamless, integrated experience within SageMaker Studio for efficient EDA.

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.