MLS-C01 Exploratory Data Analysis Practice Test 14 — 15 Questions

Question 1

A data analyst is using Amazon QuickSight to explore a dataset with 10 million rows. The analyst wants to create a histogram of a numerical column. However, the query is taking too long. Which action should the analyst take to improve performance without losing accuracy?

Accepted Answer

Import the dataset into SPICE (Super-fast, Parallel, In-memory Calculation Engine).. Option A is correct because using SPICE in-memory engine speeds up queries by caching data. Option B is wrong because sampling loses accuracy. Option C is wrong because reducing bin count is a trade-off. Option D is wrong because Athena is query engine, but QuickSight already uses it; SPICE is better.

Answer

Change the data source to Amazon Athena directly with a limit clause.

Answer

Reduce the number of bins in the histogram.

Answer

Use a sample of the data (e.g., 1 million rows) for the histogram.

Question 2

A data scientist is performing EDA on a dataset with mixed data types (numerical, categorical, text). The dataset is stored in S3. Which TWO AWS services can be used to directly perform statistical summaries and visualizations without writing custom code?

Accepted Answer

Amazon SageMaker Data Wrangler. Options A and D are correct. SageMaker Data Wrangler provides a visual interface for data preparation and analysis with built-in transforms and visualizations. QuickSight is a BI service that can connect to S3 data and create dashboards with statistical summaries. Option B is wrong because Athena is primarily SQL query engine, not visualization. Option C is wrong because Glue DataBrew is for data preparation but requires some configuration. Option E is wrong because SageMaker Studio is an IDE, not a direct analysis service.

Answer

Amazon SageMaker Studio

Answer

AWS Glue DataBrew

Answer

Amazon Athena

Question 3

A data scientist is analyzing a dataset with several categorical features and a binary target. The scientist wants to check for association between each categorical feature and the target. Which THREE statistical tests are appropriate?

Accepted Answer

Chi-square test of independence. Options A, B, and D are correct. Chi-square test of independence is for categorical-categorical association. Cramér's V is a measure of association based on chi-square. Mutual information is a non-parametric measure that can capture non-linear dependencies. Option C is wrong because ANOVA is for categorical vs continuous. Option E is wrong because Pearson correlation is for continuous variables.

Answer

ANOVA

Answer

Pearson correlation coefficient

Question 4

A data engineer is exploring a large dataset in Amazon Athena. The dataset is partitioned by date and stored in Parquet format. The engineer wants to check the number of distinct values in a column for a specific date range. Which THREE practices reduce query cost and improve performance?

Accepted Answer

Use the COUNT(DISTINCT column) function.. Options A, C, and D are correct. Using partition filtering limits data scanned. Using COUNT(DISTINCT) is efficient but still scans; however, the question asks for reducing cost, so partition filtering is key. Option B is wrong because SELECT * scans all columns. Option E is wrong because ORDER BY without LIMIT requires full scan and sort.

Answer

Use ORDER BY to sort the results.

Answer

Use SELECT * to retrieve all columns.

Question 5

The exhibit shows an Athena query result from a table. What is the output of the query?

Accepted Answer

2, 4, 4. Option B is correct. COUNT(col2) counts non-null values in col2: rows 1 and 3 have non-null, so 2. COUNT(*) counts all rows: 4. COUNT(DISTINCT col1) counts distinct col1 values: A,B,C,D = 4. Option A is wrong because COUNT(col2) is 2, not 3. Option C is wrong because COUNT(*) is 4, not 3. Option D is wrong because COUNT(DISTINCT col1) is 4, not 3.

Answer

3, 3, 4

Answer

2, 3, 3

Answer

2, 4, 3

Question 6

A data scientist is working on a customer churn prediction project for a telecom company. The dataset contains 50,000 records with 25 features, including 'tenure' (number of months customer stayed), 'monthly_charges', 'total_charges', 'contract_type' (month-to-month, one year, two year), 'payment_method', and a target 'churn' (Yes/No). The data is stored in an S3 bucket as a single CSV file. The scientist uses Amazon SageMaker Data Wrangler to perform EDA. After importing the data, the scientist notices that the 'total_charges' column has many missing values (about 20% of rows). The scientist suspects that missing values occur only for customers with tenure = 0 (new customers). After verifying that suspicion, the scientist wants to handle the missing values appropriately. Which course of action should the scientist take?

Accepted Answer

Impute missing total_charges with 0, since missing values correspond to customers with tenure=0.. Option D is correct because if total_charges is missing only for tenure=0, it means those customers have not been billed yet, so total_charges should be 0. Imputing with 0 is appropriate. Option A is wrong because dropping rows with missing total_charges would remove all new customers, biasing the dataset. Option B is wrong because imputing with mean would assign incorrect values to new customers. Option C is wrong because using a model to predict missing values is overkill and may introduce error when the true value is known to be 0.

Answer

Use a regression model to predict total_charges based on other features.

Answer

Impute missing total_charges with the mean of non-missing values.

Answer

Drop all rows with missing total_charges to avoid bias.

Question 7

A data engineer is performing EDA on a dataset containing user activity logs from a mobile app. The dataset has 10 million rows and includes columns: 'user_id', 'event_type', 'timestamp', 'device_type', and 'session_duration'. The engineer uses Amazon Athena to query the data stored in S3 as CSV files. The engineer runs a query to find the average session_duration per device_type, but the query takes over 5 minutes and scans 100 GB of data. The engineer wants to reduce query cost and improve performance for future EDA. The dataset is not partitioned, and the engineer anticipates frequent queries filtering on 'timestamp' and 'device_type'. Which action will most effectively reduce data scanned?

Accepted Answer

Partition the table by date derived from timestamp and convert to Parquet.. Option C is correct because partitioning by date (derived from timestamp) allows partition pruning when filtering by timestamp, significantly reducing data scanned. Converting to Parquet (Option A) helps but without partitioning, full scans still occur. Option B is wrong because it only partitions by device_type, but time-based filters are common. Option D is wrong because sampling loses accuracy.

Answer

Use random sampling to query a subset of data.

Answer

Convert the data to Parquet format and use columnar storage.

Answer

Partition the table by device_type.

Question 8

A data scientist is analyzing a dataset of customer reviews for a retail company. The dataset contains text reviews, star ratings (1-5), and customer metadata. The scientist wants to perform sentiment analysis to classify reviews as positive or negative. During EDA, the scientist uses Amazon SageMaker Data Wrangler to visualize the distribution of star ratings and notices that 90% of reviews are 4 or 5 stars, while only 2% are 1 star. The scientist is concerned about class imbalance. Which approach should the scientist take to address the imbalance before modeling?

Accepted Answer

Use the F1-score as the evaluation metric to account for imbalance.. Option B is correct because using F1-score as the evaluation metric accounts for class imbalance better than accuracy. Option A is wrong because random oversampling can lead to overfitting and is not always best. Option C is wrong because downsampling the majority class loses valuable data. Option D is wrong because accuracy is misleading for imbalanced datasets.

Answer

Downsample the majority class to create a balanced dataset.

Answer

Use random oversampling of the minority class to balance the dataset.

Answer

Use accuracy as the evaluation metric since it is simple.

Question 9

A data scientist wants to understand the distribution and missing values in a large dataset stored in Amazon S3. Which TWO AWS services can be used directly for this exploratory data analysis? (Choose TWO.)

Accepted Answer

Amazon Athena. Amazon Athena allows SQL-based queries on S3 data, including aggregation for distribution analysis. AWS Glue DataBrew provides visual profiling to detect missing values and distributions. SageMaker Data Wrangler is also a valid choice but is not a direct service for S3 data without additional steps; EMR requires cluster setup.

Answer

Amazon SageMaker Data Wrangler

Answer

AWS CloudTrail

Answer

Amazon EMR

Question 10

A machine learning engineer is analyzing a dataset with 500 features and suspects multicollinearity. Which TWO techniques can help identify and address multicollinearity during exploratory data analysis? (Choose TWO.)

Accepted Answer

Calculate Variance Inflation Factor (VIF) for each feature. Variance Inflation Factor (VIF) measures how much the variance of a regression coefficient is inflated due to multicollinearity. Correlation matrix heatmap shows pairwise correlations. PCA reduces dimensionality but does not directly identify multicollinearity. Lasso regression addresses it via regularization but is a modeling step. t-SNE is for visualization of high-dimensional data.

Answer

Apply t-SNE for visualization

Answer

Apply Principal Component Analysis (PCA)

Answer

Use Lasso regression to select features

Question 11

A data scientist is performing exploratory data analysis on a time-series dataset of website traffic. The dataset contains hourly page views for the past two years. The scientist wants to analyze seasonality and trends. Which THREE techniques are appropriate for this analysis? (Choose THREE.)

Accepted Answer

Moving average smoothing. Decomposition separates time series into trend, seasonal, and residual components. Autocorrelation plot (ACF) helps identify seasonality. Moving average smooths to reveal trends. Linear regression is not typical for seasonal decomposition. Box plot by month can show seasonal patterns but is less common for trend.

Answer

Box plot by month

Answer

Linear regression on time index

Question 12

A data scientist is performing exploratory data analysis on a large dataset stored in Amazon S3 (100 GB, CSV format, 500 columns). The dataset contains customer transaction records with features such as transaction amount, timestamp, customer ID, and numerous categorical variables (e.g., product category, payment method, location). The scientist wants to understand the distribution of transaction amounts across different product categories and identify any outliers. They have an Amazon SageMaker notebook instance with a ml.t3.medium instance and are using pandas. However, when trying to load the entire dataset into a DataFrame using pd.read_csv('s3://bucket/data.csv'), the notebook crashes with a memory error. Additionally, the scientist suspects that some categorical columns have high cardinality (e.g., product category has thousands of unique values), and there are missing values in several columns. What is the MOST efficient approach to perform the EDA without modifying the original dataset or using additional AWS services? Options: A) Use the SageMaker SDK to launch a parallel processing job with PySpark and read the data into a Spark DataFrame, then compute statistics and visualize with matplotlib. B) Use pandas with chunksize parameter to iterate through the dataset in chunks, compute per-chunk statistics, and aggregate results; for high-cardinality columns, use value_counts() with dropna=False and then plot the top 20 categories. C) Use the S3 Select API to filter rows and columns before loading into pandas, reducing the data size; then use pandas for EDA. D) Use SageMaker Data Wrangler to import the dataset, create a flow to handle missing values and reduce cardinality, and export a sample to the notebook for analysis.

Accepted Answer

Use pandas with chunksize parameter to iterate through the dataset in chunks, compute per-chunk statistics, and aggregate results; for high-cardinality columns, use value_counts() with dropna=False and then plot the top 20 categories.. Option B is correct because it directly addresses the memory issue by processing data in chunks and handles high-cardinality categorical columns by focusing on top categories, all within the existing pandas environment without additional services. Option A requires PySpark which is not set up on the current instance and adds complexity. Option C, S3 Select, can reduce data size but cannot perform the aggregation needed (e.g., distribution across categories) without pulling all rows; it's more suitable for simple filtering. Option D, SageMaker Data Wrangler, is a separate service that requires additional setup and is not the most efficient for an ad-hoc EDA; it also modifies the workflow.

Answer

Use the SageMaker SDK to launch a parallel processing job with PySpark and read the data into a Spark DataFrame, then compute statistics and visualize with matplotlib.

Answer

Use the S3 Select API to filter rows and columns before loading into pandas, reducing the data size; then use pandas for EDA.

Answer

Use SageMaker Data Wrangler to import the dataset, create a flow to handle missing values and reduce cardinality, and export a sample to the notebook for analysis.

Question 13

A data scientist is exploring a dataset with 10 features and observes that the correlation between feature A and feature B is 0.98. Which action should be taken to address multicollinearity before training a linear regression model?

Accepted Answer

Remove one of the two features from the dataset.. Option C is correct because dropping one of the highly correlated features reduces redundancy and mitigates multicollinearity. Option A is wrong because scaling does not address collinearity. Option B is wrong because PCA creates orthogonal components but reduces interpretability; dropping a feature is more straightforward. Option D is wrong because adding polynomial features increases correlation.

Answer

Use Principal Component Analysis (PCA) to combine them.

Answer

Apply Min-Max scaling to both features.

Answer

Add polynomial features to both.

Question 14

A machine learning engineer trains a binary classifier on an imbalanced dataset where the positive class represents 1% of the data. After training, the model achieves 99% accuracy but only 10% recall on the positive class. Which metric should the engineer focus on to evaluate the model's performance on the minority class?

Accepted Answer

F1 score. Option B is correct because the F1 score balances precision and recall, which is suitable for imbalanced datasets. Option A is wrong because accuracy can be misleading with imbalance. Option C is wrong because precision alone ignores recall. Option D is wrong because AUC-ROC may still be high even with poor recall.

Answer

Accuracy

Answer

AUC-ROC

Answer

Precision

Question 15

During EDA, a data scientist plots the distribution of a numeric feature and observes that it is right-skewed. The feature will be used as input to a linear model. Which transformation should the data scientist apply?

Accepted Answer

Log transformation. A right-skewed distribution indicates that the feature has a long tail on the right, which can violate the linear model assumption of normally distributed errors. The log transformation compresses the high values and expands the low values, making the distribution more symmetric and stabilizing variance, which improves linear model performance.

Answer

Square transformation

Answer

One-hot encoding

Answer

Standardization (Z-score)