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

Question 1

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?

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

Accepted Answer

Amazon SageMaker Data Wrangler. Options D and E are correct. Amazon SageMaker Data Wrangler provides a visual interface for data preparation and analysis with built-in transforms and visualizations directly on S3 data. Amazon QuickSight is a BI service that connects to S3 and creates dashboards with statistical summaries and visualizations. Option A (SageMaker Studio) is an IDE for ML development, not a direct analysis service without custom code. Option B (AWS Glue DataBrew) is a data preparation tool but requires some configuration and is not primarily for statistical summaries and visualizations. Option C (Athena) is a SQL query engine for querying data, but does not provide built-in visualizations.

Answer

Amazon SageMaker Studio

Answer

AWS Glue DataBrew

Answer

Amazon Athena

Question 2

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 C, D, and E are correct. The chi-square test of independence is used to test for association between two categorical variables, such as a categorical feature and a binary target. Cramér's V is a measure of association derived from chi-square, indicating the strength of association. Mutual information is a non-parametric measure that captures dependency between variables, including non-linear relationships, and is suitable for categorical data. Option A (ANOVA) is used for comparing means across groups and is appropriate for a continuous dependent variable, not a binary target. Option B (Pearson correlation coefficient) measures linear correlation between two continuous variables and is not suitable for categorical data.

Answer

ANOVA

Answer

Pearson correlation coefficient

Question 3

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, B, and E are correct. Using COUNT(DISTINCT column) (A) is a precise way to count distinct values, and while it scans the column, it avoids fetching unnecessary data. Filtering with a WHERE clause on the partition column (B) limits the data scanned to only the relevant partitions, significantly reducing cost and improving performance. Using a columnar format like Parquet (E) reduces I/O by reading only the required columns. Option C (ORDER BY) is incorrect because it requires sorting the entire result set, increasing processing time and cost. Option D (SELECT *) is incorrect as it retrieves all columns, negating the benefits of columnar storage and increasing data scanned.

Answer

Use ORDER BY to sort the results.

Answer

Use SELECT * to retrieve all columns.

Question 4

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

Accepted Answer

2, 4, 4. The query returns COUNT(col2)=2 (only rows 1 and 3 have non-null col2), COUNT(*)=4 (total rows), COUNT(DISTINCT col1)=4 (distinct values A, B, C, D). Option A is wrong because COUNT(col2) is 2, not 3. Option B 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 5

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.. 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 6

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.. The most effective because it combines partitioning by date (derived from timestamp) and converting to Parquet format. Partitioning by date enables partition pruning for queries filtering on 'timestamp', drastically reducing the amount of data scanned. Parquet provides columnar storage and compression, further minimizing I/O and cost. Option C (Parquet without partitioning) still requires full file scans when filters are applied. Option B (random sampling) sacrifices accuracy for speed, which is undesirable for accurate EDA. Option D (partitioning by device_type) helps only for device_type filters, not for the common timestamp filters mentioned in the scenario.

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 7

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.. The F1-score balances precision and recall, making it a suitable metric for imbalanced datasets where accuracy would be misleading due to the majority class dominating. Option A (downsampling) is not ideal because it discards potentially useful data from the majority class. Option B (random oversampling) can lead to overfitting by duplicating minority class instances, and is not a guaranteed solution. Option C is incorrect because accuracy is not reliable when classes are imbalanced, as high accuracy can be achieved by simply predicting the majority class.

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 8

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 9

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 10

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.. It addresses the memory issue by reading the data in chunks using the chunksize parameter, allowing processing without loading the entire dataset into memory. It computes per-chunk statistics and aggregates them, which is efficient for EDA. For high-cardinality categorical columns, it uses value_counts() with dropna=False to capture missing values, and then plots the top 20 categories, which is manageable and insightful. This approach stays within pandas and the existing SageMaker notebook without requiring additional services or changing the dataset. Option A is incorrect because launching a separate PySpark job adds complexity and extra cost, and is not the most efficient for an ad-hoc EDA. Option B (S3 Select) can reduce the data volume but cannot natively perform complex aggregations like distribution across categories without pulling all rows; it is more suited for simple filtering. Option C (SageMaker Data Wrangler) is a separate service that requires additional setup and is overkill for this simple EDA task; it also modifies the workflow and is not the most efficient for immediate analysis.

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 11

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.. Dropping one of the highly correlated features reduces redundancy and mitigates multicollinearity. Option A (PCA) creates orthogonal components, which addresses multicollinearity but reduces interpretability; dropping a feature is more straightforward. Option B (Min-Max scaling) does not address collinearity at all. 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 12

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. (F1 score) is the correct metric because it balances precision and recall, providing a single measure that is robust to class imbalance. With only 1% positive class, accuracy (Option B) is misleadingly high due to the majority class. AUC-ROC (Option C) can still be high even if recall is low, as it evaluates ranking rather than absolute performance. Precision (Option D) only considers the proportion of correct positive predictions, ignoring false negatives, which is not suitable when recall is poor. The F1 score captures both aspects, making it the best choice for evaluating minority class performance in this scenario.

Answer

Accuracy

Answer

AUC-ROC

Answer

Precision

Question 13

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)

Question 14

A data scientist has a dataset with 500 features and wants to reduce dimensionality for visualization. Which technique is most appropriate for identifying the two components that capture the most variance?

Accepted Answer

Principal Component Analysis (PCA). Principal Component Analysis (PCA) is a linear dimensionality reduction technique that finds the directions (principal components) of maximum variance in the data, making it ideal for identifying the two components that capture the most variance for visualization. Option A is incorrect because t-SNE is a non-linear technique focused on preserving local neighborhood structure, not on maximizing global variance. Option B is incorrect because Linear Discriminant Analysis (LDA) is a supervised method that requires class labels to separate classes, not to maximize variance. Option D is incorrect because K-means clustering is an unsupervised algorithm for grouping data points, not a dimensionality reduction method.

Answer

t-Distributed Stochastic Neighbor Embedding (t-SNE)

Answer

Linear Discriminant Analysis (LDA)

Answer

K-means clustering

Question 15

A data engineer runs a SQL query on Amazon Athena to explore a dataset stored in S3 as CSV. The query returns zero rows for a column that should have numeric values. Which step should the engineer take to diagnose the issue?

Accepted Answer

Check the table schema in AWS Glue Data Catalog to ensure the column data type is correct.. Checking the table schema in the AWS Glue Data Catalog helps identify data type mismatches. If Athena returns zero rows for a numeric column, it may be because the column's data type in the catalog is incorrect (e.g., string instead of int). Fixing the schema to match the actual data allows Athena to parse the values correctly. Option A is incorrect because encryption does not affect query results. Option B is incorrect because running a crawler will only re-infer the schema, which may not solve the issue if the underlying data format is inconsistent. Option C is incorrect because partitioning is unrelated to data type issues.

Answer

Verify that the S3 bucket has encryption enabled.

Answer

Run an AWS Glue crawler to update the table schema.

Answer

Add a partition to the table for the date column.