MLS-C01 Exploratory Data Analysis Practice Test 16 — 15 Questions

Question 1

A data scientist is performing exploratory data analysis on a dataset containing customer transactions. The dataset has a column 'transaction_date' with timestamps in string format. Which AWS service can be used to parse the timestamps and extract features like day of week and hour?

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

Accepted Answer

AWS Glue. AWS Glue is a serverless ETL service with built-in transforms and a Data Catalog, and it can parse string timestamps using Spark/Python date functions (e.g., to_timestamp, date_format) to extract features like day of week and hour. It is the natural choice for preparing and transforming transaction data at scale before analysis or ML.

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Amazon Athena

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Amazon SageMaker Studio

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AWS Data Pipeline

Question 2

A machine learning engineer is analyzing a dataset with high cardinality categorical features. They want to reduce the number of categories by grouping rare categories into an 'Other' category. Which Amazon SageMaker processing job capability is best suited for this task?

Accepted Answer

Amazon SageMaker Processing. Amazon SageMaker Processing allows you to run custom data processing scripts (e.g., using pandas) that can handle grouping rare categories into 'Other' based on frequency thresholds. Option B (Data Wrangler) is a visual tool that may not offer the same level of customization for complex grouping logic. Option C (AWS Glue Studio) is a visual ETL tool but lacks tight integration with SageMaker and may be less efficient for this specific task. Option D (Autopilot) is designed for automated model building, not custom data processing.

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Amazon SageMaker Data Wrangler

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AWS Glue Studio

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Amazon SageMaker Autopilot

Question 3

A data analyst needs to visualize the distribution of a numerical feature in a dataset. Which AWS service can be used to create a histogram directly from data stored in S3 without writing code?

Accepted Answer

Amazon QuickSight. Amazon QuickSight is a fully managed business intelligence service that allows users to create visualizations, including histograms, from data stored in Amazon S3 without writing code. It provides a drag-and-drop interface where you can connect to S3, import data, and build charts. QuickSight's SPICE engine enables fast, in-memory analysis, and it supports direct query to S3 via Athena or other sources. This makes it the ideal choice for a no-code visualization of a numerical feature's distribution.

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Amazon Athena

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Amazon SageMaker Studio

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AWS Glue

Question 4

A team is exploring a dataset with missing values in multiple columns. They want to decide whether to drop rows or impute values. Which approach is most appropriate for exploratory data analysis?

Accepted Answer

Analyze the missing data pattern using visualizations and summary statistics. During EDA, the goal is to understand the nature and pattern of missingness before deciding on a treatment. Visualizing missingness (e.g., missingno matrix, heatmap) and computing summary statistics (per-column null counts, correlation of missingness with other features) reveals whether data is MCAR, MAR, or MNAR — which dictates whether dropping or imputing is appropriate. Jumping straight to imputation or row deletion without this diagnostic step risks introducing bias or discarding valuable signal.

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Impute missing values with the mean of each column

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Drop all rows with missing values to ensure data quality

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Use Amazon SageMaker Data Wrangler to automatically impute missing values

Question 5

A company has customer feedback data stored in CSV files in S3. The data includes a 'feedback_text' column. Which AWS service is best suited for performing sentiment analysis as part of exploratory data analysis?

Accepted Answer

Amazon Comprehend. Amazon Comprehend is a fully managed NLP service that includes a built-in sentiment analysis API (DetectSentiment) which classifies text as POSITIVE, NEGATIVE, NEUTRAL, or MIXED. It is purpose-built for analyzing customer feedback text stored in S3, and integrates natively with S3 and Lambda for batch or real-time processing. This makes it the correct choice for sentiment analysis during exploratory data analysis.

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Amazon Rekognition

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Amazon Textract

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Amazon Lex

Question 6

During EDA, a data scientist finds that a feature has a skewed distribution. They want to apply a log transformation to make it more Gaussian-like. Which Amazon SageMaker feature is most appropriate for this transformation?

Accepted Answer

Amazon SageMaker Data Wrangler. Amazon SageMaker Data Wrangler is designed for data preparation and feature engineering, including built-in transforms like log transformation to reduce skewness and make distributions more Gaussian-like. It provides a visual interface to apply, preview, and export these transformations directly into a SageMaker pipeline.

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Amazon SageMaker Clarify

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Amazon SageMaker JumpStart

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Amazon SageMaker Ground Truth

Question 7

A data analyst wants to check for duplicate rows in a dataset stored in S3. Which AWS service can be used to run a SQL query to count duplicates without moving the data?

Accepted Answer

Amazon Athena. Amazon Athena is a serverless interactive query service that allows running standard SQL queries directly on data stored in Amazon S3, without needing to move the data. It can easily count duplicate rows using SQL GROUP BY and HAVING clauses. Option B (Amazon Redshift Spectrum) is wrong because although it can query data in S3, it requires an active Redshift cluster, which is unnecessary for this simple ad-hoc query. Option C (AWS Glue) is wrong because Glue is an ETL service for data preparation and cataloging, not a query engine. Option D (Amazon SageMaker Studio) is wrong because it is an integrated development environment for machine learning, not a SQL query service.

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Amazon Redshift Spectrum

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Amazon SageMaker Studio

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AWS Glue

Question 8

Which TWO of the following are appropriate techniques for handling missing data during exploratory data analysis? (Select TWO.)

Accepted Answer

Impute missing values using mean or median for numerical features. Option C is correct because imputing missing numerical values with the mean or median is a standard, well-established EDA technique that preserves sample size and keeps distributions usable for analysis, with the median being preferable when the data is skewed or contains outliers. Option D is correct because visualizing missingness patterns with heatmaps (e.g., missingno's matrix or heatmap) or bar charts of missing-value counts is a fundamental EDA step that reveals whether missingness is random, systematic, or concentrated in particular features, guiding the choice of imputation or exclusion strategy. Option A is not appropriate because ignoring missing values silently biases results and many algorithms cannot handle NaN inputs at all. Option B is not appropriate because substituting -1 conflates a sentinel value with real data, distorts distributions and statistics, and can mislead distance-based or linear models. Option E is not appropriate because deleting every row with any missing value causes substantial data loss, reduces statistical power, and can introduce bias unless the missingness is provably MCAR and the proportion is tiny.

Answer

Ignore missing values and proceed with modeling

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Replace missing values with -1 to indicate missing

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Delete all rows with any missing values

Question 9

Which THREE of the following are best practices for feature engineering during EDA? (Select THREE.)

Accepted Answer

Apply log transformation to highly skewed features. Applying a log transformation to highly skewed features helps normalize their distribution, reducing the impact of extreme values and making the data more suitable for many machine learning algorithms that assume normally distributed features. This is a common technique during exploratory data analysis (EDA) to stabilize variance and improve model performance, especially for linear models and neural networks.

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Remove all outliers from the dataset

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Standardize all features to have zero mean and unit variance

Question 10

Which TWO AWS services can be used to visualize data distributions as part of exploratory data analysis? (Select TWO.)

Accepted Answer

Amazon QuickSight. Amazon QuickSight is a cloud-native business intelligence service that can visualize data distributions through histograms, box plots, scatter plots, and other chart types, making it suitable for exploratory data analysis. Amazon SageMaker Data Wrangler provides a visual interface to create data distribution charts (e.g., histograms, bar charts) directly within the data preparation workflow, enabling quick inspection of feature distributions before model building.

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AWS Glue

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Amazon Athena

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Amazon Comprehend

Question 11

An Athena query SELECT COUNT(*) FROM table WHERE col1 IS NULL returns the value 5000. What does this value represent?

Accepted Answer

The number of rows where col1 is NULL. The exhibit shows the result of an Athena query that counts the number of rows where col1 is NULL. The value 5000 is that count. Therefore, Option B is correct. Option A is incorrect because the query does not count total rows; it filters for NULLs. Option C is incorrect because the query counts NULL rows, not non-NULL. Option D is incorrect because the query does not use DISTINCT to count distinct values.

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The total number of rows in the table

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The number of rows where col1 is not NULL

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The number of distinct values in col1

Question 12

A data scientist is performing exploratory data analysis on a dataset containing customer transactions. The dataset has a column 'transaction_date' with timestamps in string format. Which AWS service can be used to parse the timestamps and extract features like day of week and hour?

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

Accepted Answer

Impute with the median income. Imputing with the median is most appropriate because the income distribution is right-skewed. The median is robust to outliers and extreme values, so it preserves the central tendency and the skewness of the distribution better than the mean, which is pulled upward by high-income outliers. This keeps the imputed values representative of the typical income in the dataset.

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Impute with the mean income

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Drop rows with missing income

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Impute with the mode income

Question 13

A company has a dataset with 1 million rows and 500 features. They want to reduce dimensionality for visualization. Which technique is most suitable for preserving global structure?

Accepted Answer

Principal Component Analysis (PCA). PCA is the most suitable technique for reducing dimensionality while preserving global structure. It performs a linear projection onto the directions of maximum variance, which retains the overall geometry and relationships among all points. For a 1M x 500 dataset used for visualization, PCA is fast, deterministic, and preserves global structure far better than t-SNE, which prioritizes local neighborhoods.

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Autoencoder

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t-Distributed Stochastic Neighbor Embedding (t-SNE)

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Linear Discriminant Analysis (LDA)

Question 14

A data scientist is analyzing a dataset with many categorical features. The target variable is binary. Which statistical test should be used to assess the association between each categorical feature and the target?

Accepted Answer

Chi-squared test of independence. The Chi-squared test of independence is the appropriate test to assess association between two categorical variables. Here, both the features (categorical) and the target (binary, which is categorical) are categorical, making the Chi-squared test the correct choice. Option A (Pearson correlation) is for continuous variables, not categorical. Option C (ANOVA) compares means across groups for a continuous target, but our target is binary (categorical). Option D (Kolmogorov-Smirnov test) compares distributions of continuous variables, not categorical. Therefore, B is correct.

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Pearson correlation coefficient

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ANOVA

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Kolmogorov-Smirnov test

Question 15

A company is performing EDA on a dataset with 10,000 rows and 200 columns. They run a correlation matrix and find many high correlations (|r| > 0.9). What is the best approach to address multicollinearity before modeling?

Accepted Answer

Calculate Variance Inflation Factor (VIF) and remove features with VIF > 10. Variance Inflation Factor (VIF) quantifies how much a feature is correlated with others; removing features with VIF > 10 reduces multicollinearity. Option A is incorrect because standardizing (scaling) does not remove correlations between features. Option C is incorrect because Lasso regression can handle multicollinearity through regularization, but it is a modeling technique and not the best first step during EDA for identifying and removing redundant features. Option D is incorrect because PCA creates orthogonal (uncorrelated) components, which eliminates multicollinearity, but it reduces interpretability and is a dimensionality reduction technique applied during modeling, not an EDA step for directly addressing multicollinearity in the original features.

Answer

Standardize all features

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Use Lasso regression with cross-validation

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Apply Principal Component Analysis (PCA) to all features