MLS-C01 Exploratory Data Analysis Practice Test 22 — 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?

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

Accepted Answer

Import the dataset into SPICE (Super-fast, Parallel, In-memory Calculation Engine).. Importing the dataset into SPICE is correct because SPICE is QuickSight's in-memory, columnar calculation engine that pre-aggregates and caches data, allowing histogram generation over 10 million rows without repeatedly querying the source. It preserves full accuracy because all rows are loaded, unlike sampling. This is the standard QuickSight performance optimization for large datasets.

Answer

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

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Reduce the number of bins in the histogram.

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

Question 2

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.. The missing values in 'total_charges' occur only for customers with tenure=0, meaning they are new customers who have not yet been billed. Imputing with 0 is logically correct because their total charges are indeed zero. This preserves the data and avoids introducing bias or artificial values.

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

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

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Drop all rows with missing total_charges to avoid bias.

Question 3

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.

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Partition the table by device_type.

Question 4

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

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One-hot encoding

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Standardization (Z-score)

Question 5

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)

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Linear Discriminant Analysis (LDA)

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K-means clustering

Question 6

A data scientist uses Amazon SageMaker Data Wrangler to explore a dataset and notices that the target variable is highly imbalanced. Which technique should the data scientist apply to balance the dataset before training?

Accepted Answer

Synthetic Minority Oversampling Technique (SMOTE). Synthetic Minority Oversampling Technique (SMOTE) is the correct technique because it generates synthetic samples for the minority class by interpolating between existing minority instances and their k-nearest neighbors, effectively balancing the dataset without simply duplicating data. Amazon SageMaker Data Wrangler includes a built-in SMOTE transform, making it directly applicable for handling imbalanced target variables during exploratory data analysis.

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One-hot encoding of the target variable

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Random undersampling of the majority class

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Min-Max scaling of all features

Question 7

A data scientist examines a dataset with 100 features and suspects that some features are redundant due to high pairwise correlations. Which EDA technique should the scientist use to systematically identify groups of highly correlated features?

Accepted Answer

Generate a correlation matrix and visualize it as a heatmap.. A correlation matrix heatmap allows systematic identification of groups of highly correlated features by visually highlighting high pairwise correlations. Option B is incorrect because histograms show univariate distributions, not relationships between features. Option C is incorrect because scatter plots for each pair would be time-consuming and not systematic for 100 features. Option D is incorrect because box plots show outliers, not correlations.

Answer

Plot histograms for each feature.

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Create scatter plots for each pair of features.

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Use box plots to identify outliers.

Question 8

Which TWO actions should a data scientist take when exploring a dataset that contains missing values and outliers? (Select TWO.)

Accepted Answer

Calculate the percentage of missing values per column.. Calculating the percentage of missing values per column is a standard first step in exploratory data analysis (EDA) to quantify data completeness. This informs downstream decisions such as whether to impute, drop, or flag missing data, and helps assess the risk of bias or information loss. It is a diagnostic action, not a transformation, and should precede any imputation or removal.

Answer

Normalize all features using Min-Max scaling.

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Remove all rows with outliers.

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Impute missing values with the mean immediately.

Question 9

Which THREE techniques are commonly used in exploratory data analysis to understand the relationships between features and the target variable? (Select THREE.)

Accepted Answer

Use box plots to compare feature distributions across target classes.. Option A is correct because box plots display the distribution of a numeric feature split by the target classes, making differences in median, spread, and outliers across classes immediately visible—a standard bivariate EDA technique. Option C is correct because computing a correlation matrix (e.g., Pearson or Spearman coefficients) between each feature and the target quantifies the strength and direction of linear or monotonic relationships, guiding feature selection. Option D is correct because scatter plots and pair plots visualize pairwise feature interactions and feature-versus-target patterns, revealing nonlinear trends, clusters, and potential interactions that summary statistics miss. Option B does not belong because K-means is an unsupervised clustering algorithm that groups unlabeled observations and does not directly characterize feature-target relationships. Option E does not belong because PCA is a dimensionality-reduction technique that transforms features into uncorrelated components without reference to the target, so it does not itself explain feature-target relationships.

Answer

Perform K-means clustering on the features.

Answer

Apply Principal Component Analysis (PCA) to reduce dimensions.

Question 10

Which TWO statements about handling missing data during exploratory data analysis are correct? (Select TWO.)

Accepted Answer

Visualizing the pattern of missingness can help determine if data is missing at random.. Options B and C are correct. Visualizing the pattern of missingness helps determine if data is missing at random, which is a key EDA step. Understanding the missing data mechanism (MCAR, MAR, MNAR) is important for selecting an appropriate imputation strategy. Option A is incorrect because missing values should be addressed during EDA, not deferred to model training. Option D is incorrect because listwise deletion can introduce bias if data is not MCAR. Option E is incorrect because mean imputation reduces the variance of the imputed variable.

Answer

Missing values can be ignored during EDA and handled during model training.

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Listwise deletion (removing rows with missing values) is always safe and unbiased.

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Imputing missing values with the mean preserves the original variance.

Question 11

A machine learning engineer is performing exploratory data analysis on a dataset containing customer transaction records. The dataset has missing values in the 'age' column and outliers in the 'amount' column. Which combination of techniques should the engineer use to handle these issues during EDA?

Accepted Answer

Impute missing age values with the median and cap outliers in 'amount' using the interquartile range (IQR) method.. Median imputation is robust to outliers, and IQR-based capping is a standard method for handling outliers. Option B is wrong because removing rows with missing age can lead to data loss, and log transformation reduces skewness but does not handle outliers by capping. Option C is wrong because imputing with a constant like 0 is arbitrary and can bias the data, and capping using mean ± 3*std is sensitive to outliers. Option D is wrong because mean imputation is sensitive to outliers, and removing outliers via z-score can discard valid data points.

Answer

Remove rows with missing age and apply log transformation to 'amount'.

Answer

Impute missing age values with a constant (e.g., 0) and cap outliers using mean ± 3*std.

Answer

Impute missing age values with the mean and remove outliers in 'amount' using z-score.

Question 12

A data scientist is analyzing a dataset with 1 million records and 20 features. The target variable is continuous. The scientist wants to identify non-linear relationships between features and the target. Which technique is MOST suitable for this purpose during exploratory data analysis?

Accepted Answer

Calculate mutual information scores between each feature and the target.. Mutual information scores can capture any kind of relationship, including non-linear ones, between features and a continuous target. It measures the mutual dependence between variables, making it suitable for identifying non-linear associations. Pearson correlation only detects linear relationships, and PCA is for dimensionality reduction, not directly for feature-target relationship analysis.

Answer

Visualize the correlation matrix heatmap of all features.

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Apply Principal Component Analysis (PCA) and examine the loadings.

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Compute Pearson correlation coefficients between each feature and the target.

Question 13

A data scientist is working with a dataset that contains text reviews and a numeric rating (1-5). The goal is to predict the rating from the review text. During EDA, the scientist wants to check if there are any spelling errors or unusual characters. Which tool is BEST suited for this task?

Accepted Answer

Amazon Comprehend to detect syntax and entities.. Amazon Comprehend is the best choice among the options because it is an AWS AI service that can detect syntax, entities, and key phrases in text. While it does not directly find spelling errors, it can identify unusual patterns or anomalies in text that may indicate misspellings or odd characters. SageMaker Data Wrangler is for tabular data, Athena is for SQL queries, and QuickSight is for visualization, none of which are specialized for text analysis in this context.

Answer

Amazon SageMaker Data Wrangler with a custom transform for text cleaning.

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Amazon Athena with SQL queries to find anomalies.

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Amazon QuickSight to create word clouds.

Question 14

A data engineer is performing EDA on a time-series dataset of server metrics (CPU, memory, disk I/O) collected every minute. The dataset contains 2 years of data. The engineer suspects there are seasonal patterns and wants to decompose the time series for one metric. Which AWS service can be used to perform this decomposition natively?

Accepted Answer

Amazon SageMaker Canvas. Amazon SageMaker Canvas supports time-series forecasting with built-in decomposition. Option B is wrong because Amazon Athena with SQL window functions does not natively provide time-series decomposition. Option C is wrong because Amazon QuickSight does not decompose time series. Option D is wrong because AWS Glue DataBrew does not have decomposition functions.

Answer

Amazon Athena with SQL window functions

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Amazon QuickSight

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AWS Glue DataBrew

Question 15

A data scientist is performing EDA on a dataset with 500,000 rows and 10 columns. The dataset is stored in an S3 bucket as CSV files. The scientist wants to generate summary statistics (mean, median, min, max) for all numeric columns. Which service allows the quickest ad-hoc analysis without provisioning any infrastructure?

Accepted Answer

Amazon Athena. Amazon Athena is a serverless query service that can directly query CSV files in S3 using standard SQL, making it ideal for quick ad-hoc analysis without provisioning infrastructure. It automatically scales and charges per query, so the data scientist can run summary statistics immediately.

Answer

AWS Glue ETL

Answer

Amazon SageMaker Data Wrangler

Answer

Amazon QuickSight