MLS-C01 Exploratory Data Analysis Practice Test 30 — 15 Questions

Question 1

A data scientist is starting a new machine learning project and needs to understand the dataset. The dataset is stored as CSV files in Amazon S3, with a total size of 50 GB. The data scientist wants to quickly get summary statistics (count, mean, standard deviation, min, max) for each numerical column, and also check for missing values. The data scientist has access to SageMaker Studio. What is the most efficient way to achieve this?

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

Accepted Answer

Use SageMaker Data Wrangler to import the data and generate a data quality report.. SageMaker Data Wrangler is purpose-built for data preparation and profiling, allowing you to compute summary statistics and check for missing values with a visual interface and without writing code. Option A (AWS Glue Crawler + Athena) only infers schema and enables SQL queries; it does not automatically provide summary statistics or missing value counts. Option B (PySpark script) is possible but requires manual coding and Spark cluster management, making it less efficient for quick exploration. Option C (Amazon QuickSight) is a BI tool that requires loading data into SPICE, which is not as streamlined for initial data profiling as Data Wrangler.

Answer

Use AWS Glue Crawler to infer schema and then query with Athena.

Answer

Write a PySpark script in a SageMaker notebook to compute statistics.

Answer

Load a sample into Amazon QuickSight and use SPICE to compute statistics.

Question 2

Which TWO of the following are common techniques for detecting outliers in a dataset?

Accepted Answer

Z-score. The Z-score is a standard statistical technique for outlier detection: it measures how many standard deviations a data point lies from the mean, and points with |Z| above a threshold (commonly 3) are flagged as outliers, so option A is correct. The Interquartile range (IQR) method is also a classic outlier detection technique: it computes IQR = Q3 − Q1 and flags values below Q1 − 1.5×IQR or above Q3 + 1.5×IQR as outliers, so option B is correct. Principal Component Analysis (PCA) is a dimensionality-reduction technique, not an outlier detection method, so option C does not belong. K-means clustering is an unsupervised clustering algorithm that groups data but does not by itself identify outliers, so option D is not correct. Standard scaling (z-score normalization) merely rescales features to mean 0 and standard deviation 1 and is a preprocessing step, not an outlier detection technique, so option E does not belong.

Answer

Principal Component Analysis (PCA)

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K-means clustering

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Standard scaling

Question 3

A data scientist is performing exploratory data analysis on a dataset with 100 features. They want to identify which features are most correlated with the target variable. Which THREE methods are appropriate for this task?

Accepted Answer

Pearson correlation coefficient. Pearson correlation coefficient (A) is correct because it directly quantifies the linear relationship between each continuous feature and a continuous target, producing a ranked list of the most correlated features. Feature importance from a random forest (D) is correct because it captures both linear and non-linear relationships between features and the target, ranking features by their contribution to predictive performance. Mutual information (E) is correct because it measures any statistical dependence, including non-linear associations, between each feature and the target, making it well suited for identifying relevant features. Variance threshold (B) is not appropriate because it only removes low-variance features and does not consider the target variable at all. One-hot encoding (C) is not appropriate because it is a preprocessing transformation for categorical variables, not a method for measuring correlation with a target.

Answer

Variance threshold

Answer

One-hot encoding

Question 4

A data scientist is working on a project to predict customer churn. The dataset contains 50,000 rows and 20 features, including categorical variables like 'Region' (10 categories) and 'SubscriptionType' (5 categories). The target variable is binary (churn or not). During exploratory data analysis, they plot the distribution of each feature and notice that 'Region' has a highly imbalanced distribution: one region accounts for 80% of the data. Which of the following is the most appropriate next step?

Accepted Answer

Group rare categories into an 'Other' category.. Grouping rare categories into an 'Other' category is the most appropriate next step because it reduces the cardinality of the 'Region' feature while preserving the information that a region is rare. This helps prevent overfitting and improves model generalization, especially when one category dominates. One-hot encoding with 10 categories would create 10 sparse binary features, and the rare categories would have very few samples, leading to unstable estimates.

Answer

Apply one-hot encoding to the 'Region' feature.

Answer

Remove the 'Region' feature from the dataset.

Answer

Oversample the minority classes in the target variable.

Question 5

A machine learning team is analyzing a dataset with numerical features. They compute the pairwise correlation matrix and find that two features, 'X1' and 'X2', have a correlation coefficient of 0.98. The team plans to train a linear regression model. Which of the following actions should the team take to avoid multicollinearity issues?

Accepted Answer

Remove one of the two highly correlated features.. Removing one of the highly correlated features reduces multicollinearity. Option A is wrong because PCA creates new uncorrelated features but is not necessary for just two correlated features. Option B is wrong because adding an interaction term between X1 and X2 would actually increase multicollinearity. Option C is wrong because standard scaling does not address correlation between features.

Answer

Perform PCA on the dataset to reduce dimensionality.

Answer

Add an interaction term between X1 and X2 to the model.

Answer

Standardize both features using Z-score normalization.

Question 6

A data analyst is performing EDA on a dataset containing timestamps of user logins. They want to understand daily login patterns. The timestamp column is in Unix epoch format (integer). Which of the following is the most appropriate transformation to extract day-of-week patterns?

Accepted Answer

Convert the timestamps to datetime objects and extract the day-of-week.. Converting Unix epoch timestamps to datetime objects allows extraction of the day-of-week using functions like .dt.dayofweek() in pandas. Option B is wrong because converting to string and splitting into date and time does not directly give day-of-week patterns and loses temporal properties. Option C is wrong because min-max scaling is used for normalizing numerical features, not for extracting temporal patterns. Option D is wrong because binning into 1-hour intervals captures hourly patterns, not day-of-week patterns.

Answer

Convert the timestamps to string and split into date and time.

Answer

Apply min-max scaling to the timestamp values.

Answer

Bin the timestamps into 1-hour intervals.

Question 7

A data scientist is working with a dataset that contains a 'Price' column. After plotting a histogram, they observe that the distribution is right-skewed with many extreme high values. They plan to use a linear model that assumes normally distributed errors. Which of the following transformations should they apply to the 'Price' column to make it more normally distributed?

Accepted Answer

Apply log transformation (log(Price)).. Log transformation is commonly applied to right-skewed data to reduce skewness and make the distribution more normal, which is suitable for linear models assuming normally distributed errors. Option B (square transformation) exacerbates skewness, making it worse. Option C (min-max scaling) only rescales the data to a fixed range and does not change the shape of the distribution. Option D (binning) discards information and does not transform the distribution to be normal.

Answer

Apply square transformation (Price^2).

Answer

Apply min-max scaling to the 'Price' column.

Answer

Bin the 'Price' values into equal-width intervals.

Question 8

A data scientist is analyzing a dataset with a binary target variable. They compute the correlation matrix and find that all features have correlations between -0.1 and 0.1 with the target. They suspect that the relationship might be non-linear. Which of the following techniques should they use to detect non-linear relationships?

Accepted Answer

Mutual information. Mutual information (MI) measures the mutual dependence between two variables and can capture any kind of relationship, including non-linear ones. Unlike correlation coefficients, which only detect linear relationships, MI is based on entropy and quantifies how much knowing one variable reduces uncertainty about the other. Since the features have low linear correlation with the target, MI is the appropriate technique to detect non-linear dependencies.

Answer

ANOVA test

Answer

Spearman's rank correlation

Answer

Pearson correlation coefficient

Question 9

A machine learning engineer is examining a dataset containing text reviews. They want to convert the text into numerical features for a model. During EDA, they notice that the word 'the' appears in almost every review, while words like 'excellent' appear rarely. Which of the following techniques should they use to reduce the impact of very common words?

Accepted Answer

Apply TF-IDF transformation.. TF-IDF transformation downweights common words (like 'the') and emphasizes rare but informative words (like 'excellent'). Option B (removing stopwords) is insufficient because it does not adjust for frequency beyond removing a predefined list; TF-IDF handles frequency weighting. Option C (word2vec embeddings) captures semantic relationships but does not specifically reduce the impact of common words. Option D (bag-of-words) does not perform any weighting, so common words dominate.

Answer

Remove stopwords from the text.

Answer

Use word2vec embeddings.

Answer

Use a bag-of-words representation.

Question 10

A data scientist is performing EDA on a high-dimensional dataset with 500 features. They want to visualize the data in 2D to check for clusters. They first apply PCA and get a 2D projection that shows no clear structure. They suspect that the data lies on a non-linear manifold. Which of the following techniques should they try next?

Accepted Answer

Use t-distributed Stochastic Neighbor Embedding (t-SNE).. T-SNE is a non-linear dimensionality reduction technique specifically designed for visualization. Option A (ICA) is a linear method for separating independent components, not for capturing non-linear manifolds. Option B (LDA) is a supervised linear method that maximizes class separation, unsuitable for unsupervised non-linear structure. Option C (PCA with more components) remains linear, so adding components does not help with non-linear manifolds.

Answer

Use Independent Component Analysis (ICA).

Answer

Use Linear Discriminant Analysis (LDA).

Answer

Apply PCA again with more components.

Question 11

A team is building a model to predict house prices. They have a dataset with features like 'SquareFootage', 'Bedrooms', 'YearBuilt', and 'Neighborhood'. They notice that 'SquareFootage' has a few extreme values (e.g., 50,000 sq ft) that are likely data entry errors. They want to handle these outliers without losing all the data. Which of the following approaches is most robust?

Accepted Answer

Cap 'SquareFootage' at the 99th percentile value.. Capping 'SquareFootage' at the 99th percentile limits extreme values while retaining most data points, making it robust against data entry errors. Option B is incorrect because replacing extreme values with the mean distorts the distribution and can bias the model. Option C is incorrect because a log transformation does not fix data entry errors; it only changes the scale. Option D is incorrect because removing rows with values above 3 standard deviations from the mean may discard valid data points and is not robust when the data contains errors.

Answer

Replace extreme values with the mean of 'SquareFootage'.

Answer

Apply log transformation to 'SquareFootage'.

Answer

Remove rows where 'SquareFootage' is above 3 standard deviations from the mean.

Question 12

A data scientist is exploring a dataset with 200 features. They compute the pairwise correlation matrix and notice that many features have correlations above 0.95. They want to reduce redundancy before modeling. Which of the following techniques is most appropriate for identifying and removing highly correlated features?

Accepted Answer

Perform hierarchical clustering on the correlation matrix and select one feature per cluster.. Hierarchical clustering on correlations groups correlated features; then one can select a representative from each cluster. Option A is wrong because mutual information with the target does not capture pairwise redundancy among features. Option B is wrong because PCA creates new features but does not remove original ones. Option C is wrong because Lasso regression performs feature selection but may not handle multicollinearity well and does not directly identify redundant groups.

Answer

Compute mutual information between each feature and the target.

Answer

Apply PCA and keep the first 50 components.

Answer

Use Lasso regression to select features.

Question 13

A data scientist has a .csv file containing 500,000 rows of customer churn data stored in Amazon S3. Before building any model, she wants a quick, automated visual profile of every column — including histograms, quantile statistics, and a correlation matrix — without writing any code. Which solution should she use?

Accepted Answer

Create a SageMaker Data Wrangler flow, import the S3 data source, and use the built-in Data Quality and Insights report.. Amazon SageMaker Data Wrangler's Data Quality and Insights report is the only listed capability that ingests S3 data and automatically emits histograms, quantile statistics, and a correlation matrix with no coding. SageMaker Processing, Athena, and DynamoDB all require the analyst to build the analysis herself and do not natively produce that combined automated profile.

Answer

Open the file in Amazon Athena, run SELECT * and inspect the query results grid for distribution insights.

Answer

Load the file into Amazon DynamoDB and enable DynamoDB Streams to capture column-level statistics.

Answer

Run an Amazon SageMaker Processing job with scikit-learn to generate the plots and write them to S3.

Question 14

An ML engineer is exploring a training dataset for a fraud-detection model. The 'transaction_amount' feature has a heavy right skew and several extreme values. She wants a quick visual comparison of the distribution before and after applying a logarithmic transformation, all inside her Amazon SageMaker Studio notebook. Which approach is MOST appropriate?

Accepted Answer

Use pandas and matplotlib in the notebook to plot overlaid histograms or KDE curves of the raw and log-transformed values.. Overlaid histograms or KDE curves of the raw and log-transformed transaction_amount values directly visualize the effect of the transformation on skew, and pandas with matplotlib is immediately available in a SageMaker Studio notebook. Correlation analysis, Feature Store metrics, and SageMaker Debugger all address different concerns and cannot produce the before/after distribution comparison.

Answer

Compute the Pearson correlation between transaction_amount and the binary fraud label and plot a scatter matrix.

Answer

Enable SageMaker Debugger on a training job and review the tensor distribution summaries it emits.

Answer

Register the feature in Amazon SageMaker Feature Store and inspect the feature's online store metrics.

Question 15

A data scientist is exploring a dataset of credit card transactions stored in Amazon S3. The dataset is 500 GB in Parquet format and contains a highly skewed numerical feature 'transaction_amount'. The scientist wants to quickly assess the distribution and summary statistics without loading the entire dataset into memory. Which approach is MOST efficient and cost-effective for initial EDA using AWS-native tools?

Accepted Answer

Use Amazon Athena to run SQL queries that compute summary statistics and generate a histogram using the width_bucket function.. Amazon Athena allows querying data directly in S3 using standard SQL, which is ideal for exploratory data analysis on large datasets without the need to load or transform the data. It supports aggregation functions and histogram generation via width_bucket, and its serverless nature makes it cost-effective for ad-hoc queries.

Answer

Download the entire dataset to a local machine and use pandas to compute summary statistics and plot a histogram.

Answer

Launch an Amazon EMR cluster with Apache Spark and run a custom Scala script to compute statistics and generate a histogram.

Answer

Use AWS Glue DataBrew to create a recipe that computes summary statistics and visualizes the distribution.