MLS-C01 Exploratory Data Analysis Practice Test 27 — 15 Questions

Question 1

A data scientist is performing EDA on a dataset containing customer transaction records. The dataset includes columns: 'transaction_id', 'customer_id', 'transaction_amount', 'transaction_date', and 'product_category'. The data scientist wants to check for duplicate transactions and identify any suspicious patterns, such as multiple transactions from the same customer on the same day with the same amount. The dataset has 5 million rows. The data scientist is using a SageMaker Studio notebook with a ml.t3.medium instance. The data is stored in S3. What is the most efficient way to perform this analysis?

Accepted Answer

Use a SageMaker Spark processing job with PySpark to aggregate and detect duplicates.. Using Spark on SageMaker allows distributed processing of large data. Option A is wrong because pandas may run out of memory. Option B is wrong because Athena requires SQL queries and external setup. Option D is wrong because DataBrew is for profiling but not custom duplicate analysis.

Answer

Use Amazon Athena to run SQL queries to find duplicates.

Answer

Load the entire dataset into a pandas DataFrame and use groupby operations.

Answer

Use AWS Glue DataBrew to create a profile and manually inspect.

Question 2

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?

Accepted Answer

Use SageMaker Data Wrangler to import the data and generate a data quality report.. SageMaker Data Wrangler can profile the data without writing code. Option A is wrong because Glue Crawler creates a schema but not statistics. Option B is wrong because writing a Spark job is overkill. Option D is wrong because QuickSight requires data import.

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 3

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

Accepted Answer

Z-score. Z-score identifies outliers based on standard deviations from the mean. IQR method uses quartile ranges to flag points outside 1.5*IQR. Standard scaling, PCA, and K-means are not primarily outlier detection methods.

Answer

Principal Component Analysis (PCA)

Answer

K-means clustering

Answer

Standard scaling

Question 4

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 captures linear relationships. Mutual information captures non-linear dependencies. Feature importance from a tree-based model provides a ranking. Spearman correlation is for monotonic relationships, but the question asks for three methods among the options; the correct set includes Pearson, mutual information, and feature importance. Note: Spearman is also valid but not listed as a correct option here because we need exactly three; the options given make A, B, and D the correct choices.

Answer

Variance threshold

Answer

One-hot encoding

Question 5

A data engineer is analyzing a large dataset stored in Amazon S3 using AWS Glue and Amazon Athena. They notice that queries against a table with many small files are slow. Which TWO actions can improve query performance?

Accepted Answer

Convert files to Apache Parquet format. Compacting small files into larger ones reduces overhead. Partitioning the data limits the amount of data scanned. Using Parquet or ORC improves performance, but the question asks for two actions; converting to columnar format is also valid but not listed as a correct option here. The correct pair is A and B.

Answer

Use Athena's automatic compression

Answer

Increase the number of Glue DPUs

Answer

Decrease the number of partitions

Question 6

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.. Option B is correct because imbalanced categorical features may cause the model to ignore rare categories; grouping rare levels into an 'Other' category can improve model performance. Option A is wrong because removing the feature could discard useful information. Option C is wrong because one-hot encoding does not address imbalance. Option D is wrong because oversampling addresses target imbalance, not feature imbalance.

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 7

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.. Option C is correct because removing one of the highly correlated features reduces multicollinearity. Option A is wrong because PCA is not necessary for just two correlated features. Option B is wrong because standard scaling does not address correlation. Option D is wrong because adding interaction terms increases multicollinearity.

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 8

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.. Option B is correct because converting to datetime allows extracting day-of-week. Option A is wrong because binning into hours loses day information. Option C is wrong because converting to string does not facilitate analysis. Option D is wrong because scaling does not help.

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 9

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)).. Option D is correct because log transformation is commonly used for right-skewed data to reduce skewness. Option A is wrong because min-max scaling does not change distribution shape. Option B is wrong because square transformation increases skewness. Option C is wrong because binning loses information.

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 10

A data engineer is exploring a dataset with 1 million rows and 50 features. They notice that some features have missing values. The 'Age' column has 5% missingness, and 'Income' has 20% missingness. The target variable is 'LoanDefault' (binary). The engineer wants to impute missing values. Which of the following strategies is most appropriate?

Accepted Answer

Impute missing 'Age' with median and 'Income' with median.. Option B is correct because median imputation is robust to outliers and simple for numerical features. Option A is wrong because dropping rows with any missing values would remove 25% of data. Option C is wrong because mode is for categorical. Option D is wrong because model-based imputation is complex for initial EDA.

Answer

Impute missing 'Age' with mode and 'Income' with mode.

Answer

Use a k-NN model to predict missing values.

Answer

Drop all rows with missing values.

Question 11

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. Option C is correct because mutual information can capture any dependency. Option A is wrong because Pearson correlation only captures linear. Option B is wrong because Spearman captures monotonic but not all non-linear. Option D is wrong because ANOVA is for categorical vs continuous.

Answer

ANOVA test

Answer

Spearman's rank correlation

Answer

Pearson correlation coefficient

Question 12

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.. Option C is correct because TF-IDF downweights common words. Option A is wrong because bag-of-words does not weight. Option B is wrong because removing stopwords helps but does not adjust for frequency beyond that. Option D is wrong because word2vec focuses on context, not frequency weighting.

Answer

Remove stopwords from the text.

Answer

Use word2vec embeddings.

Answer

Use a bag-of-words representation.

Question 13

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).. Option D is correct because t-SNE is designed for non-linear dimensionality reduction and visualization. Option A is wrong because PCA is linear. Option B is wrong because LDA is supervised. Option C is wrong because ICA separates independent components, not for visualization.

Answer

Use Independent Component Analysis (ICA).

Answer

Use Linear Discriminant Analysis (LDA).

Answer

Apply PCA again with more components.

Question 14

A data analyst is examining a dataset with a target variable that has three classes: A, B, C. They plot the distribution of a feature 'X' for each class and notice that for classes A and B, the distributions are bimodal, while for class C it is unimodal. They want to assess whether feature 'X' is useful for separating the classes. Which of the following metrics should they compute to quantify the separability?

Accepted Answer

ANOVA F-statistic between feature X and the target.. Option A is correct because ANOVA F-statistic tests if means across groups are significantly different. Option B is wrong because chi-square is for categorical features. Option C is wrong because mutual information is for feature selection but doesn't directly test separability. Option D is wrong because variance ratio is not standard.

Answer

Variance ratio (between-group variance / within-group variance).

Answer

Chi-square test of independence.

Answer

Mutual information between X and the target.

Question 15

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.. Option B is correct because capping at percentiles (e.g., 99th) limits extreme values while keeping the data points. Option A is wrong because removing rows with any outlier may discard useful data. Option C is wrong because log transformation does not fix errors. Option D is wrong because imputing with mean distorts the distribution.

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.