MLS-C01 Exploratory Data Analysis Practice Test 3 — 15 Questions

Question 1

A data scientist is performing exploratory data analysis on a dataset with 10,000 rows and 20 features. The target variable is binary. The data scientist observes that one feature has 15% missing values. Which TWO actions are appropriate to handle this missing data? (Choose TWO.)

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

Accepted Answer

Use multiple imputation to fill in the missing values.. Multiple imputation is a robust statistical technique that accounts for uncertainty in missing values by creating multiple complete datasets, analyzing each, and pooling results. This is particularly appropriate for a dataset with 10,000 rows and 20 features, as it preserves the sample size and avoids bias that simpler methods might introduce.

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Replace missing values with the mode of the feature.

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Identify and remove outliers from the feature.

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Drop the entire feature from the dataset.

Question 2

A data scientist is analyzing a dataset of customer reviews. The dataset contains a text column 'review' and a numerical rating from 1 to 5. The data scientist wants to create features for sentiment analysis. Which THREE preprocessing steps should be applied to the text data before feature extraction? (Choose THREE.)

Accepted Answer

Apply stemming to reduce words to their root form.. Stemming reduces words to their root form (e.g., 'running' to 'run'), which consolidates variations of the same word and reduces feature dimensionality. This is a standard preprocessing step before feature extraction in NLP tasks like sentiment analysis, as it helps the model generalize across different word forms.

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Standardize the text data using z-score normalization.

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Tokenize the text into individual words.

Question 3

A data scientist is analyzing a dataset with a target variable that is heavily imbalanced (e.g., 99% negative class, 1% positive class). Which exploratory data analysis technique is most appropriate to understand the relationship between features and the target before modeling?

Accepted Answer

Use stratified sampling to create a balanced subset, then compute correlation matrices and box plots.. Stratified sampling preserves the class distribution in the sample, allowing you to create a balanced subset for exploratory analysis. Computing correlation matrices and box plots on this balanced subset reveals feature-target relationships without being overwhelmed by the majority class, which is critical for imbalanced datasets like 99% negative vs. 1% positive.

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Randomly sample 10% of the data and plot feature distributions by class.

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Apply PCA to reduce dimensionality, then visualize the first two components.

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Focus only on the majority class features to avoid bias.

Question 4

During EDA, a data scientist notices that a feature has a high proportion of missing values (e.g., 70%). The feature is continuous and expected to be important based on domain knowledge. What is the best approach to handle this?

Accepted Answer

Create a binary indicator for missingness and impute the continuous values with the median.. It preserves the predictive signal from the feature while accounting for the pattern of missingness. Creating a binary indicator allows the model to learn whether missingness itself is informative, and median imputation is robust to outliers for a continuous feature. This approach avoids the bias of dropping the feature entirely and is more principled than arbitrary out-of-range imputation.

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Remove the feature entirely to avoid bias.

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Impute missing values with -1 since it is out of range.

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Drop all rows with missing values in that feature.

Question 5

A data scientist is performing EDA on a dataset with 1,000 features and 10,000 rows. The target variable is binary. After checking for multicollinearity, the scientist finds many pairs of features with correlation > 0.95. Which action should be taken to prepare the data for modeling?

Accepted Answer

For each highly correlated pair, remove one feature based on domain knowledge or higher correlation with target.. When features are highly correlated (e.g., > 0.95), they introduce multicollinearity, which can destabilize coefficient estimates in linear models and reduce interpretability. Removing one feature from each correlated pair based on domain knowledge or its correlation with the target variable preserves predictive power while reducing redundancy. This approach is more targeted than PCA, which transforms features into uncorrelated components but sacrifices interpretability and may not align with the binary target.

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Apply PCA to all features to decorrelate them.

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Standardize all features using StandardScaler.

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Randomly drop half of the correlated features.

Question 6

A data scientist is analyzing a time-series dataset and wants to check for stationarity. Which EDA technique is most appropriate?

Accepted Answer

Perform the Augmented Dickey-Fuller (ADF) test.. The Augmented Dickey-Fuller (ADF) test is a formal statistical hypothesis test specifically designed to check for stationarity in a time series. It tests the null hypothesis that a unit root is present, indicating non-stationarity, against the alternative of stationarity. This makes it the most appropriate EDA technique for directly assessing stationarity.

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Plot the autocorrelation function (ACF).

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Use time-series cross-validation.

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Create a scatter plot of the series against its lag.

Question 7

During EDA, a data scientist creates a scatter matrix of numerical features and notices that some features have a funnel-shaped pattern (variance increases with the mean). What is the appropriate transformation to stabilize variance?

Accepted Answer

Apply log transformation.. A funnel-shaped pattern in a scatter matrix indicates heteroscedasticity, where variance increases with the mean. The log transformation is appropriate because it compresses the scale of the data, making the variance more constant across the range of values, which stabilizes variance for right-skewed or multiplicative data.

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Standardize the features using Z-scores.

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Apply a sine transformation.

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Apply Box-Cox transformation with lambda=0.

Question 8

Which TWO of the following are appropriate techniques for detecting outliers in a univariate continuous feature?

Accepted Answer

Use Z-score and flag values with absolute Z-score > 3.. The Z-score method (Option B) is a standard statistical technique for detecting outliers in a univariate continuous feature. It measures how many standard deviations a data point is from the mean, and flagging values with an absolute Z-score greater than 3 is a common threshold because, under a normal distribution, approximately 99.7% of data falls within three standard deviations, making points beyond this likely outliers.

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Apply a Random Forest classifier to predict outliers.

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Remove any value that is more than one standard deviation from the mean.

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Use DBSCAN clustering with default parameters.

Question 9

Which THREE of the following are best practices when performing exploratory data analysis on a dataset with both numerical and categorical features?

Accepted Answer

Check the proportion of missing values for each feature.. Checking the proportion of missing values for each feature is a fundamental step in exploratory data analysis (EDA). It helps identify data quality issues, such as systematic missingness, which can bias downstream modeling and inform decisions about imputation strategies or feature exclusion.

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Encode all categorical features using label encoding for simplicity.

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Include all categorical features with high cardinality as-is in the model.

Question 10

A data scientist is performing exploratory data analysis on a dataset with 10,000 rows and 20 features. The target variable is binary. The data scientist observes that one feature has 15% missing values. Which TWO actions are appropriate to handle this missing data? (Choose TWO.)

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

Accepted Answer

Use AWS Glue DataBrew to profile the dataset, view data quality reports, and visualize distributions.. AWS Glue DataBrew is purpose-built for visual data preparation and profiling without writing code. It can directly profile the 1-million-row dataset, automatically generate data quality reports (e.g., missing values, outliers, data types), and provide distribution visualizations for both numerical and categorical features, making it the most efficient choice for exploratory data analysis.

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Use Amazon Athena to run SQL queries and generate summary statistics.

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Use Amazon SageMaker Data Wrangler to import the data and create a flow for feature engineering.

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Use Amazon SageMaker Ground Truth to label the data and then analyze the labels.

Question 11

A data scientist is trying to read a CSV file from S3 bucket 'my-bucket' with key 'training/data.csv' using an IAM role with the attached policy shown in the exhibit. The read operation fails with an Access Denied error. What is the most likely cause?

Accepted Answer

The policy does not include the s3:ListBucket permission, which is required to access the object.. The s3:GetObject permission alone is sufficient for direct object retrieval using the object's full key (e.g., via AWS CLI `aws s3api get-object`). However, many AWS services and tools (such as the S3 console, Amazon Athena, or AWS Glue) implicitly invoke a ListObjects API call to resolve the object path or display the bucket contents, which requires the s3:ListBucket permission. Without it, these operations fail with an Access Denied error even though GetObject is granted. In this scenario, the error likely occurs because the tool or service used to read the file performs a ListObjects call first.

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The object is encrypted with SSE-KMS and the role does not have kms:Decrypt permission.

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The resource ARN in the first statement should be 'arn:aws:s3:::my-bucket/training' without the wildcard.

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The policy explicitly denies s3:GetObject because of the second statement with the trailing slash.

Question 12

A data scientist is building a fraud detection model using a dataset of 500,000 credit card transactions. The dataset contains 20 features, including transaction amount, merchant category, time since last transaction, and customer age. The target variable 'is_fraud' has 0.1% positive examples. Initial EDA reveals that the transaction amount distribution is highly skewed with a long tail. Also, there are missing values in the 'customer_age' field (5% missing). The data scientist needs to prepare the data for training a binary classifier. Which combination of preprocessing steps should the data scientist apply to address these issues and improve model performance? (Select TWO.)

Accepted Answer

Apply log transformation to the transaction amount to reduce skewness.. This is a multi-select question requiring two correct preprocessing steps. Option C is correct because applying a log transformation to the highly skewed transaction amount reduces skewness and compresses the dynamic range, which helps many machine learning algorithms (especially those sensitive to feature scales like logistic regression or SVM) converge faster and perform better. Option D is correct because imputing missing values in customer_age with the mean is a simple and effective method when the missing rate is only 5% and the data is roughly normally distributed, preserving sample size. Option A is wrong because SMOTE is typically applied after splitting the data to avoid data leakage, and it is not a preprocessing step for EDA; also, the class imbalance is severe but SMOTE may be considered later. Option B is wrong because standard scaling does not handle skewness; it should be applied after skewness correction. Option E is wrong because dropping the feature due to skewness would lose valuable information; transformation is preferable.

Answer

Use SMOTE to generate synthetic samples of the minority class.

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Apply standard scaling to all numerical features.

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Drop the transaction amount feature because of its skewness.

Question 13

A data scientist is analyzing a dataset with 100 features and 10,000 observations. The target variable is binary (0/1). Initial exploratory data analysis reveals that many features have missing values, high correlation with each other, and non-normal distributions. The data scientist wants to identify the most important features for predicting the target while reducing dimensionality. Which TWO actions should the data scientist take? (Choose two.)

Accepted Answer

Apply Principal Component Analysis (PCA) to reduce dimensionality.. B is correct because Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms correlated features into a set of linearly uncorrelated principal components, effectively handling high correlation and reducing the feature space. It does not require normality assumptions and can work with missing values after imputation, making it suitable for this dataset.

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Use chi-squared test to rank features by p-value.

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Perform a t-test for each feature to compare means between classes.

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Calculate Pearson correlation coefficients between features and target.

Question 14

Refer to the exhibit. A data scientist ran an S3 Select query on a large CSV file stored in Amazon S3. The output shows only 2 records returned, but the data scientist expected thousands. The file size is 10 GB. What is the MOST likely reason for the small result set?

Accepted Answer

The city column may have leading/trailing spaces or case differences.. S3 Select performs exact string matching by default, so if the WHERE clause filters on the city column, any leading/trailing spaces or case differences will cause mismatches, returning far fewer rows than expected. The query likely used a literal like 'New York' while the data contains ' New York ' or 'new york', resulting in only 2 matches instead of thousands.

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The file needs to be indexed by S3 Select before querying.

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The CSV file contains nested arrays that S3 Select cannot parse.

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S3 Select does not support the WHERE clause on CSV files.

Question 15

A machine learning engineer is working on a regression problem to predict house prices. The dataset contains 500,000 rows and 20 features, including 'sqft_living', 'bedrooms', 'bathrooms', 'floors', 'waterfront', 'view', 'condition', 'grade', 'yr_built', 'zipcode', and 'lat'. After performing exploratory data analysis, the engineer notices that the 'sqft_living' feature has a right-skewed distribution with a long tail. The 'zipcode' feature is categorical with 70 unique values. The 'lat' feature is continuous. The engineer wants to prepare the data for a linear regression model. Which action should the engineer take to improve model performance?

Accepted Answer

Apply a log transformation to the 'sqft_living' feature.. Linear regression assumes that features are approximately normally distributed, and a right-skewed distribution like 'sqft_living' can violate this assumption, leading to poor model performance. Applying a log transformation compresses the long tail, making the distribution more symmetric and helping the model learn a linear relationship between the feature and the target. This is a standard preprocessing step for skewed features in regression tasks.

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Remove the 'sqft_living' feature because it violates the normality assumption.

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One-hot encode the 'zipcode' feature to capture location effects.

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Apply standard scaling (z-score) to the 'sqft_living' feature.