MLS-C01 Exploratory Data Analysis Practice Test 14 — 15 Questions

Question 1

Drag and drop the steps to use Amazon SageMaker Feature Store for feature engineering in the correct order.

Accepted Answer

1. Define feature group, 2. Ingest data, 3. Query features, 4. Train model, 5. Maintain feature store. Feature Store involves defining group, ingesting, querying, training, and maintaining.

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1. Define feature group, 2. Query features, 3. Ingest data, 4. Train model, 5. Maintain feature store

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1. Ingest data, 2. Define feature group, 3. Query features, 4. Train model, 5. Maintain feature store

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1. Define feature group, 2. Ingest data, 3. Train model, 4. Query features, 5. Maintain feature store

Question 2

Match each ML model evaluation concept to its definition.

Accepted Answer

Precision: The proportion of true positive predictions among all positive predictions. Precision, Recall, F1 Score, and ROC AUC are key evaluation metrics. Common confusions include swapping Accuracy, Specificity, and Precision definitions.

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ROC AUC: The proportion of correct predictions among total predictions

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Accuracy: The ability to correctly identify negative cases

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Specificity: The proportion of true positive predictions among all positive predictions

Question 3

A machine learning engineer is performing exploratory data analysis on a dataset containing customer transactions. They notice that the target variable is highly imbalanced: 99% of samples belong to class 0 and 1% to class 1. Which technique should they use to address this imbalance before training a classification model?

Accepted Answer

Apply SMOTE to generate synthetic samples for the minority class.. SMOTE (Synthetic Minority Over-sampling Technique) generates synthetic samples for the minority class, which helps balance the dataset and improves model performance on the minority class without losing information from the majority class. Option A is wrong: training on raw data without addressing imbalance will cause the model to be biased toward the majority class and perform poorly on the minority class. Option C is wrong: accuracy is not a suitable evaluation metric for imbalanced datasets because a model that always predicts the majority class will achieve 99% accuracy, masking poor performance on the minority class; instead, metrics like precision, recall, F1-score, or AUC should be used. Option D is wrong: under-sampling the majority class to match the minority class size discards a large amount of data, potentially losing valuable patterns and reducing model performance.

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Train the model on the raw data without any modification.

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Use accuracy as the evaluation metric and train on the raw data.

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Under-sample the majority class to match the minority class size.

Question 4

A data scientist is analyzing a dataset with missing values. The missing data mechanism is missing at random (MAR). Which imputation method is most appropriate to preserve relationships between variables?

Accepted Answer

Use multiple imputation by chained equations (MICE).. MICE (Multiple Imputation by Chained Equations) is the most appropriate method for MAR data because it models each variable with missing values conditional on the other variables, preserving multivariate relationships and injecting proper uncertainty across multiple imputations. It produces unbiased estimates under MAR and is the gold standard for complex missingness patterns.

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Remove all rows with any missing values.

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Use k-nearest neighbors imputation.

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Replace missing values with the mean of the column.

Question 5

Which TWO actions are appropriate when dealing with outliers in a dataset during exploratory data analysis? (Select TWO.)

Accepted Answer

Apply log transformation to reduce the impact of extreme values.. Applying a log transformation compresses the range of the data, reducing the influence of extreme values without removing them. This is a common technique in exploratory data analysis for right-skewed distributions, as it can make the data more normally distributed and improve the performance of models that assume normality.

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Replace the mean with the median for numerical features.

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Remove all outliers without further investigation.

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Assume outliers are errors and delete them.

Question 6

Which THREE techniques are commonly used for feature engineering in exploratory data analysis? (Select THREE.)

Accepted Answer

Extracting date/time components like day of week or hour.. Extracting date/time components such as day of week, hour, or month from a timestamp is a standard feature engineering technique. It transforms a single datetime column into multiple categorical or cyclical features that can reveal temporal patterns like weekly seasonality or peak hours, which are often critical for time-series models.

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Using principal component analysis (PCA) to create new features.

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Applying one-hot encoding to numerical features.

Question 7

Which TWO statements about handling categorical variables in exploratory data analysis are correct? (Select TWO.)

Accepted Answer

When a categorical feature has high cardinality, consider grouping rare categories.. High-cardinality categorical features can lead to overfitting and sparse representations. Grouping rare categories into a single 'Other' bucket reduces dimensionality and noise, improving model generalization without losing significant predictive signal.

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Target encoding always avoids data leakage.

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Label encoding is suitable for nominal categorical variables.

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Categorical variables should always be dropped if they have many unique values.

Question 8

A data scientist is using Amazon Athena to query a CSV file stored in S3. The query fails with the error: 'HIVE_CANNOT_OPEN_SPLIT: Number of fields in line 1502 does not match number of fields in the first line.' What is the most likely cause?

Accepted Answer

The CSV file has inconsistent number of columns in some rows.. Athena uses a schema-on-read model where the table definition specifies a fixed number of columns. When a CSV row contains more or fewer comma-separated fields than the header/first line, the Hive SerDe cannot map the fields to columns and throws HIVE_CANNOT_OPEN_SPLIT with the line number. The fix is to clean the CSV (ensure every row has the same column count) or use OpenCSVSerDe with tolerance settings.

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The CSV file uses a different delimiter than comma.

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The CSV file is missing a header row.

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The CSV file is too large for Athena to process.

Question 9

A data scientist is working with a dataset that includes a 'timestamp' column. They want to create features that capture seasonality. Which feature engineering approach is most appropriate?

Accepted Answer

Extract hour of day and apply sine/cosine transformation.. Extracting hour of day and applying a sine/cosine transformation captures the cyclical nature of time, ensuring that hour 23 and hour 0 are close in the feature space, which is essential for models to learn daily seasonality. This approach avoids the discontinuity of raw integer hours and provides a smooth, periodic representation.

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Bin timestamps into fixed intervals.

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Convert timestamp to Unix epoch seconds.

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One-hot encode the timestamp column.

Question 10

During exploratory data analysis, a data scientist discovers that a feature has a variance of 0.01, while other features have variances around 1.0. Which action should be taken?

Accepted Answer

Consider removing the feature or applying variance threshold.. A feature with variance 0.01 is nearly constant, meaning it carries very little information for distinguishing between samples. In exploratory data analysis, such low-variance features are often candidates for removal because they contribute little to model performance and can add noise. Applying a variance threshold is a standard technique to filter out such features. Scaling or transforming would not increase its informational value.

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Scale the feature to have unit variance.

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Apply a log transformation to the feature.

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Impute missing values in the feature.

Question 11

A data scientist wants to understand the relationship between a categorical feature with 3 levels and a continuous target variable. Which visualization is most appropriate?

Accepted Answer

Box plot grouped by category. A box plot grouped by category (Option C) is the most appropriate visualization because it directly compares the distribution of a continuous target variable across the three levels of a categorical feature. It displays median, quartiles, and potential outliers for each group, making it ideal for understanding central tendency, spread, and skewness in a side-by-side comparison.

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Correlation matrix

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Line chart

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Scatter plot

Question 12

A data scientist is analyzing a dataset and finds that the target variable has a bimodal distribution. Which preprocessing step is most appropriate before modeling?

Accepted Answer

Consider clustering to separate the two modes and model them separately.. A bimodal target distribution suggests the data may come from two distinct subpopulations or generating processes. Rather than forcing a single model to fit both modes, the most appropriate preprocessing step is to investigate whether clustering can separate the two modes so each can be modeled separately (e.g., a mixture-of-experts or two-model approach). This preserves the underlying structure instead of distorting it with transformations that assume unimodality.

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Standardize the target variable to have mean 0 and variance 1.

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Remove outliers from the target variable.

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Apply a log transformation to the target variable.

Question 13

A data scientist is performing exploratory data analysis on text data. They want to identify the most common terms and their frequencies. Which approach should they use?

Accepted Answer

Create a bag-of-words matrix and compute term frequencies.. A bag-of-words matrix counts the frequency of each term in the text, directly providing the most common terms and their frequencies. Option A is incorrect because sentiment analysis determines the emotional tone of text, not term frequencies. Option B is incorrect because Latent Dirichlet Allocation (LDA) is a topic modeling technique that assigns topics to documents, not term frequencies. Option D is incorrect because word2vec generates dense vector embeddings that capture semantic relationships, not raw term frequencies.

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Perform sentiment analysis on the text.

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Apply Latent Dirichlet Allocation (LDA) to extract topics.

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Use word2vec to generate word embeddings.

Question 14

A data scientist is exploring a dataset with 500 features and 100,000 observations for a regression problem. The scientist notices that many features are highly correlated with each other. Which technique should the scientist use to reduce multicollinearity and improve model interpretability during exploratory data analysis?

Accepted Answer

Calculate Variance Inflation Factor (VIF) for each feature and remove those with VIF > 10.. Variance Inflation Factor (VIF) directly quantifies multicollinearity by measuring how much a feature's variance is inflated by correlation with other features. A VIF above 10 (some use 5) indicates problematic multicollinearity, so removing those features directly addresses the stated goal of reducing multicollinearity and improving interpretability. This is the textbook diagnostic-and-remove approach for the specific problem described.

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Compute mutual information between each feature and the target, and keep only the top 50 features.

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Apply Principal Component Analysis (PCA) to reduce the feature space.

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Use Lasso regression to select features with non-zero coefficients.

Question 15

A machine learning team is analyzing a dataset with a target variable that is highly imbalanced (99% negative class, 1% positive class). They want to understand the distribution and relationships before modeling. Which exploratory data analysis technique is most appropriate to visualize the imbalance and guide resampling strategy?

Accepted Answer

Bar chart of class frequencies and a correlation heatmap. A bar chart of class frequencies clearly visualizes the imbalance (99% negative vs 1% positive), and a correlation heatmap helps identify which features are correlated with the target, guiding resampling strategy. Option A is wrong because a confusion matrix is used for evaluating model predictions, not for initial exploratory data analysis of class imbalance. Option B is wrong because a scatterplot matrix is designed to visualize relationships between continuous variables and can be overwhelming with many features; it does not directly highlight the class imbalance. Option C is wrong because box plots grouped by target class show feature distributions across classes but do not explicitly quantify the imbalance ratio itself.

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Confusion matrix on a sample of the data

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Scatterplot matrix of all features colored by class

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Box plots of each feature grouped by the target class