DA0-002 Data Analysis — Timed 100 Questions

Question 1

A retail company wants to test whether a new website layout increases the conversion rate compared to the current layout. They randomly assign visitors to either the control or treatment group. Which statistical test is most appropriate to compare the conversion rates?

100 minute time limit — choose an answer to begin.

Accepted Answer

Chi-square test. A chi-square test of independence is appropriate for comparing two proportions (conversion rates) between independent groups. The two-sample t-test compares means, not proportions. ANOVA is for three or more groups, and logistic regression models probability but is not the simplest test for a two-group proportion comparison.

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Two-sample t-test

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ANOVA

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Logistic regression

Question 2

A data analyst needs to identify the most frequently occurring value in a dataset. Which measure of central tendency should they use?

Accepted Answer

Mode. The mode is the measure of central tendency that identifies the most frequently occurring value in a dataset. Unlike the mean or median, the mode directly counts the frequency of each distinct value and returns the value with the highest count, making it the correct choice for this specific requirement.

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Standard deviation

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Median

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Mean

Question 3

A retail company wants to predict future sales based on historical data. Which modeling approach is most appropriate if the data shows a clear seasonal pattern?

Accepted Answer

Time series analysis. Time series analysis is specifically designed to model data points indexed in time order, making it ideal for capturing and forecasting seasonal patterns. Unlike regression models, it accounts for autocorrelation, trends, and seasonality components, which are critical for accurate sales prediction from historical data.

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Linear regression

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K-means clustering

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Logistic regression

Question 4

A data analyst is building a model to predict customer churn. The dataset has 10,000 records with 500 churned customers. The model predicts churn with 95% accuracy, but only identifies 10% of actual churners. Which metric best highlights this issue?

Accepted Answer

Recall. Recall (also known as sensitivity or true positive rate) measures the proportion of actual positives correctly identified. With only 10% of actual churners detected, the model has a recall of 0.1, which directly highlights the failure to capture churners despite high overall accuracy.

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Accuracy

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F1 score

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Precision

Question 5

A data analyst needs to combine two datasets that have the same columns but different rows. Which operation should they use?

Accepted Answer

Append. (Append) is correct because appending is the standard operation for combining two datasets with identical columns but different rows, stacking the rows from one dataset onto the other. In tools like SQL, this is achieved with the UNION or UNION ALL operator, and in Python pandas, it is done via the `append()` method or `pd.concat()` with axis=0. This operation preserves the column structure while extending the row count.

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Concatenate

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Merge

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Aggregate

Question 6

A data analyst is performing a hypothesis test with a significance level of 0.05. The p-value obtained is 0.03. What should the analyst conclude?

Accepted Answer

Reject the null hypothesis. Since the p-value (0.03) is less than the significance level (0.05), the result is statistically significant. This means the observed data provides sufficient evidence to reject the null hypothesis in favor of the alternative hypothesis. The analyst should conclude that there is a statistically significant effect or difference.

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Fail to reject the null hypothesis

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Accept the null hypothesis

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The result is practically significant

Question 7

A data scientist trains a regression model and observes high variance with low bias. Which technique is most appropriate to reduce variance?

Accepted Answer

Apply Ridge regularization. Ridge regularization (L2) reduces variance by adding a penalty term proportional to the square of the coefficients, which shrinks them toward zero without eliminating them. This directly addresses high variance (overfitting) by constraining the model's complexity, while low bias indicates the model fits the training data well. The regularization parameter λ controls the trade-off between bias and variance.

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Increase polynomial features

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Use a smaller training set

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Remove correlated features

Question 8

A data analyst is cleaning a dataset and finds missing values in a categorical variable representing customer region. Which imputation method is most appropriate?

Accepted Answer

Mode imputation. Mode imputation is the most appropriate method for a categorical variable because it replaces missing values with the most frequently occurring category, preserving the distribution of the data. Unlike mean or median imputation, which are designed for numerical data, mode imputation maintains the categorical nature of the variable and avoids introducing invalid values. This approach is simple and effective when missing data is random and the category is well-represented.

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Drop rows with missing values

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Mean imputation

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Median imputation

Question 9

A data analyst needs to visualize the distribution of a continuous variable across different categories. Which chart type is most suitable?

Accepted Answer

Box plot. A box plot (option D) is the most suitable chart for visualizing the distribution of a continuous variable across different categories because it displays the median, quartiles, and potential outliers for each group, enabling direct comparison of spread and central tendency. Unlike a histogram, which shows the distribution of a single continuous variable without categorical grouping, the box plot inherently supports categorical axes. This makes it ideal for exploratory data analysis when assessing how a metric like revenue varies by region or product category.

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Bar chart

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Histogram

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Scatter plot

Question 10

A company is analyzing customer feedback sentiment. The dataset is highly imbalanced with 95% positive and 5% negative comments. Which technique should the analyst use to address class imbalance before modeling?

Accepted Answer

Use SMOTE. SMOTE (Synthetic Minority Oversampling Technique) is the correct choice because it generates synthetic samples for the minority class (negative comments) by interpolating between existing minority instances, rather than simply duplicating them. This addresses the 95:5 imbalance without the information loss of undersampling or the overfitting risk of naive oversampling.

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Use accuracy as the evaluation metric

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Undersample the majority class

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Oversample the majority class

Question 11

Which TWO of the following are common assumptions of linear regression?

Accepted Answer

Independence of observations. Linear regression relies on several assumptions for valid OLS estimation. Two fundamental assumptions are independence of observations (option A), meaning the residuals are independent, and linearity of the relationship (option C), meaning the model correctly specifies a linear relationship between predictors and outcome. While homoscedasticity (equal variance of residuals) is also an important assumption, it is not listed as one of the two most common assumptions in this context. Options B and D are incorrect: no multicollinearity applies only to multiple regression, and normality of the dependent variable is not required; rather, normality of residuals is needed for hypothesis testing.

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No multicollinearity

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Normality of the dependent variable

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Homoscedasticity

Question 12

Which THREE of the following are appropriate methods to handle outliers in a dataset?

Accepted Answer

Transforming the data using log transformation. Log transformation compresses the scale of data, reducing the impact of extreme values and making the distribution more symmetric. This is a standard technique for handling skewed data where outliers are present, as it preserves the relative order of observations while mitigating outlier influence.

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Binning continuous variables

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Imputing outliers with the mean

Question 13

Which TWO of the following are examples of supervised learning algorithms?

Accepted Answer

Linear regression. Linear regression is a supervised learning algorithm because it learns a mapping from input features to a continuous target variable using labeled training data. The model minimizes the difference between predicted and actual values (e.g., via ordinary least squares) to make predictions on new data.

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K-means clustering

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Principal component analysis (PCA)

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Apriori algorithm

Question 14

A healthcare analytics team is building a predictive model to identify patients at high risk of readmission within 30 days of discharge. The dataset includes 50,000 patient records with 200 features, including demographics, vital signs, lab results, and historical admissions. The target variable is binary (readmitted or not). The team uses a logistic regression model and achieves an AUC of 0.72 on the test set. However, the model's calibration is poor: for patients predicted to have a 70% risk, the actual readmission rate is only 40%. The team wants to improve calibration without significantly reducing discrimination (AUC). The data scientist suggests applying Platt scaling. However, the team lead is concerned that Platt scaling may reduce the model's ability to rank patients correctly. Which of the following is the best course of action?

Accepted Answer

Apply Platt scaling on a held-out validation set to recalibrate the predicted probabilities without refitting the original model.. Platt scaling is a post-processing technique that fits a logistic regression model on the predicted probabilities from the original model using a held-out validation set. This recalibrates the probabilities without altering the ranking of patients (the AUC remains unchanged), directly addressing the poor calibration while preserving discrimination. Option C correctly describes this procedure.

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Remove poorly calibrated predictions by discarding all patients with predicted risk between 0.3 and 0.7.

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Ignore calibration because AUC is the only metric that matters for readmission risk models.

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Switch to a random forest model, which inherently produces better-calibrated probabilities.

Question 15

A data analyst at a marketing firm is tasked with segmenting customers based on their purchasing behavior. The dataset contains 10,000 customers with features such as annual spend, frequency of purchases, recency of last purchase, and average order value. The analyst decides to use k-means clustering. After standardizing the features, the analyst runs k-means with k=3, k=4, and k=5, and computes the silhouette score for each: k=3: 0.45, k=4: 0.52, k=5: 0.48. The analyst also plots the elbow curve and observes that the within-cluster sum of squares (WCSS) decreases sharply from k=2 to k=4, then levels off. Based on these results, what is the most appropriate number of clusters?

Accepted Answer

k=4. The silhouette score is highest at k=4 (0.52), indicating that clusters are well-separated and cohesive. The elbow curve shows WCSS decreasing sharply up to k=4 and then leveling off, suggesting that k=4 captures the optimal trade-off between model complexity and variance explained. Together, these metrics point to k=4 as the most appropriate number of clusters.

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k=2

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k=3

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k=5

Question 16

A data analyst is building a linear regression model to predict sales based on advertising spend across TV, radio, and newspaper channels. Which TWO diagnostics should the analyst perform to validate the model assumptions?

Accepted Answer

Q-Q plot to assess normality of residuals. A Q-Q plot is used to assess whether the residuals of a linear regression model are approximately normally distributed, which is a key assumption for valid inference (e.g., p-values and confidence intervals). Option E is correct because residual plots (e.g., fitted vs. residuals) are the standard diagnostic to check for homoscedasticity—constant variance of errors across all levels of the independent variables—another core assumption of ordinary least squares regression.

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Durbin-Watson test for autocorrelation

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Variance inflation factor (VIF) for multicollinearity

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Cook's distance to identify influential points

Question 17

A data analyst is preparing a logistic regression model to predict customer churn. After examining the exhibit, which data quality issue should the analyst address first?

Accepted Answer

Inconsistent data in total_charges. Inconsistent data formats, such as mixed numeric representations in a column like 'total_charges', will cause parsing errors when loading data into a logistic regression model. Inconsistent data must be standardized (e.g., to a uniform numeric type) before any other preprocessing steps, as it prevents the model from executing. This issue is more critical to address first than missing values or outliers, which can be handled later through imputation or transformation without blocking model execution.

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Duplicate customer IDs

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Missing values in total_charges

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Outliers in monthly_charges

Question 18

A healthcare analytics team is building a classification model to predict patient readmission within 30 days. The dataset contains 10,000 records with 30 features, including demographics, vital signs, lab results, and medication history. The target variable is imbalanced: 85% no readmission, 15% readmission. The team used logistic regression with default settings and achieved an accuracy of 85%, but the model predicted 'no readmission' for all patients. The lead analyst suspects the model is not learning due to class imbalance. The team has time to implement one corrective action before the next model review. Which action should the team take?

Accepted Answer

Apply SMOTE to oversample the readmission class. SMOTE (Synthetic Minority Oversampling Technique) directly addresses the class imbalance by generating synthetic samples for the minority class (readmission). This forces the logistic regression model to learn decision boundaries that separate the two classes, rather than defaulting to the majority class prediction. With 85% majority and 15% minority, accuracy alone is misleading, and SMOTE is a proven technique to improve recall for the minority class.

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Remove features with low variance to reduce noise

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Use accuracy as the evaluation metric to monitor improvement

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Switch to a random forest model with default settings

Question 19

Drag and drop the steps to normalize a database table from 1NF to 3NF in the correct order.

Accepted Answer

Step 1: Achieve 1NF by removing repeating groups and ensuring atomic values. Step 2: Achieve 2NF by removing partial dependencies. Step 3: Achieve 3NF by removing transitive dependencies.. Normalization proceeds from 1NF to 2NF to 3NF, then table creation and foreign keys.

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Step 1: Achieve 3NF by removing transitive dependencies. Step 2: Achieve 2NF by removing partial dependencies. Step 3: Achieve 1NF by removing repeating groups.

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Step 1: Achieve 2NF by removing partial dependencies. Step 2: Achieve 1NF by removing repeating groups. Step 3: Achieve 3NF by removing transitive dependencies.

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Step 1: Achieve 1NF by removing repeating groups. Step 2: Achieve 3NF by removing transitive dependencies. Step 3: Achieve 2NF by removing partial dependencies.

Question 20

Drag and drop the steps to implement a data classification policy in the correct order.

Accepted Answer

Define classification levels, assign ownership, label data, enforce access control, train employees. Classification involves defining levels, assigning ownership, labeling, access control, and training.

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Train employees, define classification levels, assign ownership, label data, enforce access control

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Label data, define classification levels, assign ownership, enforce access control, train employees

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Define classification levels, label data, assign ownership, enforce access control, train employees