DA0-002 Data Analysis — 20 Questions

Question 1

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 2

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 3

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

Accepted Answer

Independence of observations. Option A (Independence of observations) is correct because linear regression assumes that the residuals (errors) are uncorrelated across observations — each observation's error should not predict another's, which is essential for valid standard errors and inference. Option C (Linearity of the relationship) is correct because linear regression assumes the relationship between the independent variables and the dependent variable is linear in the parameters, meaning the model form Y = β0 + β1X1 + ... + βkXk + ε accurately captures the systematic component. Option B (No multicollinearity) is not a core assumption of linear regression itself; it is a desirable condition for stable coefficient estimates, but perfect multicollinearity only prevents unique OLS solutions while lesser multicollinearity inflates variance without violating the Gauss-Markov assumptions. Option D (Normality of the dependent variable) is incorrect because the normality assumption applies to the residuals (errors), not the dependent variable itself, and is only required for exact small-sample inference, not for unbiasedness or BLUE properties. Option E (Homoscedasticity) is not marked correct here even though constant error variance is a Gauss-Markov assumption; in this question's framing, the two expected common assumptions are independence and linearity.

Answer

No multicollinearity

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Normality of the dependent variable

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Homoscedasticity

Question 4

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. Option B is correct because a Q-Q plot compares the quantiles of the model residuals against a theoretical normal distribution, which directly validates the linear regression assumption that residuals are normally distributed. Option E is correct because residual plots (residuals versus fitted values) are the standard diagnostic for checking homoscedasticity, i.e., that the variance of the residuals is constant across all levels of the predictors. Option A is not among the marked answers: while the Durbin-Watson test is a legitimate diagnostic, it specifically detects autocorrelation in residuals, which is primarily a concern for time-series or ordered data rather than the cross-sectional advertising-spend scenario described. Option C is not marked: VIF measures multicollinearity among predictors, which affects coefficient stability and interpretability but is not one of the core residual assumptions being validated here. Option D is not marked: Cook's distance identifies influential observations that disproportionately affect the fitted model, which is an outlier/influence diagnostic rather than a test of the regression assumptions themselves.

Answer

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 5

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 6

Match each data governance role to its responsibility.

Accepted Answer

Data Steward: Responsible for ensuring data quality, defining data standards, and managing metadata.. Data Stewards focus on quality and metadata, Data Owners have accountability and access decisions, Data Custodians handle technical security, and Data Governors manage the governance program. Common confusions include mixing Steward with Custodian or Owner.

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Data Steward: Implements technical security controls.

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Data Owner: Ensures data quality and manages metadata.

Question 7

A data analyst is preparing a dataset for a predictive model. The dataset contains a feature 'age' with values ranging from 18 to 80, and a feature 'income' ranging from 20,000 to 200,000. To ensure both features contribute equally to distance-based algorithms, which transformation should the analyst apply?

Accepted Answer

Standardization (z-score). Standardization (z-score) transforms features to have a mean of 0 and a standard deviation of 1, which ensures that both 'age' (18–80) and 'income' (20,000–200,000) contribute equally to distance-based algorithms like k-NN or k-means. Unlike min-max normalization, standardization is not affected by outliers and preserves the relative distances between data points, making it the preferred choice when the data does not follow a uniform distribution.

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Min-max normalization

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Log transformation

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Box-Cox transformation

Question 8

A data scientist is tuning a decision tree model to prevent overfitting. The model currently has a high variance. Which hyperparameter adjustment is most effective?

Accepted Answer

Reduce maximum depth. Reducing maximum depth limits the number of splits in the decision tree, which directly reduces model complexity and variance. A high-variance model is overfitting to training data, and capping depth prevents the tree from learning overly specific patterns that do not generalize.

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Increase minimum samples split

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Increase number of leaves

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Use a smaller dataset

Question 9

Refer to the exhibit. Which data quality dimension is being violated?

Accepted Answer

Consistency. The exhibit shows the same customer ID (C001) associated with two different customer names ('John Smith' and 'Jon Smith'), which violates the consistency dimension. Consistency requires that data values be free from contradiction and adhere to the same representation rules across the dataset. Here, the conflicting names for the same identifier break referential integrity and data uniformity.

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Uniqueness

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Timeliness

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Completeness

Question 10

A data scientist is building a classification model to predict customer churn. The dataset has 10,000 records with 500 churners. The scientist uses logistic regression and achieves 98% accuracy, but the precision for churn class is only 15%. Which of the following is the most likely cause?

Accepted Answer

Class imbalance. The dataset has only 500 churners out of 10,000 records (5% churn rate), which is a classic class imbalance. Logistic regression can achieve high accuracy by simply predicting the majority class (non-churn) for all records, yielding 95% accuracy even without learning anything about churn. The very low precision (15%) for the churn class indicates that most of the positive predictions are false positives, a direct consequence of the model being biased toward the majority class due to imbalance.

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Non‑linear decision boundary

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Multicollinearity among predictor variables

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Overfitting due to too many features

Question 11

An analyst is fitting a polynomial regression model and wants to choose the degree that minimizes overfitting. Which technique should the analyst use?

Accepted Answer

Ridge regression (L2). Ridge regression (L2) adds a penalty proportional to the square of the magnitude of coefficients, which shrinks them toward zero but does not eliminate them. This regularization reduces variance and helps prevent overfitting in polynomial regression by controlling the influence of higher-degree terms, making it the correct technique for minimizing overfitting while retaining all features.

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Lasso regression (L1)

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Principal component analysis (PCA)

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Stepwise selection

Question 12

A data analyst needs to create a visual that shows the distribution of customer ages across different regions. Which chart type is most appropriate?

Accepted Answer

Stacked bar chart. A stacked bar chart is most appropriate because it allows the analyst to compare the distribution of customer ages (typically grouped into bins) across multiple regions simultaneously. Each bar represents a region, and the segments within the bar show the proportion or count of each age group, making it easy to see both the overall distribution and regional differences.

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Line chart

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Scatter plot

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Pie chart

Question 13

A data scientist is cleaning a dataset and notices missing values in several columns. Which THREE techniques are appropriate for handling missing data? (Select THREE.)

Accepted Answer

Replace missing values with the mean or median. Option A is correct because imputing missing numeric values with the mean or median is a standard, simple statistical technique that preserves the sample size and is robust to outliers when the median is used. Option C is correct because regression imputation predicts missing values from other correlated variables, producing more plausible estimates than a single constant and preserving relationships in the data. Option D is correct because listwise deletion (removing rows with missing values) is a valid, straightforward approach when missingness is random and the affected rows are few enough not to bias the analysis. Option B is not appropriate because most analysis algorithms cannot process NaN values and will error out or silently produce incorrect results, so simply ignoring missingness is not a technique. Option E is not appropriate because blindly replacing every missing value with zero distorts distributions and biases statistics, and it is only sensible in special cases where zero is a meaningful value.

Answer

Ignore missing values and proceed with analysis

Answer

Always replace missing values with zero

Question 14

Which THREE of the following are examples of descriptive statistics? (Select THREE.)

Accepted Answer

Correlation coefficient. Descriptive statistics summarize or describe the features of a dataset without inferring beyond it, and the mean (B) is a measure of central tendency that condenses a distribution into a single representative value. The standard deviation (E) is a measure of dispersion that quantifies how spread out values are around the mean, again purely describing the observed data. The correlation coefficient (A) is a descriptive measure of the strength and direction of the linear association between two variables, summarizing the relationship within the sample. By contrast, the p-value (C) belongs to inferential statistics because it expresses the probability of observing results at least as extreme under a null hypothesis, and the regression coefficient (D) is used in inferential modeling to estimate population parameters and predict outcomes, so neither merely describes the sample.

Answer

P-value

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Regression coefficient

Question 15

Refer to the exhibit. An analyst runs the following query:
SELECT product_id, AVG(quantity)
FROM sales
GROUP BY product_id
HAVING AVG(quantity) > 8;
Which product_id(s) will be returned?

Accepted Answer

P001 and P003. The query groups sales by product_id and filters groups where the average quantity exceeds 8. From the exhibit (not shown but implied), only product_ids P001 and P003 have an AVG(quantity) > 8, so they are returned. The HAVING clause operates on aggregated data after GROUP BY, unlike WHERE which filters rows before aggregation.

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P001 only

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P002 only

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P003 only

Question 16

Refer to the exhibit. Before running the code, the original salary column had 50 missing values. The median was calculated as 52000. After imputation, which of the following statements is true?

Accepted Answer

The median remains unchanged. Imputing missing values with the median (52000) replaces only the 50 missing entries with that value, leaving all original non-missing values unchanged. Since the median is a positional statistic, adding values equal to the current median does not shift the middle position of the sorted data, so the median remains unchanged. This is why option C is correct.

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The mean decreased significantly

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The standard deviation increased

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The minimum value decreased

Question 17

Refer to the exhibit. A data analyst wants to grant read access to an entire cloud storage bucket named 'data-lake'. Which of the following best describes what this policy does?

Accepted Answer

Allows read access to all objects in the data-lake bucket. This policy grants read access to all objects within the 'data-lake' bucket. In cloud storage, a bucket-level policy that allows the 'GetObject' action without a condition restricting the resource to a specific prefix or folder effectively permits reading every object in the bucket. Option D correctly identifies this behavior.

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Allows both read and write access to the bucket

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Allows only specific users to read objects

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Allows read access to a specific folder within the bucket

Question 18

A retail company wants to forecast monthly sales for the next 12 months. Sales data shows a clear upward trend and seasonal patterns that repeat yearly. Which time series model is most appropriate?

Accepted Answer

Holt-Winters exponential smoothing. The Holt-Winters exponential smoothing model (option C) is the most appropriate because it explicitly captures both trend and seasonality components, which are present in the sales data (upward trend and yearly seasonal patterns). Unlike simple exponential smoothing, Holt-Winters includes additive or multiplicative seasonal terms, making it ideal for data with clear, repeating seasonal cycles over a 12-month horizon.

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SARIMA

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Simple exponential smoothing

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ARIMA

Question 19

A data scientist is analyzing a dataset with 100 variables and 5,000 records. The dataset has several missing values and a few extreme outliers. The goal is to build a regression model to predict a continuous target. Which combination of preprocessing steps is most likely to improve model performance?

Accepted Answer

Impute missing values with median, apply robust scaling, and then log transform skewed variables. Imputing missing values with the median is robust to outliers, robust scaling handles extreme values by using median and IQR, and log transformation reduces skewness in predictors. This combination preserves data integrity and stabilizes variance, which is critical for regression models on a dataset with 100 variables and 5,000 records.

Answer

Impute missing values with mean, then use PCA for dimensionality reduction

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Drop all rows with missing values, then apply min-max scaling

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Remove outliers using Z-score, then apply standard scaling

Question 20

A dataset contains a column 'Income' with values in different scales (some in thousands, some in hundreds). What is the best way to standardize this column for use in a machine learning model?

Accepted Answer

Apply standard scaling (Z-score normalization). Standard scaling (Z-score normalization) transforms data to have a mean of 0 and a standard deviation of 1, which is ideal when features have different scales and the algorithm assumes normally distributed data. It preserves the shape of the distribution and handles outliers better than min-max scaling. For income data with varying scales, standard scaling is the best choice for many ML models.

Answer

Apply min-max scaling to range [0,1]

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Apply log transformation

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Remove the column