DA0-002 Data Analysis Practice Test 6 — 15 Questions

Question 1

A data analyst is preparing to build a predictive model. Which TWO steps are essential to ensure model validity? (Choose two.)

Accepted Answer

Perform cross-validation. Cross-validation is essential for model validity because it partitions the data into multiple folds, training on k-1 folds and validating on the remaining fold, which provides a robust estimate of model performance and reduces overfitting. This technique ensures that the model generalizes well to unseen data by repeatedly testing different subsets, making it a standard practice in predictive modeling.

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Increase model complexity

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Avoid feature selection

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Use the entire dataset for training

Question 2

In multiple linear regression, which TWO assumptions are critical for unbiased coefficient estimates? (Choose two.)

Accepted Answer

Linearity: the relationship between predictors and response is linear. For unbiased coefficient estimates in multiple linear regression, the linearity assumption (A) ensures that the model correctly specifies the functional form, and the independence of errors assumption (E) ensures that errors are uncorrelated, both of which are required for ordinary least squares (OLS) estimates to be unbiased. Homoscedasticity (D) is not required for unbiasedness but for efficiency (Gauss-Markov theorem).

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Large sample size

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Normality of errors

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Homoscedasticity: errors have constant variance

Question 3

A data analyst is performing data cleaning. Which THREE steps are part of this process? (Choose three.)

Accepted Answer

Correcting inconsistent data. Correcting inconsistent data (Option A) is a core data cleaning step because it ensures that values follow a consistent format, such as standardizing date formats (e.g., 'MM/DD/YYYY' vs 'DD-MM-YYYY') or fixing capitalization (e.g., 'USA' vs 'usa'). This process directly addresses data quality issues that arise from human entry errors or system differences, making the dataset reliable for analysis.

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Normalization

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Feature engineering

Question 4

You are a data analyst at a logistics company. The operations manager wants to reduce delivery delays. You have historical data including order date, delivery date, distance, weather conditions, and driver ID. Initial analysis shows that the average delivery time has increased over the past six months. You suspect that weather is a contributing factor, but you need to confirm. The company also wants to build a model to predict delivery times to better manage customer expectations. The data contains missing values for weather conditions in about 10% of records, and some driver IDs are incorrect. You have limited time and resources. What should you do first?

Accepted Answer

Start by cleaning the data: correct driver IDs and decide how to handle missing weather data, then perform exploratory data analysis. Data cleaning and exploratory data analysis (EDA) are foundational steps before any modeling or time series work. With missing weather data (10%) and incorrect driver IDs, proceeding without cleaning would introduce bias and errors. EDA will reveal patterns, correlations, and data quality issues, enabling informed decisions on imputation and feature engineering for the predictive model.

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Immediately focus on time series analysis to look for patterns

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Collect more data to fill missing values

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Build a predictive model using all available data after imputing missing weather data

Question 5

A healthcare analytics team is analyzing patient readmission rates. They have a dataset with thousands of records including patient age, diagnosis, length of stay, number of prior admissions, and discharge date. The goal is to identify key factors influencing readmission and create a model to predict high-risk patients. The data is imbalanced: only 5% of patients are readmitted within 30 days. The team plans to use logistic regression. What is the most appropriate approach?

Accepted Answer

Apply oversampling techniques like SMOTE to the training set. With imbalanced data, logistic regression can be biased toward the majority class. Oversampling the minority class (e.g., SMOTE) helps the model learn patterns for readmission. Using accuracy as a metric would be misleading. Removing majority samples discards valuable data. Using data as-is often fails to predict the minority class.

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Use the dataset as is because logistic regression handles imbalance

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Remove most of the non-readmitted patients to balance the dataset

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Use accuracy as the evaluation metric

Question 6

A marketing analyst wants to segment customers based on purchasing behavior and demographics. The dataset includes continuous variables (spending amount, frequency) and categorical variables (region, gender). The analyst decides to use k-means clustering. What should the analyst do to prepare the data?

Accepted Answer

Standardize continuous variables and one-hot encode categorical variables. K-means clustering relies on Euclidean distance, which is sensitive to the scale of features. Standardizing continuous variables (e.g., spending amount, frequency) ensures they contribute equally to distance calculations, while one-hot encoding categorical variables (e.g., region, gender) converts them into numerical form without implying ordinal relationships, allowing k-means to process mixed data types correctly.

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Use raw data because k-means works with mixed types

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Apply PCA first to reduce dimensionality

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Remove categorical variables entirely

Question 7

A financial analyst is building a model to predict stock price movements. The data is time series with daily prices. The analyst wants to use a regression model but notices that the residuals are autocorrelated. What adjustment should be made?

Accepted Answer

Use a time series model like ARIMA instead. When residuals from a regression model on time series data exhibit autocorrelation, the standard ordinary least squares (OLS) assumptions are violated, leading to biased standard errors and unreliable inference. An ARIMA model is specifically designed to handle autocorrelated time series by explicitly modeling the autoregressive (AR) and moving average (MA) components, making it the correct adjustment to capture the temporal dependencies in stock price movements.

Answer

Use cross-validation to validate the model

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Add more predictors to the regression model

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Transform the data to remove autocorrelation (e.g., differencing)

Question 8

A data analyst is building a supervised learning model to predict customer churn. The target variable is binary (churn = yes/no). Which TWO modeling techniques are appropriate for this task? (Select two.)

Accepted Answer

Logistic regression. Logistic regression is appropriate because it models the probability of a binary outcome (churn yes/no) using a logistic function, making it a standard choice for binary classification tasks. It outputs a value between 0 and 1, which can be thresholded to predict the class label.

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K-means clustering

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Linear regression

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Apriori algorithm

Question 9

A data analyst at a retail company is building a multiple linear regression model to forecast weekly sales. The dataset contains 50 predictor variables, including store size, promotional spend, holiday indicators, and many others. After training the model, the analyst observes an R-squared of 0.99 on the training set but only 0.55 on the holdout test set. Which action should the analyst take first to address this discrepancy?

Accepted Answer

Remove highly correlated predictor variables and apply regularization (e.g., Ridge or Lasso).. The high R-squared of 0.99 on training data versus 0.55 on test data is a classic sign of overfitting, where the model has learned noise and specific patterns in the training set that do not generalize. Removing highly correlated predictors reduces multicollinearity and model complexity, while regularization (Ridge or Lasso) penalizes large coefficients, shrinking them to prevent overfitting. This is the most direct first step to improve generalization.

Answer

Add more predictor variables to increase the training R-squared further.

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Use k-fold cross-validation with a different random seed to get a more reliable test set estimate.

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Increase the number of hidden layers in the model to capture more complexity.

Question 10

A marketing analyst wants to segment customers based on their purchase history, including total spent, number of transactions, and average order value. The analyst runs k-means clustering with k=5 on the raw data but notices that the cluster assignments change significantly every time the algorithm is executed. What should the analyst do first to obtain consistent and meaningful clusters?

Accepted Answer

Normalize the features and set a fixed random seed for the initial centroids.. The instability in cluster assignments is caused by the algorithm's sensitivity to the scale of features and the random initialization of centroids. Normalizing the features ensures that each variable contributes equally to the distance calculations, while setting a fixed random seed makes the initial centroid selection deterministic, leading to reproducible results.

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Switch to hierarchical clustering, which does not require specifying k.

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Increase the number of clusters to k=10 to capture more detail.

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Use principal component analysis (PCA) to reduce the number of features to two.

Question 11

An analyst computed the mean, median, and mode of a dataset and found they are all equal. Which of the following best describes the distribution?

Accepted Answer

Symmetric. When mean, median, and mode are equal, the distribution is symmetric and unimodal, often resembling a normal distribution.

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Bimodal

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Negatively skewed

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Positively skewed

Question 12

A data analyst wants to compare the average revenue per customer between two marketing campaigns (A and B). The analyst is unsure if the data follows a normal distribution. Which statistical test is most appropriate for comparing the means of the two groups?

Accepted Answer

Two-sample t-test. For comparing means of two independent groups, the t-test is the standard parametric test. If normality is violated, a non-parametric alternative like Mann-Whitney U could be used, but the t-test is robust for moderate sample sizes.

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Pearson correlation

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Chi-square test

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ANOVA

Question 13

A data analyst is performing a multiple linear regression with three predictors. The model output shows an R-squared of 0.85 and an adjusted R-squared of 0.80. Which of the following is the best interpretation of the difference between these two values?

Accepted Answer

One or more predictors may not be contributing meaningfully. Adjusted R-squared penalizes for adding predictors that do not improve the model significantly; a gap suggests some predictors may be irrelevant or the sample size is small.

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The model is overfitted, so all predictors should be removed

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The model has high multicollinearity

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The residuals are not normally distributed

Question 14

A data scientist is preparing data for a K-means clustering algorithm. The dataset contains features measured in different units (e.g., income in dollars and age in years). Which preprocessing step is most critical before running K-means?

Accepted Answer

Standardize or normalize the features. K-means is sensitive to the scale of features because it uses Euclidean distance. Min-max normalization or standardization ensures all features contribute equally.

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Remove outliers

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Encode categorical variables

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Perform feature selection

Question 15

In a time series analysis, a retail analyst observes consistent peaks in sales every December and troughs every February. This pattern repeats annually. Which component of time series does this represent?

Accepted Answer

Seasonality. Seasonality refers to regular patterns that repeat over fixed periods, such as months or quarters.

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Irregular

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Trend

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Cyclical