DA0-002 Data Analysis Practice Test 13 — 15 Questions

Question 1

An analyst is performing a logistic regression to predict customer churn (yes/no). The model outputs a probability of 0.75 for a particular customer. Which of the following best describes the interpretation?

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

Accepted Answer

There is a 75% chance that the customer will churn. In logistic regression, the model outputs a probability between 0 and 1 that the positive class (churn = yes) occurs. A value of 0.75 means the model estimates a 75% probability that the customer will churn, given the input features. This is a probabilistic prediction, not a deterministic one, and it does not mean the customer will definitely churn.

Answer

The model predicts that the customer will not churn

Answer

The customer will definitely churn because the probability is above 0.5

Answer

The odds of churning are 0.75 to 1

Question 2

A marketing team uses K-means clustering to segment customers based on purchase history. To determine the optimal number of clusters, they plot the within-cluster sum of squares (WCSS) against k and look for an elbow. What is the purpose of this method?

Accepted Answer

To find the point where the rate of decrease in WCSS slows down. The elbow method helps choose k where adding more clusters yields diminishing returns in reducing variance.

Answer

To identify the value of k that minimizes WCSS

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To determine the initial centroids for the algorithm

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To ensure all clusters have equal size

Question 3

A time series of monthly sales data exhibits a clear upward trend over several years, with consistent peaks each December. Which components are present in this series?

Accepted Answer

Trend and seasonality. The time series exhibits an upward trend over several years and consistent peaks each December, which is seasonality. Therefore, the components present are trend and seasonality. Cyclical components are not mentioned, and irregular components are always present but not the focus.

Answer

Cyclical and irregular components only

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Seasonality and cyclical components only

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Trend and irregular components only

Question 4

In an A/B test, the null hypothesis states that there is no difference between the control and treatment groups. After running the test, the p-value is 0.04. Assuming α = 0.05, what is the correct conclusion?

Accepted Answer

Reject the null hypothesis. The p-value of 0.04 is less than the significance level α = 0.05, so we reject the null hypothesis. This means there is statistically significant evidence to suggest a difference between the control and treatment groups. The correct conclusion is to reject the null hypothesis.

Answer

Fail to reject the null hypothesis

Answer

Accept the null hypothesis

Answer

The test is invalid because the p-value is too low

Question 5

A data analyst notices that a dataset of customer ages has several missing values. Which method for handling missing data is most appropriate if the data is missing completely at random and the analyst wants to preserve sample size?

Accepted Answer

Impute with the mean age. When data is missing completely at random (MCAR) and the analyst wants to preserve sample size, mean imputation is the standard approach — it replaces missing values with the average of the observed values, retaining all rows and avoiding the bias that deletion would introduce. For MCAR data, mean imputation produces unbiased estimates of the mean (though it reduces variance).

Answer

Forward-fill using the previous value

Answer

Replace missing values with zero

Answer

Delete all rows with missing data

Question 6

A data analyst is cleaning a dataset and finds that some records have duplicate entries based on customer ID. Which data quality dimension is most directly affected by these duplicates?

Accepted Answer

Uniqueness. Uniqueness is the data quality dimension that measures whether each real-world entity appears exactly once in a dataset. Duplicate records based on customer ID directly violate uniqueness because the same customer is represented multiple times. Timeliness, consistency, and accuracy describe other dimensions and are not the primary issue when duplicates exist.

Answer

Timeliness

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Consistency

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Accuracy

Question 7

Which statistical test should be used to determine if there is a significant association between two categorical variables, such as gender and product preference?

Accepted Answer

Chi-square test. The chi-square test is specifically designed to test for independence between two categorical variables, such as gender (male/female) and product preference (e.g., product A/B/C). It compares observed frequencies in each category combination to the frequencies expected if the variables were independent, using the chi-square statistic. A significant result indicates that the variables are associated, not independent.

Answer

ANOVA

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Pearson correlation

Answer

t-test

Question 8

An analyst wants to compare the mean sales revenue across three different store regions. The data is normally distributed and variances are equal. Which statistical test is most appropriate?

Accepted Answer

ANOVA. ANOVA (Analysis of Variance) is the correct test for comparing means across three or more groups when data is normally distributed and variances are equal (homogeneity of variance). It tests the null hypothesis that all group means are equal using an F-statistic comparing between-group to within-group variance.

Answer

Two-sample t-test

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Paired t-test

Answer

Chi-square test

Question 9

A data team is preparing data for a clustering analysis. Which THREE of the following steps are commonly part of data cleaning?

Accepted Answer

Removing duplicate records. Option A (Removing duplicate records) is correct because duplicate rows distort distance calculations in clustering, causing the same observation to be counted multiple times and biasing cluster centroids, so deduplication is a standard data-cleaning step. Option B (Imputing missing values) is correct because clustering algorithms such as k-means cannot handle nulls, so missing entries must be filled via mean/median/mode imputation, k-NN, or similar methods before analysis. Option E (Capping outliers at the 5th and 95th percentiles) is correct because winsorizing extreme values limits their disproportionate influence on distance metrics and centroid placement, which is a recognized cleaning technique. Option C (Calculating the mean) is not a cleaning step but a descriptive statistic or profiling operation, and Option D (Training a regression model) is a modeling task, not data preparation, so neither belongs to data cleaning.

Answer

Calculating the mean

Answer

Training a regression model

Question 10

An analyst is conducting an A/B test on a new website layout. Which TWO of the following must be defined before the test begins?

Accepted Answer

The significance threshold (alpha). Option D is correct because the significance threshold (alpha), typically set at 0.05, must be fixed in advance to determine the probability of a Type I error and to provide the criterion against which the resulting p-value will be judged. Option E is correct because the required sample size must be calculated beforehand, based on the desired statistical power (commonly 0.80), the baseline conversion rate, and the minimum detectable effect, to ensure the test can reliably detect a real difference. Options A, B, and C are not pre-test definitions: the final conversion rates and the actual p-value are outcomes observed only after the test concludes, and the confidence interval for the lift is likewise a post-hoc computed estimate rather than an input specified before the test begins.

Answer

The final conversion rates for each group

Answer

The actual p-value from the test

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The confidence interval for the lift

Question 11

Which TWO of the following are true about Pearson correlation coefficient (r)?

Accepted Answer

It measures the strength and direction of a linear relationship. Option C is correct because the Pearson correlation coefficient r is specifically designed to quantify both the strength and the direction (positive or negative) of a linear association between two continuous variables. Option D is correct because r = +1 represents the maximum possible value, meaning the two variables lie exactly on a straight line with a positive slope, i.e., a perfect positive linear relationship. Option A is incorrect because r = 0 indicates no linear relationship, but a strong non-linear (e.g., curvilinear) relationship may still exist. Option B is incorrect because r ranges from -1 to +1, not 0 to 1; negative values indicate an inverse linear relationship. Option E is incorrect because Pearson r requires continuous, interval- or ratio-scaled variables, whereas categorical variables call for other measures such as chi-square, Cramér's V, or point-biserial correlation.

Answer

An r of 0 means no relationship exists

Answer

It ranges from 0 to 1

Answer

It can be used for categorical variables

Question 12

A retailer wants to test if a new website layout increases the average time spent on the site. They split traffic: control group (old layout) and treatment group (new layout). Which statistical test is most appropriate to compare the average time spent between the two groups?

Accepted Answer

Two-sample t-test. The question compares the means of a continuous outcome (average time spent) between two independent groups (control vs. treatment), which is exactly the two-sample t-test's purpose. The two-sample t-test evaluates whether the difference in group means is statistically significant, assuming approximately normal distributions or sufficiently large samples. It is the standard test for a two-group A/B comparison on a continuous metric.

Answer

ANOVA

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Pearson correlation

Answer

Chi-square test

Question 13

A data scientist builds a logistic regression model to predict customer churn (yes/no). The model outputs a probability of 0.75 for a particular customer. Which of the following best describes this output?

Accepted Answer

There is a 75% chance the customer will churn.. The output of a logistic regression model is a probability between 0 and 1. A value of 0.75 means the model estimates a 75% probability that the customer will churn (the positive class). This is a probabilistic prediction, not a certainty.

Answer

The customer will definitely churn.

Answer

The odds of churning are 0.75 to 1.

Answer

The model is 75% accurate.

Question 14

A dataset contains employee salaries ranging from $30,000 to $200,000. An analyst wants to scale the salaries to a range of 0 to 1 for use in a distance-based clustering algorithm. Which method should they use?

Accepted Answer

Min-max normalization. Min-max normalization rescales values linearly to a fixed range, typically [0, 1], using the formula (x − min) / (max − min). This is exactly what the analyst needs for a distance-based clustering algorithm, where features on different scales would otherwise dominate the distance metric. It preserves the relative ordering and shape of the distribution while bounding all values between 0 and 1.

Answer

Log transformation

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Robust scaling

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Z-score standardization

Question 15

A marketing team runs an A/B test on email subject lines. The p-value is 0.03 with α = 0.05. Which of the following is the correct interpretation?

Accepted Answer

Reject the null hypothesis; there is a statistically significant difference.. With a p-value of 0.03 and α = 0.05, the p-value is less than the significance level, so we reject the null hypothesis. This indicates a statistically significant difference at the 95% confidence level. The p-value is not the probability that the null hypothesis is true.

Answer

The result is not statistically significant at the 95% confidence level.

Answer

The probability that the null hypothesis is true is 3%.

Answer

Fail to reject the null hypothesis; no significant difference.