DA0-002 Data Analysis Practice Test 12 — 15 Questions

Question 1

A data scientist is building a model to predict customer churn (yes/no). After training a logistic regression model, the coefficient for 'monthly charges' is 0.05 with a p-value of 0.03. Which interpretation is correct at α=0.05?

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

Accepted Answer

For every unit increase in monthly charges, the odds of churn increase by about 5%.. In logistic regression, coefficients are in log-odds units. A coefficient of 0.05 means that for a one-unit increase in monthly charges, the log-odds of churn increase by 0.05. Exponentiating gives e^0.05 ≈ 1.051, so the odds increase by about 5.1%. The p-value of 0.03 is less than α=0.05, so the effect is statistically significant.

Answer

The model's R-squared is 0.05.

Answer

Monthly charges decrease the probability of churn.

Answer

Monthly charges have no significant effect on churn.

Question 2

In a simple linear regression model y = 2.5 + 1.2x, what is the predicted value of y when x = 10?

Accepted Answer

14.5. Plug x=10: y = 2.5 + 1.2*10 = 2.5 + 12 = 14.5.

Answer

12.0

Answer

13.7

Answer

10.0

Question 3

A data analyst is performing time series analysis on monthly sales data and notices a consistent pattern of higher sales every December. Which component of time series does this represent?

Accepted Answer

Seasonality. Seasonality refers to a repeating pattern that occurs at fixed, predictable intervals within a year, such as higher sales every December or increased traffic every Monday. Because the December spike recurs annually at the same calendar point, it is a seasonal component. Seasonality is distinguished from trend (long-term direction) and cyclical (irregular multi-year fluctuations) by its fixed, calendar-based period.

Answer

Trend

Answer

Irregular component

Answer

Cyclical

Question 4

An analyst runs an A/B test with 1000 users per group and observes a conversion rate of 5% in the control and 6% in the treatment. The p-value is 0.12. What should the analyst conclude?

Accepted Answer

The difference is not statistically significant at the 0.05 level.. A p-value of 0.12 is greater than the conventional significance level of 0.05, so the null hypothesis of no difference cannot be rejected. The observed difference between 5% and 6% conversion rates is not statistically significant at the 0.05 level. The analyst should conclude that there is insufficient evidence to claim the treatment outperforms control.

Answer

The sample size is too small to detect an effect.

Answer

The treatment significantly outperforms control.

Answer

There is a 12% chance the treatment is better.

Question 5

A dataset has missing values in the 'age' column. The distribution of age is approximately normal with few outliers. Which imputation method is most appropriate?

Accepted Answer

Mean imputation. Mean imputation is appropriate when the data is approximately normally distributed and has few outliers, because the mean is a representative measure of central tendency for symmetric distributions. It preserves the overall mean of the variable and is simple to implement. Forward-fill and mode imputation are better for time-series or categorical data, respectively, and deleting rows can introduce bias and reduce sample size.

Answer

Forward-fill

Answer

Delete all rows with missing data

Answer

Mode imputation

Question 6

Which measure best describes the spread of the middle 50% of a dataset?

Accepted Answer

IQR. Interquartile range (IQR) is the range between Q1 and Q3, covering the middle 50%.

Answer

Range

Answer

Standard deviation

Answer

Variance

Question 7

An analyst calculates a Pearson correlation coefficient of -0.8 between advertising spend and customer churn rate. Which interpretation is correct?

Accepted Answer

Increasing advertising spend is associated with decreasing churn rate.. A Pearson correlation of -0.8 indicates a strong negative linear relationship, meaning that as advertising spend increases, customer churn rate tends to decrease. The correct interpretation is directional association, not causation — the coefficient describes the strength and direction of the linear relationship between the two variables.

Answer

There is a weak positive relationship.

Answer

Advertising spend causes churn to decrease.

Answer

64% of the variance in churn is explained by spend.

Question 8

A data analyst uses the elbow method to determine the number of clusters for k-means. The plot shows a sharp bend at k=3 and a small bend at k=5. What is the recommended number of clusters?

Accepted Answer

3. The elbow method suggests choosing k where the decrease in inertia becomes marginal; the sharp bend at 3 indicates the optimal k.

Answer

5

Answer

The method is inconclusive.

Answer

2

Question 9

In a multiple regression model, one predictor has a high p-value (0.45). What should the analyst consider doing?

Accepted Answer

Remove the predictor from the model. A p-value of 0.45 is far above the conventional significance threshold (typically 0.05), meaning there is no statistically significant evidence that this predictor is associated with the outcome variable after accounting for the other predictors. Removing non-significant predictors simplifies the model, reduces multicollinearity risk, and improves interpretability without meaningful loss of predictive power. This is the standard first step in backward elimination-style model refinement.

Answer

Transform the predictor

Answer

Keep the predictor regardless

Answer

Increase the sample size

Question 10

A data analyst is cleaning a customer dataset. Which two actions are appropriate for handling duplicate records? (Choose TWO)

Accepted Answer

Remove all rows with identical values in every field. Option C is correct because removing rows whose values are identical across every field eliminates exact duplicate records, which is a standard and safe deduplication step in data cleaning. Option E is correct because fuzzy matching algorithms (e.g., Levenshtein distance or Jaro-Winkler similarity) identify near-duplicates that differ slightly due to typos, formatting, or abbreviations, allowing the analyst to review and consolidate them. Option A is incorrect because mean imputation addresses missing values, not duplicate records. Option B is incorrect because deleting every row with a duplicate email address is overly aggressive and may remove legitimate distinct customers who share an email. Option D is incorrect because Z-score standardization is a scaling technique for numeric features and does not handle duplicates.

Answer

Impute missing values with mean

Answer

Delete any row with a duplicate email address

Answer

Apply Z-score standardization

Question 11

A data scientist is conducting an A/B test with a significance level of 0.05. Which three factors should be considered when calculating the required sample size? (Choose THREE)

Accepted Answer

Statistical power (e.g., 0.80). Option B is correct because statistical power (typically 0.80 or 80%) directly determines sample size: higher power requires a larger sample to reliably detect a true effect if one exists. Option C is correct because the minimum detectable effect size is a core input to sample-size formulas (e.g., n ≈ 16σ²/Δ² for a two-sample t-test at α=0.05, power=0.80); smaller effects demand much larger samples. Option E is correct because the significance level α (here 0.05) sets the Type I error threshold and appears in every sample-size calculation, with smaller α requiring larger samples. Option A is not a direct input to the standard sample-size formula, though seasonality may inform variance estimates or test design. Option D is unrelated: the number of clusters in k-means is an unsupervised clustering parameter and has no bearing on A/B test sample-size determination.

Answer

Seasonality of the data

Answer

Number of clusters in k-means

Question 12

A dataset contains outliers in a feature that will be used for linear regression. Which two outlier treatment methods are appropriate? (Choose TWO)

Accepted Answer

Cap the outliers at a percentile (e.g., 99th percentile). Option A (Cap the outliers at a percentile, e.g., 99th percentile) is correct because winsorizing/capping replaces extreme values with a boundary value at a chosen percentile, which preserves the data points while reducing the leverage that extreme values exert on the least-squares fit, thereby limiting their disproportionate influence on the regression coefficients. Option D (Remove the outlier rows) is correct because deleting the offending observations eliminates their influence entirely, which is a standard and valid treatment when outliers are identified as erroneous or unrepresentative and the remaining sample is still adequate for fitting the model. Option B (Use min-max normalization) is not appropriate because rescaling features to a [0,1] range is a transformation that preserves the relative positions and leverage of outliers, so it does nothing to mitigate their effect on the regression. Option C (Increase the sample size) does not treat the outliers themselves; adding more data does not reduce the influence of existing extreme points and may even introduce more outliers. Option E (Replace outliers with the mean) is generally inappropriate because substituting the mean distorts the distribution, artificially reduces variance, and biases the regression estimates rather than properly handling the extreme values.

Answer

Use min-max normalization

Answer

Increase the sample size

Answer

Replace outliers with the mean

Question 13

A data analyst calculates the mean, median, and mode of a dataset. Which of the following best describes how these measures are used in descriptive statistics?

Accepted Answer

To describe the central tendency of the data. Mean, median, and mode are measures of central tendency, which are used in descriptive statistics to summarize the center point of a dataset. They describe where the data tends to cluster. The correct answer is C because these measures describe the central tendency.

Answer

To identify outliers using standard deviation

Answer

To test hypotheses about population parameters

Answer

To determine the probability of an event

Question 14

An analyst is comparing the average sales of two different store locations using a t-test. The p-value obtained is 0.03, and the significance level is 0.05. What should the analyst conclude?

Accepted Answer

Reject the null hypothesis; there is a significant difference. When the p-value (0.03) is less than the significance level (0.05), the analyst rejects the null hypothesis. This means there is statistically significant evidence of a difference between the average sales of the two store locations.

Answer

Fail to reject the null hypothesis; no significant difference

Answer

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

Answer

Accept the null hypothesis; the means are equal

Question 15

A data scientist builds a simple linear regression model to predict house prices based on square footage. The model yields an R-squared value of 0.85. Which statement accurately interprets this result?

Accepted Answer

The model explains 85% of the variability in house prices. R-squared (the coefficient of determination) measures the proportion of variance in the dependent variable explained by the independent variable(s). An R² of 0.85 means 85% of the variability in house prices is accounted for by the square footage in this linear model. It is a goodness-of-fit measure, not a probability, slope, or count of points on the line.

Answer

The slope of the regression line is 0.85

Answer

85% of the data points lie exactly on the regression line

Answer

There is a 85% chance that square footage causes higher prices