DA0-002 Data Analysis Practice Test 11 — 15 Questions

Question 1

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.. Since p-value > 0.05, we fail to reject the null hypothesis; the observed difference is not statistically significant.

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 2

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. For normally distributed data, mean imputation is reasonable and preserves the mean.

Answer

Forward-fill

Answer

Delete all rows with missing data

Answer

Mode imputation

Question 3

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 4

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.. Negative correlation means as one variable increases, the other decreases; strength is high (close to -1).

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 5

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 6

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. High p-value indicates the predictor is not statistically significant; it may be removed to simplify the model.

Answer

Transform the predictor

Answer

Keep the predictor regardless

Answer

Increase the sample size

Question 7

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. Removing exact duplicates and standardizing identifiers help resolve duplicates.

Answer

Impute missing values with mean

Answer

Delete any row with a duplicate email address

Answer

Apply Z-score standardization

Question 8

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). Sample size calculation depends on desired power, effect size, and significance level.

Answer

Seasonality of the data

Answer

Number of clusters in k-means

Question 9

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). Capping outliers or transforming the variable can reduce their influence.

Answer

Use min-max normalization

Answer

Increase the sample size

Answer

Replace outliers with the mean

Question 10

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. Descriptive statistics summarize data using measures like mean, median, and mode to describe 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 11

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. Since p-value (0.03) < α (0.05), we reject the null hypothesis, indicating a statistically significant difference in mean sales between the two 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 12

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 of 0.85 means 85% of the variance in house prices is explained by square footage.

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

Question 13

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?

Accepted Answer

There is a 75% chance that the customer will churn. Logistic regression outputs the probability that the event (churn) occurs, given the input features.

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 14

A dataset contains features with vastly different scales (e.g., age 0-100 and income 0-1,000,000). Which data transformation should be applied before using a K-nearest neighbors algorithm?

Accepted Answer

Min-max normalization. Distance-based algorithms like KNN require features on similar scales; min-max normalization is appropriate.

Answer

No transformation is needed

Answer

Log transformation

Answer

Z-score standardization

Question 15

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

Answer

To determine the initial centroids for the algorithm

Answer

To ensure all clusters have equal size