DA0-002 Data Analysis Practice Test 13 — 15 Questions

Question 1

Which data quality dimension is violated if a customer record has a missing phone number?

Accepted Answer

Completeness. Completeness refers to the extent to which data is not missing.

Answer

Consistency

Answer

Accuracy

Answer

Validity

Question 2

A simple linear regression model predicts sales (y) from advertising spend (x). The equation is y = 2.5x + 10, and R² = 0.81. Which interpretation is correct?

Accepted Answer

81% of the variation in sales is explained by advertising spend.. Slope indicates that each unit increase in x increases y by 2.5 units. R² of 0.81 means 81% of variance in y is explained by x.

Answer

The correlation between sales and advertising is 0.81.

Answer

When advertising is $0, sales are $2.5.

Answer

For every $1 increase in advertising, sales increase by $10 on average.

Question 3

A data analyst has a time series of monthly sales data. They observe that sales are consistently higher every December and lower every January. Which component of time series does this pattern represent?

Accepted Answer

Seasonality. Regular patterns that repeat within one year are seasonality.

Answer

Irregular

Answer

Cyclical

Answer

Trend

Question 4

Which data cleaning method involves replacing a missing value with the average of the available values in that column?

Accepted Answer

Mean imputation. Mean imputation replaces missing values with the column mean.

Answer

Interpolation

Answer

Listwise deletion

Answer

Forward-fill

Question 5

An analyst compares average sales across three different store locations using a statistical test. Which test is most appropriate?

Accepted Answer

ANOVA. ANOVA compares means across three or more groups.

Answer

t-test

Answer

Correlation analysis

Answer

Chi-square test

Question 6

In A/B testing, which factor is increased by having a larger sample size?

Accepted Answer

Statistical power. Larger sample size increases statistical power (ability to detect a true effect).

Answer

P-value

Answer

Effect size

Answer

Type I error rate

Question 7

A data analyst is preparing a dataset for a machine learning algorithm that assumes normally distributed features. Which TWO data transformation methods should the analyst consider to achieve this?

Accepted Answer

Square root transformation. Log transformation and square root transformation are both methods that can reduce skewness and help make data more normally distributed. Z-score standardization only centers and scales the data but does not change the shape of the distribution. One-hot encoding is for categorical variables, and min-max normalization is for scaling, not normality.

Answer

One-hot encoding

Answer

Z-score standardization

Answer

Min-max normalization

Question 8

A retail company wants to segment its customers based on purchase history. Which THREE methods are appropriate for customer segmentation?

Accepted Answer

RFM analysis. K-means clustering, hierarchical clustering, and RFM analysis are common segmentation techniques. Linear regression and t-test are not segmentation methods.

Answer

Linear regression

Answer

t-test

Question 9

A data analyst is cleaning a dataset and identifies several outliers. Which TWO methods are appropriate for handling outliers?

Accepted Answer

Capping. Capping (winsorizing) and removal are common outlier treatments. Mean imputation is for missing values, and min-max normalization is scaling.

Answer

Mean imputation

Answer

Min-max normalization

Answer

Forward-fill

Question 10

A data analyst calculates the mean, median, and mode of a dataset. Which measure of central tendency is most affected by extreme outliers?

Accepted Answer

Mean. The mean is sensitive to extreme values because it includes all data points in its calculation, whereas median and mode are more robust.

Answer

Range

Answer

Median

Answer

Mode

Question 11

A retail company wants to analyze monthly sales data over the past three years to identify long-term trends. Which component of time series analysis is most relevant for this goal?

Accepted Answer

Trend. The trend component represents the long-term direction of the data, which is exactly what the company wants to identify.

Answer

Irregular component

Answer

Cyclical component

Answer

Seasonality

Question 12

An analyst runs a simple linear regression with an R² value of 0.85. Which interpretation is correct?

Accepted Answer

85% of the variance in the dependent variable is explained by the independent variable.. R² represents the proportion of variance in the dependent variable explained by the independent variable. 0.85 means 85% is explained.

Answer

The slope of the regression line is 0.85.

Answer

The independent variable is 85% correlated with the dependent variable.

Answer

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

Question 13

A data scientist is performing a hypothesis test with a significance level α=0.05. The p-value obtained is 0.03. What should the scientist conclude?

Accepted Answer

Reject the null hypothesis because the p-value is less than the significance level.. Since 0.03 < 0.05, we reject the null hypothesis, indicating statistically significant evidence against it.

Answer

Fail to reject the null hypothesis because the p-value is greater than 0.01.

Answer

The test is inconclusive, need a larger sample size.

Answer

Accept the null hypothesis because the p-value is small.

Question 14

A marketing team runs an A/B test comparing two webpage designs. The null hypothesis states there is no difference in conversion rates. The p-value is 0.08 at α=0.05. Which is the correct interpretation?

Accepted Answer

There is insufficient evidence to conclude a difference between the designs.. Since p > α, we fail to reject the null hypothesis, meaning no statistically significant difference was found.

Answer

The null hypothesis is rejected, indicating the designs are different.

Answer

The alternative hypothesis is accepted, showing the new design is better.

Answer

There is a statistically significant difference between the designs.

Question 15

A dataset contains a feature with values ranging from 10 to 1000. The analyst applies min-max normalization to scale the feature between 0 and 1. What is the normalized value of 520?

Accepted Answer

0.515. Min-max normalization formula: (x - min) / (max - min) = (520 - 10) / (1000 - 10) = 510 / 990 = 0.515.

Answer

0.510

Answer

0.480

Answer

0.520