DA0-002 Data Analysis Practice Test 11 — 15 Questions

Question 1

A data analyst is analyzing customer purchase amounts. The dataset contains several extreme high values due to luxury purchases. Which measure of central tendency is most robust to these outliers?

Accepted Answer

Median. The median is the middle value when data is ordered, so extreme high values (luxury purchases) shift it only slightly — it is a robust measure of central tendency. Unlike the mean, which is pulled upward by outliers because it sums all values, the median depends only on rank position. This makes it the preferred measure for skewed distributions like income or purchase amounts.

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Range

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Mean

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Mode

Question 2

In A/B testing, the null hypothesis typically states that:

Accepted Answer

There is no difference between the control and treatment groups. The null hypothesis (H0) is a statement of no effect or no difference between groups.

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The treatment group will perform better than the control group

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The sample size is sufficient for the test

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There is a significant difference between the control and treatment groups

Question 3

A data scientist is performing K-means clustering on customer data. She plots the within-cluster sum of squares (WCSS) for different values of k and observes an 'elbow' at k=4. What does this indicate?

Accepted Answer

The optimal number of clusters is 4. The elbow method suggests that adding more clusters beyond k=4 yields diminishing returns, so k=4 is a suitable number of clusters.

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The algorithm should be run with k=3 to avoid overfitting

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The data contains exactly 4 outliers

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The WCSS is minimized at k=4, indicating perfect clustering

Question 4

A data analyst is asked to compare the average sales across three different store locations. The data is normally distributed and variances are approximately equal. Which statistical test is most appropriate?

Accepted Answer

ANOVA. ANOVA (Analysis of Variance) is the correct test because it compares the means of three or more independent groups (store locations) while controlling the Type I error rate. The assumptions of normality and equal variances are satisfied, which are key prerequisites for ANOVA. A t-test would be inappropriate for more than two groups as it would require multiple pairwise comparisons, inflating the risk of false positives.

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Pearson correlation

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Chi-square test

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Two-sample t-test

Question 5

In simple linear regression, the coefficient of determination R² measures:

Accepted Answer

The proportion of variance in the dependent variable explained by the independent variable. R², the coefficient of determination, quantifies the proportion of variance in the dependent variable that is explained by the independent variable(s) in the regression model. It ranges from 0 to 1, where 1 means the model explains all variance and 0 means it explains none.

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The probability that the slope is zero

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The slope of the regression line

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The strength and direction of the linear relationship

Question 6

In time series analysis, which component represents regular patterns that repeat over fixed periods, such as daily or yearly?

Accepted Answer

Seasonality. Seasonality refers to patterns that repeat at regular intervals.

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Trend

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Cyclical

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Irregular

Question 7

Which data quality dimension ensures that data represents the real-world object or event correctly?

Accepted Answer

Accuracy. Accuracy is the data quality dimension that measures how closely data values reflect the real-world object, event, or condition they are intended to describe. If a customer's address, a transaction amount, or a sensor reading is recorded incorrectly, the data is inaccurate even if it is complete, consistent, and timely. Accuracy is therefore the dimension specifically concerned with correctness of representation.

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Completeness

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Consistency

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Timeliness

Question 8

A marketing analyst wants to predict whether a customer will churn (yes/no) based on account age and monthly charges. Which regression technique is most appropriate?

Accepted Answer

Logistic regression. Logistic regression is specifically designed for binary classification problems, such as predicting churn (yes/no). It models the probability of the outcome using a logistic function, making it appropriate for this scenario. Simple and multiple linear regression are for continuous outcomes, and K-means is for clustering, not prediction.

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Simple linear regression

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Multiple linear regression

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K-means clustering

Question 9

A dataset contains a variable 'Income' with many missing values. The analyst decides to impute missing values with the median income of the non-missing values. Which type of imputation is this?

Accepted Answer

Median imputation. Imputing missing values with the median of the non-missing values is exactly median imputation. It is a common method for handling missing data, especially when the distribution is skewed or contains outliers.

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Interpolation

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Deletion

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Forward-fill imputation

Question 10

A data analyst is comparing the means of two independent groups using a t-test. The sample sizes are small and the data is not normally distributed. Which condition is violated for a valid t-test?

Accepted Answer

Normality. A t-test assumes that the sampling distribution of the mean is approximately normal. With small sample sizes, the Central Limit Theorem does not guarantee normality, so the data itself should be approximately normal. Since the data is not normally distributed and sample sizes are small, the normality assumption is violated, making the t-test invalid.

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Equal variances

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Independence of observations

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Sample size larger than 30

Question 11

A data analyst is preparing a dataset for analysis and needs to address data quality issues. Which TWO of the following are common data cleaning tasks?

Accepted Answer

Imputing missing values. Option B (Imputing missing values) is correct because missing data is a classic data quality problem, and imputation—filling gaps using methods like mean, median, mode, or model-based estimates—is a standard data cleaning step that makes the dataset complete and usable for analysis. Option E (Deduplicating records) is correct because duplicate rows or records distort counts, aggregates, and model results, so identifying and removing or merging duplicates is a core data cleaning task. The unmarked options do not belong because hypothesis testing (A), building a regression model (C), and calculating correlation coefficients (D) are all downstream analytical or statistical modeling activities performed on already-cleaned data, not cleaning operations themselves.

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Performing hypothesis testing

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Building a regression model

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Calculating correlation coefficients

Question 12

A data analyst is performing a chi-square test for independence between two categorical variables. Which THREE of the following are necessary conditions for the test to be valid?

Accepted Answer

Sample is randomly selected. Option C is correct because a chi-square test for independence requires that the sample be randomly selected from the population, ensuring the results can be generalized and that the expected counts reflect the underlying distribution. Option D is correct because the test assumes observations are independent; each subject or case must contribute to only one cell of the contingency table, and correlated or repeated observations violate the chi-square model. Option E is correct because the chi-square approximation is valid only when the expected frequency in each cell is at least 5 (or, in larger tables, when no more than 20% of cells have expected counts below 5 and none below 1). Option A is not required because chi-square is a nonparametric test of frequencies and does not assume homogeneity of variances. Option B is not required because chi-square does not assume normally distributed data; it operates on counts of categorical outcomes.

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Variances are equal across groups

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Data is normally distributed

Question 13

An analyst is planning an A/B test to compare two website designs. Which TWO factors should be considered when calculating the required sample size?

Accepted Answer

Desired effect size. The required sample size for an A/B test depends directly on the desired effect size (B), because smaller effects are harder to detect and demand more observations to distinguish a real difference from noise. It also depends on statistical power (C), conventionally set at 0.80, since higher power (lower Type II error risk) requires a larger sample to reliably detect the effect when it truly exists. The data type of the outcome variable (A) affects the choice of statistical test, not the sample-size formula's core inputs, and the color scheme (D) is merely the design variation being tested, not a computational factor. The number of missing values (E) is a data-quality issue handled during cleaning or imputation and is not a standard parameter in sample-size calculation.

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Data type of the outcome variable

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Color scheme of the designs

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Number of missing values

Question 14

A data analyst is summarizing the central tendency of a dataset with extreme outliers. Which measure is most robust to outliers?

Accepted Answer

Median. The median is the middle value when data is ordered, so extreme outliers (whether very high or very low) do not affect its position. Unlike the mean, which incorporates every value and can be pulled strongly by outliers, the median remains stable. Therefore, for a dataset with extreme outliers, the median is the most robust measure of central tendency.

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Standard deviation

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Mean

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Range

Question 15

A retail company wants to test whether a new website layout increases the conversion rate compared to the current layout. They randomly assign visitors to either the control or treatment group. Which statistical test is most appropriate to compare the conversion rates?

Accepted Answer

Chi-square test. The outcome variable is conversion (yes/no), a categorical binary variable, and the predictor is group (control vs treatment), also categorical. The chi-square test of independence is designed to compare observed versus expected frequencies across categories, making it the correct choice for comparing two conversion rates.

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Two-sample t-test

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ANOVA

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Logistic regression