DA0-002 Data Analysis Practice Test 10 — 15 Questions

Question 1

An analyst is conducting an A/B test on a new checkout process. To calculate sample size, which THREE factors must be considered?

Accepted Answer

Desired effect size. Statistical power, significance level (alpha), and desired effect size (minimum detectable effect) are essential for sample size calculation.

Answer

Number of control groups

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Population standard deviation

Question 2

A data analyst is building a logistic regression model to predict whether a customer will churn (yes/no). Which TWO statements about logistic regression are correct?

Accepted Answer

The output is a probability between 0 and 1.. Option C is correct because logistic regression applies the sigmoid (logistic) function to a linear combination of predictors, producing an output that is a probability bounded between 0 and 1, which is exactly what is needed to model the churn probability (yes/no). Option E is correct because logistic regression assumes linearity on the logit scale: the log-odds of the outcome, ln(p/(1-p)), is modeled as a linear function of the predictor variables. Option A is incorrect because logistic regression is a classification method for binary outcomes, not a time series forecasting technique. Option B is incorrect because the dependent variable is binary/categorical (churn yes/no), not continuous. Option D is incorrect because logistic regression does not require normally distributed errors; that assumption belongs to linear regression, whereas logistic regression uses maximum likelihood estimation with a binomial error distribution.

Answer

It is used only for time series forecasting.

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The dependent variable is continuous.

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It requires normally distributed errors.

Question 3

A data analyst is examining sales data for a retail chain and notices that the mean monthly sales is $50,000 while the median is $35,000. Which of the following best describes the distribution of the sales data?

Accepted Answer

The distribution is right-skewed.. When the mean ($50,000) is greater than the median ($35,000), the distribution has a long tail on the right side pulling the mean upward, which is the definition of a right-skewed (positively skewed) distribution. In right-skewed data, a few high values inflate the mean above the median. Therefore the sales data is right-skewed.

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The distribution is bimodal.

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The distribution is left-skewed.

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The distribution is symmetrical.

Question 4

A data analyst is comparing the average test scores of students who attended a tutoring program versus those who did not. Which statistical test is most appropriate for determining if there is a significant difference between the means of these two independent groups?

Accepted Answer

Two-sample t-test. The independent samples t-test is used to compare the means of two independent groups.

Answer

Paired t-test

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Chi-square test

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ANOVA

Question 5

In a linear regression model predicting house prices, the coefficient for the number of bedrooms is $30,000 and the intercept is $50,000. If a house has 3 bedrooms, what is the predicted price?

Accepted Answer

$140,000. The linear regression equation is Price = Intercept + (Coefficient × Bedrooms) = $50,000 + ($30,000 × 3) = $50,000 + $90,000 = $140,000. This is a direct application of the slope-intercept form of a simple linear regression model with one predictor.

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$80,000

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$150,000

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$90,000

Question 6

A data analyst wants to segment customers into groups based on their purchasing behavior. The dataset includes numerical features such as annual income and purchase frequency. Which algorithm is most appropriate for this task?

Accepted Answer

K-means clustering. K-means clustering is an unsupervised learning algorithm that partitions data points into k groups based on similarity of numerical features, which is exactly what customer segmentation requires. Since the task is to discover natural groupings in unlabeled data using features like annual income and purchase frequency, K-means is the standard and most appropriate choice.

Answer

Linear regression

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Logistic regression

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Chi-square test

Question 7

A data analyst is cleaning a dataset and finds that the 'age' column has several missing values. Which of the following is a valid method for handling missing numerical data?

Accepted Answer

Impute with the mean. Imputing missing numerical values with the mean is a standard and valid statistical approach for handling missing data in a numerical column. It preserves the overall distribution's central tendency and allows the analyst to retain all other rows for analysis. This method is especially appropriate when the missingness is random and the column is roughly normally distributed, minimizing bias introduced by dropping records.

Answer

Delete the entire column

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Ignore the missing values

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Replace with zeros

Question 8

An analyst is conducting an A/B test to compare two website designs. The null hypothesis is that there is no difference in conversion rates. The p-value obtained is 0.03, and the significance threshold is 0.05. What should the analyst conclude?

Accepted Answer

Reject the null hypothesis; there is a significant difference.. In hypothesis testing, if the p-value (0.03) is less than the significance level (0.05), you reject the null hypothesis. This indicates that there is statistically significant evidence of a difference in conversion rates between the two designs. The correct conclusion is to reject the null hypothesis.

Answer

Accept the alternative hypothesis that the new design is better.

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The test is inconclusive; need a larger sample size.

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Fail to reject the null hypothesis; there is no significant difference.

Question 9

In time series decomposition, a data analyst separates a retail sales series into trend, seasonal, and residual components. After decomposition, the residual component shows no pattern and is random. Which of the following best describes the seasonal component?

Accepted Answer

Regular patterns that repeat at fixed intervals.. The seasonal component captures regular, repeating patterns at fixed intervals — for example, higher retail sales every December or every weekend. Because the residual shows no pattern (random noise), the decomposition has successfully isolated the systematic periodic structure into the seasonal component. This is the defining characteristic of seasonality in time series decomposition.

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Cyclical variations lasting more than a year.

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Irregular fluctuations that cannot be predicted.

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A long-term increase or decrease in sales.

Question 10

A data analyst needs to identify outliers in a dataset. Which of the following is a common method based on the interquartile range (IQR)?

Accepted Answer

Values below Q1 - 1.5*IQR or above Q3 + 1.5*IQR. A common rule is to consider any data point below Q1 - 1.5*IQR or above Q3 + 1.5*IQR as an outlier.

Answer

Values more than 2 standard deviations from the mean

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Values that are negative

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Values below the 5th percentile or above the 95th percentile

Question 11

A data analyst is evaluating a multiple regression model with three predictors. The R² value is 0.85. Which of the following is the best interpretation of R²?

Accepted Answer

85% of the variance in the outcome is explained by the predictors.. R² (coefficient of determination) measures the proportion of variance in the dependent variable explained by the independent variables in the model. An R² of 0.85 means 85% of the variability in the outcome is accounted for by the three predictors, with the remaining 15% attributable to factors not in the model or random noise. This is a goodness-of-fit measure, not an accuracy percentage or correlation coefficient.

Answer

85% of the predicted values are correct.

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The model has a high bias.

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The model has a strong correlation of 0.85.

Question 12

A data analyst is performing a chi-square test of independence on a contingency table of customer satisfaction (satisfied vs. dissatisfied) and product type (A, B, C). The test yields a p-value of 0.04 with α = 0.05. What is the correct conclusion?

Accepted Answer

There is a significant association between satisfaction and product type.. With a p-value of 0.04 and α = 0.05, the p-value is less than the significance level, so we reject the null hypothesis of independence. This means there is statistically significant evidence of an association between customer satisfaction and product type. The correct conclusion is that a significant association exists.

Answer

There is no evidence of an association between satisfaction and product type.

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The test is invalid because the expected counts are too low.

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Satisfaction and product type are independent.

Question 13

A data analyst is preparing a dataset for analysis and needs to ensure data quality. Which TWO of the following are dimensions of data quality?

Accepted Answer

Consistency. Consistency (C) is a core dimension of data quality because it ensures that the same data values are represented uniformly across different datasets, systems, or records, preventing contradictions that would corrupt analysis results. Accuracy (E) is also a fundamental data quality dimension, as it verifies that data correctly reflects the real-world entities or events it is meant to describe, which is essential for trustworthy analysis. In contrast, Volume (A), Velocity (B), and Variety (D) are the three defining characteristics of big data (the '3 Vs'), describing the scale, speed, and diversity of data rather than its quality. Therefore, only Consistency and Accuracy belong to the set of data quality dimensions.

Answer

Volume

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Velocity

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Variety

Question 14

A data analyst is performing K-means clustering on customer data. Which THREE of the following are steps in the K-means algorithm?

Accepted Answer

Initialize k centroids randomly.. Option C is correct because the standard K-means algorithm begins by initializing k centroids, typically at random positions in the feature space, before any assignment occurs. Option E is correct because after initialization, each data point is assigned to the nearest centroid, usually measured by Euclidean distance, forming the initial clusters. Option D is correct because the algorithm then recomputes each centroid as the mean (average) of all points assigned to it, and this assignment-and-update cycle repeats until convergence. Options A and B are not steps in K-means: eigenvalue decomposition is used in dimensionality-reduction techniques such as PCA, and calculating a correlation matrix is a preprocessing or exploratory analysis step, not part of the iterative K-means procedure.

Answer

Perform eigenvalue decomposition.

Answer

Calculate the correlation matrix.

Question 15

An analyst is preparing data for an A/B test and wants to ensure valid results. Which TWO of the following should be considered when calculating the required sample size?

Accepted Answer

Desired effect size. Sample size calculation depends on desired effect size and statistical power, among other factors like significance level.

Answer

Data dimensionality

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Skewness of data

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Number of features