CCNA Data Analysis Questions — Page 1 of 4

MCQhard

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?

A.The difference is not statistically significant at the 0.05 level.

B.The sample size is too small to detect an effect.

C.The treatment significantly outperforms control.

D.There is a 12% chance the treatment is better.

AnswerA

Correct interpretation.

Why this answer

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

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MCQhard

Refer to the exhibit. Before running the code, the original salary column had 50 missing values. The median was calculated as 52000. After imputation, which of the following statements is true?

A.The mean decreased significantly

B.The standard deviation increased

C.The median remains unchanged

D.The minimum value decreased

AnswerC

Since missing values are replaced by the median, the median of the dataset does not change.

Why this answer

Imputing missing values with the median (52000) replaces only the 50 missing entries with that value, leaving all original non-missing values unchanged. Since the median is a positional statistic, adding values equal to the current median does not shift the middle position of the sorted data, so the median remains unchanged. This is why option C is correct.

Exam trap

CompTIA often tests the misconception that imputing with the median will change the median itself, when in fact adding values equal to the current median leaves the median unchanged because it is a rank-based statistic.

How to eliminate wrong answers

Option A is wrong because imputing with the median does not significantly change the mean; the mean may shift slightly toward the median but not decrease significantly unless the missing values were extremely high. Option B is wrong because adding values exactly at the median reduces variance (since imputed values are all equal to the median), so the standard deviation decreases, not increases. Option D is wrong because the minimum value is unaffected—imputation only adds values at the median, which is far above the minimum, so the minimum remains the same.

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MCQeasy

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

A.There is no difference between the control and treatment groups

B.The treatment group will perform better than the control group

C.The sample size is sufficient for the test

D.There is a significant difference between the control and treatment groups

AnswerA

Correct definition of null hypothesis.

Why this answer

The null hypothesis (H0) is a statement of no effect or no difference between groups.

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MCQeasy

An analyst computed the mean, median, and mode of a dataset and found they are all equal. Which of the following best describes the distribution?

A.Bimodal

B.Negatively skewed

C.Positively skewed

D.Symmetric

AnswerD

Symmetric distributions have equal mean, median, and mode.

Why this answer

When mean, median, and mode are equal, the distribution is symmetric and unimodal, often resembling a normal distribution.

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MCQhard

A time series of monthly sales data exhibits a clear upward trend over several years, with consistent peaks each December. Which components are present in this series?

A.Trend and seasonality

B.Cyclical and irregular components only

C.Seasonality and cyclical components only

D.Trend and irregular components only

AnswerA

Correct identification.

Why this answer

The upward trend is a trend component, and the consistent December peaks indicate seasonality.

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MCQhard

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?

A.0.515

B.0.510

C.0.480

D.0.520

AnswerA

Calculation yields 0.515.

Why this answer

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

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MCQmedium

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?

A.Fail to reject the null hypothesis; no significant difference

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

C.Reject the null hypothesis; there is a significant difference

D.Accept the null hypothesis; the means are equal

AnswerC

Correct interpretation.

Why this answer

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.

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Multi-Selectmedium

A data analyst is preparing to run an A/B test comparing two email subject lines. Which TWO of the following should the analyst define before the test begins?

Select 2 answers

A.The exact lift in conversion rate

B.The time series decomposition

C.The p-value after the test

D.The null and alternative hypotheses

E.The sample size required for the desired power

AnswersD, E

Needed to frame the test and interpret results.

Why this answer

Before A/B testing, define null and alternative hypotheses, and determine sample size needed for desired statistical power and effect size.

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MCQmedium

A retailer wants to test if a new website layout increases the average time spent on the site. They split traffic: control group (old layout) and treatment group (new layout). Which statistical test is most appropriate to compare the average time spent between the two groups?

A.ANOVA

B.Pearson correlation

C.Chi-square test

D.Two-sample t-test

AnswerD

Compares means of two independent groups.

Why this answer

A t-test is used to compare means of two independent groups.

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MCQmedium

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

A.Reject the null hypothesis

B.Fail to reject the null hypothesis

C.Accept the null hypothesis

D.The result is practically significant

AnswerA

p < alpha indicates statistically significant result.

Why this answer

Since the p-value (0.03) is less than the significance level (0.05), the result is statistically significant. This means the observed data provides sufficient evidence to reject the null hypothesis in favor of the alternative hypothesis. The analyst should conclude that there is a statistically significant effect or difference.

Exam trap

The trap here is that candidates often confuse 'fail to reject' with 'accept' the null hypothesis, or they mistakenly think a p-value less than α means the null hypothesis is proven false with certainty, rather than just providing sufficient evidence to reject it.

How to eliminate wrong answers

Option B is wrong because failing to reject the null hypothesis occurs only when the p-value is greater than or equal to the significance level (p ≥ 0.05), not when it is smaller. Option C is wrong because hypothesis testing never 'accepts' the null hypothesis; we either reject it or fail to reject it, as acceptance implies proof of truth, which is not a valid statistical conclusion. Option D is wrong because practical significance is a separate consideration from statistical significance; a statistically significant result (p < 0.05) does not automatically imply practical importance, and the question only asks about the hypothesis test conclusion.

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MCQhard

A data analyst runs an A/B test on a new website layout. The test yields a p-value of 0.04 with the null hypothesis being no difference in conversion rates. The significance threshold is α=0.05. Which of the following is the correct conclusion?

A.The result is not significant; accept the alternative hypothesis.

B.Reject the null hypothesis; the new layout is proven to increase conversions.

C.Reject the null hypothesis; there is a statistically significant difference in conversion rates.

D.Fail to reject the null hypothesis; there is no evidence of a difference.

AnswerC

Correct interpretation: statistically significant difference exists.

Why this answer

Since p-value (0.04) < α (0.05), we reject the null hypothesis and conclude there is a statistically significant difference. However, statistical significance does not guarantee practical significance.

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Multi-Selectmedium

Which TWO of the following are common assumptions of linear regression?

Select 2 answers

A.Independence of observations

B.No multicollinearity

C.Linearity of the relationship

D.Normality of the dependent variable

E.Homoscedasticity

AnswersA, C

Correct. Independence of observations is a key assumption of linear regression; violations can lead to biased standard errors.

Why this answer

Linear regression relies on several assumptions for valid OLS estimation. Two fundamental assumptions are independence of observations (option A), meaning the residuals are independent, and linearity of the relationship (option C), meaning the model correctly specifies a linear relationship between predictors and outcome. While homoscedasticity (equal variance of residuals) is also an important assumption, it is not listed as one of the two most common assumptions in this context.

Options B and D are incorrect: no multicollinearity applies only to multiple regression, and normality of the dependent variable is not required; rather, normality of residuals is needed for hypothesis testing.

Exam trap

Candidates often confuse homoscedasticity as an assumption that must be strictly satisfied, but it is actually a requirement for efficiency of OLS estimators. However, in many definitions, independence and linearity are considered the primary assumptions.

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Multi-Selectmedium

A data analyst is performing a chi-square test of independence on a 2x2 contingency table. The p-value is 0.04. At α=0.05, which THREE of the following statements are correct?

Select 3 answers

A.There is a statistically significant association between the two variables.

B.The test indicates a strong association between variables.

C.The variables are not independent.

D.The null hypothesis is rejected.

E.The result is not statistically significant.

AnswersA, C, D

Correct: Significant association exists.

Why this answer

Since p < α, reject the null hypothesis, meaning there is an association. The test does not measure strength (Cramer's V does) and does not identify specific categories.

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MCQmedium

A dataset contains height measurements in centimeters and inches. An analyst wants to apply k-means clustering. Which data transformation should be applied before clustering?

A.Log transformation

B.Z-score standardization

C.Min-max normalization

D.No transformation needed

AnswerC

Normalization ensures equal weight from all features.

Why this answer

Min-max normalization scales features to a range, often [0,1], which is appropriate for distance-based algorithms like k-means.

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MCQhard

A data analyst is testing whether a new website layout increases conversion rate. The p-value from the test is 0.03. Using a significance level of 0.05, what is the correct conclusion?

A.Reject the null hypothesis; the new layout significantly increases conversion rate

B.The test is inconclusive because p-value is greater than 0.01

C.Accept the null hypothesis; the new layout has no effect

D.Fail to reject the null hypothesis; the new layout does not increase conversion rate

AnswerA

Correct interpretation.

Why this answer

Since p-value (0.03) < α (0.05), we reject the null hypothesis and conclude there is a statistically significant difference.

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Multi-Selectmedium

Which TWO of the following data quality dimensions are most directly affected by duplicate records?

Select 2 answers

A.Timeliness

B.Consistency

C.Uniqueness

D.Accuracy

E.Completeness

AnswersC, D

Correct: Duplicates violate uniqueness.

Why this answer

Duplicates harm accuracy (incorrect counts) and uniqueness (duplicate entries). Completeness, consistency, timeliness are less directly affected.

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MCQhard

A data scientist is analyzing a dataset with 100 variables and 5,000 records. The dataset has several missing values and a few extreme outliers. The goal is to build a regression model to predict a continuous target. Which combination of preprocessing steps is most likely to improve model performance?

A.Impute missing values with median, apply robust scaling, and then log transform skewed variables

B.Impute missing values with mean, then use PCA for dimensionality reduction

C.Drop all rows with missing values, then apply min-max scaling

D.Remove outliers using Z-score, then apply standard scaling

AnswerA

Median imputation is robust, robust scaling handles outliers, log transform handles skewness.

Why this answer

Imputing missing values with the median is robust to outliers, robust scaling handles extreme values by using median and IQR, and log transformation reduces skewness in predictors. This combination preserves data integrity and stabilizes variance, which is critical for regression models on a dataset with 100 variables and 5,000 records.

Exam trap

CompTIA often tests the misconception that mean imputation and standard scaling are universally safe, but the trap here is that outliers and skewness require robust methods like median imputation and robust scaling to avoid distorting the model.

How to eliminate wrong answers

Option B is wrong because imputing with the mean is sensitive to outliers, which can distort the distribution and negatively affect PCA, and PCA may discard important variance related to the target. Option C is wrong because dropping all rows with missing values reduces the already limited 5,000 records, potentially losing significant information and introducing bias, and min-max scaling is not robust to outliers. Option D is wrong because removing outliers using Z-score assumes a normal distribution, which may not hold with skewed variables, and standard scaling is also sensitive to outliers, leading to poor model performance.

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MCQmedium

In a dataset with variables on different scales (e.g., age in years and income in dollars), which preprocessing step is necessary before applying k-means clustering?

A.Feature selection

B.Dimensionality reduction

C.Normalization (scaling)

D.One-hot encoding

AnswerC

Normalization ensures each feature contributes equally to distance calculations.

Why this answer

K-means clustering relies on Euclidean distance to measure similarity between data points. When variables like age (in years) and income (in dollars) are on different scales, the variable with larger numeric values (income) will dominate the distance calculation, skewing the clustering results. Normalization (scaling), such as min-max scaling or z-score standardization, rescales all features to a comparable range (e.g., [0,1] or mean=0, variance=1), ensuring each feature contributes equally to the distance computation.

Exam trap

The trap here is that candidates may confuse normalization with other preprocessing steps like feature selection or dimensionality reduction, thinking that removing irrelevant features or reducing dimensions will automatically fix scale differences, but k-means specifically requires scaling to ensure equal feature influence in distance calculations.

How to eliminate wrong answers

Option A is wrong because feature selection is about choosing a subset of relevant features to reduce noise or improve model performance, but it does not address the issue of differing scales among features, which is required before k-means. Option B is wrong because dimensionality reduction (e.g., PCA) reduces the number of features, but it does not inherently scale the data; scaling is typically performed before dimensionality reduction, not as a substitute for it. Option D is wrong because one-hot encoding is used to convert categorical variables into numerical format, not to handle numerical variables on different scales; applying one-hot encoding to already numerical features would be incorrect and does not solve the scaling problem.

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MCQhard

A data analyst at a retail company is building a multiple linear regression model to forecast weekly sales. The dataset contains 50 predictor variables, including store size, promotional spend, holiday indicators, and many others. After training the model, the analyst observes an R-squared of 0.99 on the training set but only 0.55 on the holdout test set. Which action should the analyst take first to address this discrepancy?

A.Remove highly correlated predictor variables and apply regularization (e.g., Ridge or Lasso).

B.Add more predictor variables to increase the training R-squared further.

C.Use k-fold cross-validation with a different random seed to get a more reliable test set estimate.

D.Increase the number of hidden layers in the model to capture more complexity.

AnswerA

Regularization and feature selection reduce overfitting by penalizing large coefficients and removing redundant predictors.

Why this answer

The high R-squared of 0.99 on training data versus 0.55 on test data is a classic sign of overfitting, where the model has learned noise and specific patterns in the training set that do not generalize. Removing highly correlated predictors reduces multicollinearity and model complexity, while regularization (Ridge or Lasso) penalizes large coefficients, shrinking them to prevent overfitting. This is the most direct first step to improve generalization.

Exam trap

The trap here is that candidates may think a high R-squared is always good, or they may confuse overfitting with underfitting and choose to add more complexity (Option D) or more data (Option B), rather than recognizing the need to reduce model complexity and apply regularization.

How to eliminate wrong answers

Option B is wrong because adding more predictor variables would increase the training R-squared but worsen overfitting, making the test set performance even lower. Option C is wrong because k-fold cross-validation with a different random seed does not address the fundamental overfitting issue; it only provides a different estimate of test error but does not change the model's tendency to overfit. Option D is wrong because increasing the number of hidden layers (a neural network technique) is irrelevant for a multiple linear regression model and would introduce unnecessary complexity, likely exacerbating overfitting.

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Multi-Selecthard

Which THREE of the following are appropriate methods to handle outliers in a dataset?

Select 3 answers

A.Transforming the data using log transformation

B.Removing the outlier records

C.Capping the outlier values at a certain percentile

D.Binning continuous variables

E.Imputing outliers with the mean

AnswersA, B, C

Transformation can reduce the impact of outliers.

Why this answer

Log transformation compresses the scale of data, reducing the impact of extreme values and making the distribution more symmetric. This is a standard technique for handling skewed data where outliers are present, as it preserves the relative order of observations while mitigating outlier influence.

Exam trap

The trap here is that candidates may confuse data preprocessing techniques like binning or imputation with outlier handling methods, but binning is for discretization and mean imputation is not robust for outliers, while the correct methods (transformation, removal, capping) directly address outlier impact.

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Multi-Selectmedium

An analyst is preparing data for a clustering algorithm that uses Euclidean distance. Which TWO data preprocessing techniques should be applied to ensure all features contribute equally?

Select 2 answers

A.Min-max normalization

B.Z-score standardization

C.Log transformation

D.Principal component analysis

E.One-hot encoding

AnswersA, B

Scales features to [0,1] range.

Why this answer

Min-max normalization and Z-score standardization both scale features to comparable ranges, preventing features with larger scales from dominating distance calculations.

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MCQmedium

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

A.There is a weak positive relationship.

B.Advertising spend causes churn to decrease.

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

D.Increasing advertising spend is associated with decreasing churn rate.

AnswerD

Negative correlation: one goes up, other down.

Why this answer

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

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Matchingmedium

Match each data governance role to its responsibility.

Drag a concept onto its matching description — or click a concept then click the description.

Concepts

Matches

Ensures data quality and adherence to policies

Manages technical environment and data access

Has accountability for specific data assets

Sets strategic direction for data management

Designs data structures and integration processes

Why these pairings

Data Stewards focus on quality and metadata, Data Owners have accountability and access decisions, Data Custodians handle technical security, and Data Governors manage the governance program. Common confusions include mixing Steward with Custodian or Owner.

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MCQmedium

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?

A.Linear regression

B.K-means clustering

C.Logistic regression

D.Chi-square test

AnswerB

Correct: K-means is unsupervised clustering for segmentation.

Why this answer

K-means clustering is a common algorithm for customer segmentation based on numerical features.

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MCQhard

Refer to the exhibit. Which data quality dimension is being violated?

A.Uniqueness

B.Consistency

C.Timeliness

D.Completeness

AnswerB

Consistency ensures data formats and values are uniform; mixed data types violate this.

Why this answer

The exhibit shows the same customer ID (C001) associated with two different customer names ('John Smith' and 'Jon Smith'), which violates the consistency dimension. Consistency requires that data values be free from contradiction and adhere to the same representation rules across the dataset. Here, the conflicting names for the same identifier break referential integrity and data uniformity.

Exam trap

The trap here is that candidates confuse consistency with uniqueness, assuming any conflict between rows must be a duplicate record issue, when in fact consistency violations involve contradictory values for the same identifier across multiple records.

How to eliminate wrong answers

Option A is wrong because uniqueness is about ensuring no duplicate records exist for the same entity, but here the issue is conflicting attribute values for the same ID, not duplicate rows. Option C is wrong because timeliness concerns whether data is up-to-date and available when needed, which is not indicated by the name mismatch. Option D is wrong because completeness checks for missing values, but both records have all fields populated; the problem is contradictory data, not absent data.

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MCQmedium

A data analyst needs to determine whether the mean sales of two different regions are significantly different. The samples are independent and the data is normally distributed. Which statistical test should be used?

A.Chi-square test for independence

B.ANOVA

C.Independent samples t-test

D.Paired t-test

AnswerC

This test compares means of two independent groups with normal distribution.

Why this answer

The independent samples t-test is the correct choice because the scenario involves comparing the means of two independent groups (two different regions) with normally distributed data. This test specifically assesses whether the difference between the two sample means is statistically significant, assuming equal or unequal variances as determined by Levene's test.

Exam trap

CompTIA often tests the distinction between independent and paired t-tests, trapping candidates who overlook the 'independent samples' condition and mistakenly choose the paired t-test for any two-group comparison.

How to eliminate wrong answers

Option A is wrong because the Chi-square test for independence is used for categorical data to assess associations between two variables, not for comparing means of continuous data. Option B is wrong because ANOVA is used to compare means among three or more groups, not exactly two independent groups. Option D is wrong because the paired t-test is used for dependent samples (e.g., before-and-after measurements on the same subjects), not for independent samples from different regions.

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Multi-Selecthard

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)

Select 3 answers

A.Seasonality of the data

B.Statistical power (e.g., 0.80)

C.Minimum detectable effect size

D.Number of clusters in k-means

E.Significance level (α)

AnswersB, C, E

Higher power requires larger sample.

Why this answer

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

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MCQhard

A logistic regression model is used to predict the probability of customer churn. The model's coefficient for the feature 'customer support calls' is 0.8 with a p-value of 0.001. Which interpretation is correct?

A.For each additional support call, the log-odds of churn increase by 0.8, and this effect is statistically significant.

B.The odds of churn are multiplied by 0.8 for each additional call.

C.Support calls have no significant effect on churn.

D.For each additional support call, the probability of churn increases by 80%.

AnswerA

Correct interpretation of logistic regression coefficient.

Why this answer

In logistic regression, a positive coefficient indicates that as the predictor increases, the log-odds of the outcome increase. The p-value being less than 0.05 indicates the effect is statistically significant.

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MCQhard

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?

A.Cyclical variations lasting more than a year.

B.Irregular fluctuations that cannot be predicted.

C.Regular patterns that repeat at fixed intervals.

D.A long-term increase or decrease in sales.

AnswerC

Correct: seasonality is regular periodic patterns.

Why this answer

Seasonality refers to regular, periodic patterns that repeat at fixed intervals (e.g., monthly, quarterly).

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MCQmedium

A data analyst is preparing a dataset for a predictive model. The dataset contains a feature 'age' with values ranging from 18 to 80, and a feature 'income' ranging from 20,000 to 200,000. To ensure both features contribute equally to distance-based algorithms, which transformation should the analyst apply?

A.Min-max normalization

B.Log transformation

C.Standardization (z-score)

D.Box-Cox transformation

AnswerC

Standardization ensures each feature has mean 0 and std 1, providing equal weight in distance calculations.

Why this answer

Standardization (z-score) transforms features to have a mean of 0 and a standard deviation of 1, which ensures that both 'age' (18–80) and 'income' (20,000–200,000) contribute equally to distance-based algorithms like k-NN or k-means. Unlike min-max normalization, standardization is not affected by outliers and preserves the relative distances between data points, making it the preferred choice when the data does not follow a uniform distribution.

Exam trap

The trap here is that candidates often confuse min-max normalization with standardization, assuming that scaling to a fixed range is sufficient for distance-based algorithms, without considering the impact of outliers or the need for zero mean and unit variance.

How to eliminate wrong answers

Option A is wrong because min-max normalization scales features to a fixed range (e.g., [0,1]), but it is highly sensitive to outliers and does not guarantee equal contribution if the data contains extreme values; it also does not center the data around zero, which can distort distance calculations. Option B is wrong because log transformation is used to reduce skewness in positively skewed data, not to standardize features with different scales; it changes the shape of the distribution and would not make 'age' and 'income' comparable for distance-based algorithms. Option D is wrong because Box-Cox transformation is designed to make data more normally distributed and requires all values to be positive, but it does not standardize features to a common scale; applying it to 'age' and 'income' would not ensure equal contribution to distance metrics.

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MCQeasy

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

A.Mean

B.Range

C.Median

D.Mode

AnswerA

The mean is the average and is pulled toward extreme values.

Why this answer

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

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MCQmedium

A retail company wants to identify customer segments based on purchase history and demographics. Which technique is most appropriate for this task?

A.Linear regression

B.K-means clustering

C.Chi-square test

D.Logistic regression

AnswerB

K-means groups similar customers into clusters.

Why this answer

K-means clustering is an unsupervised learning technique designed to segment data into groups based on similarity.

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MCQmedium

A data scientist is using K-means clustering with k=3. After the first iteration, the centroids are recalculated. Which step occurs next in the algorithm?

A.Calculate the sum of squared errors

B.Stop the algorithm because k is fixed

C.Compute the elbow curve

D.Assign each point to the nearest centroid

AnswerD

After centroid update, points are reassigned based on distance.

Why this answer

In K-means, after recalculating centroids, each point is reassigned to the nearest centroid, then centroids are updated again, iterating until convergence.

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MCQmedium

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

A.Transform the predictor

B.Keep the predictor regardless

C.Remove the predictor from the model

D.Increase the sample size

AnswerC

The variable is not significant.

Why this answer

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

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MCQeasy

A data analyst needs to join two tables in a SQL database: Orders and Customers. The analyst wants to include all orders, even if there is no matching customer record. Which type of join should be used?

A.RIGHT JOIN

B.FULL OUTER JOIN

C.LEFT JOIN

D.INNER JOIN

AnswerC

LEFT JOIN returns all orders, including those without matching customers.

Why this answer

A LEFT JOIN returns all rows from the left table (Orders) and the matching rows from the right table (Customers). If there is no match, NULL values are returned for the right table's columns. This satisfies the requirement to include all orders, even those without a matching customer record.

Exam trap

The trap here is that candidates often confuse LEFT JOIN with RIGHT JOIN, mistakenly thinking they need to 'keep all customers' instead of 'keep all orders,' or they overcomplicate the requirement by choosing FULL OUTER JOIN when only one side needs to be preserved.

How to eliminate wrong answers

Option A (RIGHT JOIN) is wrong because it returns all rows from the right table (Customers) and matching rows from the left table (Orders), which would include all customers, not all orders. Option B (FULL OUTER JOIN) is wrong because it returns all rows from both tables, including unmatched rows from both sides, which is unnecessary when the requirement is specifically to keep all orders. Option D (INNER JOIN) is wrong because it returns only rows where there is a match in both tables, which would exclude orders without a matching customer record.

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MCQmedium

A data analyst is examining the distribution of customer ages in a dataset. The ages are: 22, 25, 29, 30, 31, 34, 35, 37, 40, 42, 45, 50, 55, 60, 65. Which measure of central tendency would be least affected by an outlier if a value of 120 is incorrectly recorded as age 120?

A.Mode

B.Median

C.Mean

D.Range

AnswerB

The median is not affected by outliers.

Why this answer

The median is resistant to outliers because it is the middle value when data are sorted. The mean is sensitive to extreme values, and the mode may not change but is not a robust measure of central tendency. The range is a measure of spread, not central tendency.

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MCQhard

In a multiple regression model with three predictors, the coefficient for one predictor is 5.2 with a p-value of 0.001. Which of the following is the best interpretation?

A.The predictor explains 5.2% of the variance in the dependent variable.

B.A one-unit increase in the predictor decreases the dependent variable by 5.2 units, on average.

C.The model is not a good fit because one predictor is significant.

D.The predictor has a statistically significant effect on the dependent variable, controlling for other variables.

AnswerD

p < 0.05 indicates significance, and 'holding constant' is key.

Why this answer

The coefficient indicates the change in the dependent variable for a one-unit increase in the predictor, holding other predictors constant.

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MCQmedium

A company wants to determine if there is a significant difference in the average sales revenue between two different store layouts. They collect sales data from 30 stores with Layout A and 30 stores with Layout B. Which statistical test is most appropriate for comparing the means of these two independent groups?

A.ANOVA

B.Chi-square test

C.Paired t-test

D.Two-sample t-test

AnswerD

Correct for comparing means of two independent groups.

Why this answer

The two-sample t-test (independent t-test) compares the means of two independent groups. A paired t-test would be for dependent samples, ANOVA for three or more groups, and chi-square for categorical variables.

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MCQhard

A data scientist builds a logistic regression model to predict customer churn (yes/no). The model outputs a probability of 0.75 for a particular customer. Which of the following best describes this output?

A.The customer will definitely churn.

B.There is a 75% chance the customer will churn.

C.The odds of churning are 0.75 to 1.

D.The model is 75% accurate.

AnswerB

Probability interpretation.

Why this answer

Logistic regression outputs a probability between 0 and 1, interpreted as the likelihood of the positive class (churn = yes).

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MCQmedium

A data analyst at a marketing firm is tasked with segmenting customers based on their purchasing behavior. The dataset contains 10,000 customers with features such as annual spend, frequency of purchases, recency of last purchase, and average order value. The analyst decides to use k-means clustering. After standardizing the features, the analyst runs k-means with k=3, k=4, and k=5, and computes the silhouette score for each: k=3: 0.45, k=4: 0.52, k=5: 0.48. The analyst also plots the elbow curve and observes that the within-cluster sum of squares (WCSS) decreases sharply from k=2 to k=4, then levels off. Based on these results, what is the most appropriate number of clusters?

A.k=4

B.k=2

C.k=3

D.k=5

AnswerA

Highest silhouette score and elbow point.

Why this answer

The silhouette score is highest at k=4 (0.52), indicating that clusters are well-separated and cohesive. The elbow curve shows WCSS decreasing sharply up to k=4 and then leveling off, suggesting that k=4 captures the optimal trade-off between model complexity and variance explained. Together, these metrics point to k=4 as the most appropriate number of clusters.

Exam trap

The trap here is that candidates might rely solely on the elbow curve and pick k=3 or k=5, ignoring the silhouette score which directly measures cluster quality and clearly favors k=4.

How to eliminate wrong answers

Option B (k=2) is wrong because the elbow curve shows a sharp decrease in WCSS from k=2 to k=4, meaning k=2 would underfit the data and miss meaningful segmentation. Option C (k=3) is wrong because its silhouette score (0.45) is lower than k=4 (0.52), indicating poorer cluster separation and cohesion. Option D (k=5) is wrong because its silhouette score (0.48) is lower than k=4, and the elbow curve shows WCSS leveling off after k=4, so adding a fifth cluster introduces unnecessary complexity without significant improvement.

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Multi-Selectmedium

A data analyst is evaluating data quality for a customer database. Which TWO dimensions of data quality are most directly affected by duplicate customer records?

Select 2 answers

A.Consistency

B.Accuracy

C.Timeliness

D.Completeness

E.Uniqueness

AnswersB, E

Duplicates can cause inaccurate counts and misrepresent entity.

Why this answer

Duplicates reduce accuracy (records may be incorrect) and uniqueness (each entity should appear once).

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MCQeasy

A dataset contains the ages of 100 customers. The analyst wants to transform the ages to a 0-1 range for use in a distance-based algorithm. Which technique should be used?

A.Square root transformation

B.Log transformation

C.Z-score normalization

D.Min-max normalization

AnswerD

Min-max scales to a range, e.g., 0-1.

Why this answer

Min-max normalization scales features to a fixed range, typically 0-1.

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MCQeasy

A data analyst runs the Python code shown. What is the result of executing this code?

A.It reads the data, adds a calculated column, and shows the first 5 rows

B.It throws an error because 'total' column already exists

C.It reads the data and displays all rows

D.It reads the data and displays summary statistics

AnswerA

The code does exactly that.

Why this answer

The code reads a CSV file into a pandas DataFrame, then creates a new column 'total' by summing columns 'col1' and 'col2'. Finally, `head()` returns the first 5 rows. Option A correctly describes this sequence of operations.

Exam trap

The trap here is that candidates may think `head()` shows all rows or that adding a column with an existing name throws an error, but pandas silently overwrites the column.

How to eliminate wrong answers

Option B is wrong because pandas allows adding a new column with the same name as an existing column only if the assignment overwrites it; here, if 'total' already existed, it would be overwritten without error. Option C is wrong because `head()` without an argument defaults to 5 rows, not all rows. Option D is wrong because `head()` displays rows, not summary statistics (which would require `.describe()`).

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MCQeasy

A data analyst is cleaning a dataset and finds missing values in a categorical variable representing customer region. Which imputation method is most appropriate?

A.Drop rows with missing values

B.Mode imputation

C.Mean imputation

D.Median imputation

AnswerB

Mode is appropriate for categorical variables.

Why this answer

Mode imputation is the most appropriate method for a categorical variable because it replaces missing values with the most frequently occurring category, preserving the distribution of the data. Unlike mean or median imputation, which are designed for numerical data, mode imputation maintains the categorical nature of the variable and avoids introducing invalid values. This approach is simple and effective when missing data is random and the category is well-represented.

Exam trap

The trap here is that candidates often confuse imputation methods across data types, incorrectly applying mean or median imputation to categorical variables because they focus on central tendency without considering data type appropriateness.

How to eliminate wrong answers

Option A is wrong because dropping rows with missing values can lead to significant data loss and potential bias, especially if the missingness is not completely random, reducing the dataset's representativeness. Option C is wrong because mean imputation is only appropriate for numerical data, not categorical variables, as calculating the mean of categories is meaningless and would produce non-categorical values. Option D is wrong because median imputation is also designed for numerical data and cannot be applied to categorical variables, as the median requires ordered numerical values to compute.

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Multi-Selecthard

An analyst is conducting an A/B test on a new website layout. Which TWO of the following must be defined before the test begins?

Select 2 answers

A.The final conversion rates for each group

B.The actual p-value from the test

C.The confidence interval for the lift

D.The significance threshold (alpha)

E.The sample size required for adequate statistical power

AnswersD, E

Must be set beforehand.

Why this answer

Sample size and significance level must be set a priori to ensure proper test design.

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MCQmedium

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?

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

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

C.There is insufficient evidence to conclude a difference between the designs.

D.There is a statistically significant difference between the designs.

AnswerC

We fail to reject the null hypothesis due to high p-value.

Why this answer

Since p > α, we fail to reject the null hypothesis, meaning no statistically significant difference was found.

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MCQeasy

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

A.IQR

B.Range

C.Standard deviation

D.Variance

AnswerA

IQR is robust and covers middle 50%.

Why this answer

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

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MCQmedium

An analyst uses K-means clustering on customer purchase data. After plotting the within-cluster sum of squares for different values of k, they observe an elbow at k=4. What is the most appropriate number of clusters?

A.4

B.6

C.5

D.3

AnswerA

The elbow indicates the optimal k.

Why this answer

The elbow method suggests choosing k where the WSS starts to diminish slowly; that point is the elbow.

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MCQmedium

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?

A.Mean imputation

B.Forward-fill

C.Delete all rows with missing data

D.Mode imputation

AnswerA

Mean imputation is appropriate for normal distribution.

Why this answer

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

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MCQeasy

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?

A.Paired t-test

B.Chi-square test

C.Two-sample t-test

D.ANOVA

AnswerC

Correct: independent samples t-test compares means of two groups.

Why this answer

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

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Matchingmedium

Match each data sampling method to its description.

Drag a concept onto its matching description — or click a concept then click the description.

Concepts

Matches

Each member has equal chance of selection

Population divided into subgroups; random sample from each

Randomly select entire groups (clusters)

Select every k-th element from a list

Sample based on ease of access

Why these pairings

In matching, ensure you distinguish between methods: simple random gives equal chance; stratified divides by characteristic; cluster divides into groups and picks whole groups; systematic uses a fixed interval.

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MCQhard

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?

A.$80,000

B.$150,000

C.$90,000

D.$140,000

AnswerD

Correct: 30000*3 + 50000 = 140000.

Why this answer

Using y = mx + b, predicted price = 30000 * 3 + 50000 = $140,000.

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MCQmedium

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

A.Seasonality

B.Trend

C.Cyclical

D.Irregular

AnswerA

Correct definition.

Why this answer

Seasonality refers to patterns that repeat at regular intervals.

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MCQeasy

After a marketing campaign, sales increased by 15%. The analyst wants to understand which customer segment contributed most to the increase. Which type of analysis is this?

A.Predictive analysis

B.Diagnostic analysis

C.Prescriptive analysis

D.Descriptive analysis

AnswerB

Diagnostic analysis investigates the cause of the outcome—here, which segment drove the increase.

Why this answer

Diagnostic analysis is used to understand the root cause of an event or change. In this scenario, the analyst already knows sales increased by 15% and wants to determine which customer segment drove that increase, which is a classic diagnostic question. This type of analysis goes beyond describing what happened to explain why it happened.

Exam trap

The trap here is confusing diagnostic analysis with descriptive analysis, as both deal with past data, but descriptive only summarizes what happened while diagnostic explains why it happened.

How to eliminate wrong answers

Option A is wrong because predictive analysis uses historical data to forecast future outcomes, not to explain past changes. Option C is wrong because prescriptive analysis recommends actions or decisions to achieve a desired outcome, not to diagnose the cause of a past event. Option D is wrong because descriptive analysis summarizes what happened (e.g., 'sales increased by 15%') but does not investigate which segment contributed most to the increase.

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MCQhard

A data scientist is building a K-means clustering model for customer segmentation. After plotting the within-cluster sum of squares (WCSS) against the number of clusters (k), she observes that the WCSS decreases sharply until k=5 and then levels off. Which value of k should she choose based on the elbow method?

A.k=5

B.k=6

C.k=4

D.k=3

AnswerA

Correct elbow point.

Why this answer

The elbow method suggests selecting the number of clusters at the point where the WCSS starts to diminish less rapidly, forming an 'elbow'. Here, the elbow is at k=5, where adding more clusters yields diminishing returns.

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Multi-Selectmedium

Which TWO of the following are commonly used techniques for handling missing data in a dataset? (Select TWO).

Select 2 answers

A.Mean imputation

B.Mode imputation

C.Dropping columns with missing data

D.Dropping rows with missing data

E.Regression imputation

AnswersA, E

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

Why this answer

Mean imputation is a commonly used technique for handling missing numerical data where the missing value is replaced with the mean of the observed values for that feature. It preserves the sample size and is simple to implement, though it can reduce variance and distort relationships if data is not missing completely at random.

Exam trap

CompTIA often tests the distinction between common imputation methods (mean, median, mode, regression) and data removal techniques, trapping candidates who confuse 'dropping rows' as a primary technique when imputation is more widely recommended for preserving data integrity.

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Multi-Selectmedium

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?

Select 2 answers

A.Square root transformation

B.Log transformation

C.One-hot encoding

D.Z-score standardization

E.Min-max normalization

AnswersA, B

Correct. Square root transformation can reduce right skewness and make the distribution more normal.

Why this answer

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.

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MCQmedium

A dataset contains employee salaries ranging from $30,000 to $200,000. An analyst wants to scale the salaries to a range of 0 to 1 for use in a distance-based clustering algorithm. Which method should they use?

A.Log transformation

B.Robust scaling

C.Min-max normalization

D.Z-score standardization

AnswerC

Scales to 0-1 using (x - min)/(max - min).

Why this answer

Min-max normalization scales data to a 0-1 range.

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MCQeasy

A data analyst needs to create a visual that shows the distribution of customer ages across different regions. Which chart type is most appropriate?

A.Line chart

B.Stacked bar chart

C.Scatter plot

D.Pie chart

AnswerB

A stacked bar chart can display the distribution of age groups within each region, making comparisons easy.

Why this answer

A stacked bar chart is most appropriate because it allows the analyst to compare the distribution of customer ages (typically grouped into bins) across multiple regions simultaneously. Each bar represents a region, and the segments within the bar show the proportion or count of each age group, making it easy to see both the overall distribution and regional differences.

Exam trap

The trap here is that candidates often choose a pie chart because they think of 'distribution' as a single whole, forgetting that the question requires comparison across multiple regions, which a pie chart cannot handle.

How to eliminate wrong answers

Option A is wrong because a line chart is designed to show trends over a continuous variable (e.g., time), not the distribution of categorical age groups across regions. Option C is wrong because a scatter plot is used to show the relationship between two continuous variables, not the distribution of a single categorical variable across regions. Option D is wrong because a pie chart can only show the composition of a whole for a single category (e.g., age distribution for one region), but it cannot effectively compare distributions across multiple regions.

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MCQeasy

In an A/B test, the null hypothesis states that there is no difference between the conversion rates of the control and treatment groups. After collecting data, the p-value is 0.03. Using a significance level α = 0.05, what should the analyst conclude?

A.Reject the null hypothesis; there is a significant difference

B.Accept the alternative hypothesis; the treatment is better

C.The test is inconclusive

D.Fail to reject the null hypothesis; no significant difference

AnswerA

Correct conclusion.

Why this answer

Since the p-value (0.03) is less than α (0.05), the null hypothesis is rejected, indicating a statistically significant difference between the groups.

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MCQeasy

A data analyst is designing a data model for a sales data warehouse. The model should optimize query performance for aggregations by minimizing joins and duplicating data where necessary. Which schema design should the analyst use?

A.Entity-relationship model

B.Snowflake schema

C.3NF normalized model

D.Star schema

AnswerD

Star schema denormalizes dimensions, minimizing joins and optimizing aggregate queries.

Why this answer

Star schema denormalizes dimensions into a single table, reducing joins and improving query speed for aggregates. Snowflake schema normalizes dimensions increasing joins. Entity-relationship and 3NF are optimized for transactional systems, not analytical queries.

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MCQhard

A data analyst uses linear regression to model the relationship between advertising spend and sales. The residual plot shows a clear U-shaped pattern. What assumption is violated?

A.Independence of residuals

B.Homoscedasticity

C.Normality of residuals

D.Linearity

AnswerD

A U-shaped pattern means the relationship is not linear; the model is missing a nonlinear term.

Why this answer

The U-shaped pattern in the residual plot indicates that the relationship between advertising spend and sales is not linear; the model fails to capture the curvature in the data. Linear regression assumes a straight-line relationship between predictors and the response, so a systematic pattern like a U-shape directly violates the linearity assumption. This means the model is misspecified and requires a transformation or a nonlinear modeling approach.

Exam trap

CompTIA often tests the distinction between residual pattern shapes and their corresponding assumptions, so the trap here is that candidates confuse a curved pattern (nonlinearity) with heteroscedasticity or non-normality, leading them to pick B or C instead of D.

How to eliminate wrong answers

Option A is wrong because independence of residuals refers to errors being uncorrelated with each other, often violated in time-series data, but a U-shaped pattern does not imply autocorrelation. Option B is wrong because homoscedasticity means constant variance of residuals across fitted values, which would appear as a funnel or cone shape, not a U-shaped curve. Option C is wrong because normality of residuals concerns the distribution of errors (checked via Q-Q plot or histogram), not the pattern of residuals versus fitted values; a U-shaped pattern does not directly indicate non-normality.

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MCQhard

A healthcare analytics team is building a predictive model to identify patients at high risk of readmission within 30 days of discharge. The dataset includes 50,000 patient records with 200 features, including demographics, vital signs, lab results, and historical admissions. The target variable is binary (readmitted or not). The team uses a logistic regression model and achieves an AUC of 0.72 on the test set. However, the model's calibration is poor: for patients predicted to have a 70% risk, the actual readmission rate is only 40%. The team wants to improve calibration without significantly reducing discrimination (AUC). The data scientist suggests applying Platt scaling. However, the team lead is concerned that Platt scaling may reduce the model's ability to rank patients correctly. Which of the following is the best course of action?

A.Remove poorly calibrated predictions by discarding all patients with predicted risk between 0.3 and 0.7.

B.Ignore calibration because AUC is the only metric that matters for readmission risk models.

C.Apply Platt scaling on a held-out validation set to recalibrate the predicted probabilities without refitting the original model.

D.Switch to a random forest model, which inherently produces better-calibrated probabilities.

AnswerC

Platt scaling is designed to improve calibration while maintaining AUC.

Why this answer

Platt scaling is a post-processing technique that fits a logistic regression model on the predicted probabilities from the original model using a held-out validation set. This recalibrates the probabilities without altering the ranking of patients (the AUC remains unchanged), directly addressing the poor calibration while preserving discrimination. Option C correctly describes this procedure.

Exam trap

The trap here is that candidates may think Platt scaling changes the model's ranking (AUC), but in reality it applies a monotonic transformation that preserves rank order, so discrimination is unaffected.

How to eliminate wrong answers

Option A is wrong because discarding patients with predicted risk between 0.3 and 0.7 removes a large portion of the data and does not fix the underlying miscalibration; it merely hides the problem and reduces the model's utility. Option B is wrong because AUC measures only rank ordering, not probability accuracy; for clinical risk models, well-calibrated probabilities are critical for decision-making (e.g., resource allocation). Option D is wrong because random forest models are known to produce poorly calibrated probabilities due to their averaging of decision tree outputs, often requiring their own calibration (e.g., isotonic regression) and do not inherently guarantee better calibration than logistic regression.

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MCQmedium

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?

A.Reject the null hypothesis because the p-value is less than the significance level.

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

C.The test is inconclusive, need a larger sample size.

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

AnswerA

A p-value less than α leads to rejection of the null hypothesis.

Why this answer

Since 0.03 < 0.05, we reject the null hypothesis, indicating statistically significant evidence against it.

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MCQmedium

A healthcare analytics team is building a classification model to predict patient readmission within 30 days. The dataset contains 10,000 records with 30 features, including demographics, vital signs, lab results, and medication history. The target variable is imbalanced: 85% no readmission, 15% readmission. The team used logistic regression with default settings and achieved an accuracy of 85%, but the model predicted 'no readmission' for all patients. The lead analyst suspects the model is not learning due to class imbalance. The team has time to implement one corrective action before the next model review. Which action should the team take?

A.Remove features with low variance to reduce noise

B.Apply SMOTE to oversample the readmission class

C.Use accuracy as the evaluation metric to monitor improvement

D.Switch to a random forest model with default settings

AnswerB

SMOTE generates synthetic samples, balancing the classes and allowing the model to learn from the minority class.

Why this answer

SMOTE (Synthetic Minority Oversampling Technique) directly addresses the class imbalance by generating synthetic samples for the minority class (readmission). This forces the logistic regression model to learn decision boundaries that separate the two classes, rather than defaulting to the majority class prediction. With 85% majority and 15% minority, accuracy alone is misleading, and SMOTE is a proven technique to improve recall for the minority class.

Exam trap

The trap here is that candidates often choose accuracy as a metric (Option C) because it seems intuitive, but in imbalanced datasets, accuracy is misleading and does not reflect model performance for the minority class.

How to eliminate wrong answers

Option A is wrong because removing low-variance features does not address class imbalance; it only reduces noise or redundant features, but the model will still predict the majority class if the imbalance is not handled. Option C is wrong because using accuracy as the evaluation metric is exactly the problem—it will remain high (85%) even if the model predicts all 'no readmission', so it does not monitor improvement for the minority class. Option D is wrong because switching to a random forest model with default settings does not inherently solve class imbalance; random forest can also be biased toward the majority class without techniques like class weighting or resampling.

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Multi-Selectmedium

Which TWO of the following are components of time series data?

Select 2 answers

A.Mean

B.Variance

C.Trend

D.Seasonality

E.Median

AnswersC, D

Correct: Trend is a long-term direction.

Why this answer

Trend and seasonality are classic components of time series. Mean, median, and variance are statistical measures but not components of time series decomposition.

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MCQmedium

A data analyst wants to understand the relationship between advertising spend and sales revenue. The analyst calculates a Pearson correlation coefficient of 0.85. Which of the following is the best interpretation?

A.There is a strong positive linear relationship between advertising spend and sales.

B.85% of the variation in sales is explained by advertising spend.

C.Increasing advertising spend by $1 will increase sales by $0.85.

D.There is a strong negative linear relationship between advertising spend and sales.

AnswerA

r=0.85 indicates strong positive linear relationship.

Why this answer

Pearson r ranges from -1 to +1; 0.85 indicates a strong positive linear relationship, but correlation does not imply causation.

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MCQhard

A company is analyzing customer feedback sentiment. The dataset is highly imbalanced with 95% positive and 5% negative comments. Which technique should the analyst use to address class imbalance before modeling?

A.Use accuracy as the evaluation metric

B.Undersample the majority class

C.Oversample the majority class

D.Use SMOTE

AnswerD

SMOTE generates synthetic minority samples to balance classes.

Why this answer

SMOTE (Synthetic Minority Oversampling Technique) is the correct choice because it generates synthetic samples for the minority class (negative comments) by interpolating between existing minority instances, rather than simply duplicating them. This addresses the 95:5 imbalance without the information loss of undersampling or the overfitting risk of naive oversampling.

Exam trap

The trap here is that candidates often confuse oversampling the minority class with oversampling the majority class, or they incorrectly assume that simply using a different evaluation metric (like accuracy) can fix the imbalance problem without modifying the dataset.

How to eliminate wrong answers

Option A is wrong because accuracy is a misleading metric for imbalanced datasets; a model predicting all comments as positive would achieve 95% accuracy but fail to identify any negative comments. Option B is wrong because undersampling the majority class discards a large amount of potentially useful data, which can lead to loss of important patterns and reduced model performance. Option C is wrong because oversampling the majority class would exacerbate the imbalance, making the model even more biased toward the majority class.

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Multi-Selecteasy

Which TWO of the following are measures of central tendency?

Select 2 answers

A.Median

B.Range

C.Variance

D.Standard deviation

E.Mean

AnswersA, E

Correct: Median is a measure of central tendency.

Why this answer

Mean, median, and mode are measures of central tendency. Range and standard deviation measure dispersion.

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Multi-Selecteasy

Which THREE of the following are examples of descriptive statistics? (Select THREE.)

Select 3 answers

A.Correlation coefficient

B.Mean

C.P-value

D.Regression coefficient

E.Standard deviation

AnswersA, B, E

Correlation coefficient describes the strength of a linear relationship, a descriptive statistic.

Why this answer

The correlation coefficient (A) is a descriptive statistic because it quantifies the strength and direction of a linear relationship between two variables using a single number (ranging from -1 to +1) without making inferences about a larger population. It simply describes the observed data's association, which is the core function of descriptive statistics.

Exam trap

CompTIA often tests the distinction between descriptive and inferential statistics by including p-values and regression coefficients as distractors, exploiting the common misconception that any numerical summary of data is descriptive.

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MCQeasy

A data analyst wants to use a Z-score to standardize a dataset. The variable has a mean of 50 and a standard deviation of 10. What is the Z-score for a raw value of 70?

A.0.5

B.20

C.-2

D.2

AnswerD

Correct Z-score.

Why this answer

Z = (X - mean) / std = (70 - 50) / 10 = 2.

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MCQmedium

A data analyst notices that a dataset of customer ages has several missing values. Which method for handling missing data is most appropriate if the data is missing completely at random and the analyst wants to preserve sample size?

A.Forward-fill using the previous value

B.Impute with the mean age

C.Replace missing values with zero

D.Delete all rows with missing data

AnswerB

Mean imputation is simple and preserves sample size.

Why this answer

Mean imputation replaces missing values with the mean, preserving sample size, but can bias estimates. However, for MCAR, it is a common simple approach.

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Multi-Selecteasy

A data analyst is cleaning a dataset with missing values. Which TWO of the following are acceptable methods for handling missing numerical data?

Select 2 answers

A.Min-max normalisation

B.Forward-fill

C.Mode imputation

D.Mean imputation

E.Deletion of rows with missing values

AnswersD, E

Correct: Replacing missing with mean is acceptable.

Why this answer

Mean imputation and deletion (listwise) are common methods. Mode imputation is for categorical, and forward-fill is for time series; min-max is normalisation.

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Multi-Selecthard

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?

Select 2 answers

A.It is used only for time series forecasting.

B.The dependent variable is continuous.

C.The output is a probability between 0 and 1.

D.It requires normally distributed errors.

E.It assumes a linear relationship between predictors and the log-odds of the outcome.

AnswersC, E

Logistic regression predicts probabilities.

Why this answer

Logistic regression outputs probabilities between 0 and 1, and can use a sigmoid function. It is a classification algorithm, and coefficients represent log-odds changes.

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Multi-Selecthard

Which TWO of the following are valid techniques for validating the performance of a predictive model?

Select 2 answers

A.Bootstrapping

B.Feature scaling

C.Train-test split

D.K-fold cross-validation

E.Increasing training data

AnswersC, D

Splitting data into training and testing sets is a basic validation approach.

Why this answer

The train-test split (Option C) is a fundamental technique for validating predictive model performance by partitioning the dataset into separate training and testing subsets, ensuring the model is evaluated on unseen data to gauge generalization. This method directly addresses overfitting and provides an unbiased estimate of model accuracy, making it a standard practice in supervised learning workflows.

Exam trap

CompTIA often tests the distinction between data preprocessing techniques (like feature scaling) and actual model validation methods, leading candidates to mistakenly select feature scaling as a validation technique because it is a common step in the modeling pipeline.

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