DA0-002 Data Analysis Practice Test 15 — 15 Questions

Question 1

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

Accepted Answer

0.515. Min-max normalization scales values using the formula (x - min) / (max - min). With min=10 and max=1000, the normalized value of 520 is (520 - 10) / (1000 - 10) = 510 / 990 = 0.51515..., which rounds to 0.515. Option A correctly applies this formula.

Answer

0.510

Answer

0.480

Answer

0.520

Question 2

Which data quality dimension ensures that data represents the real-world scenario correctly and without errors?

Accepted Answer

Accuracy. Accuracy is the data quality dimension that measures whether data correctly reflects the real-world object or event it describes, free from errors. If a customer's address is stored incorrectly or a transaction amount is wrong, the data is inaccurate even if it is complete and consistent. Completeness, consistency, and timeliness address different aspects and do not capture correctness of representation.

Answer

Completeness

Answer

Consistency

Answer

Timeliness

Question 3

A financial analyst wants to compare the mean annual returns of three different investment strategies. Which statistical test is most appropriate?

Accepted Answer

One-way ANOVA. ANOVA is used to compare means of three or more groups.

Answer

Chi-square test

Answer

Paired t-test

Answer

Two-sample t-test

Question 4

In logistic regression, the output is a probability between 0 and 1. If the predicted probability for a customer churning is 0.7 and the decision threshold is 0.5, what is the predicted class?

Accepted Answer

Churn (class 1). In logistic regression, the predicted class is determined by comparing the predicted probability to the decision threshold. Here the probability is 0.7 and the threshold is 0.5; since 0.7 ≥ 0.5, the model predicts the positive class, which is churn (class 1).

Answer

Not churn (class 0)

Answer

Both classes equally likely

Answer

Uncertain, need more data

Question 5

A data scientist applies K-means clustering to a customer dataset. The elbow method suggests using 4 clusters. After running K-means with k=4, the within-cluster sum of squares (WCSS) is plotted against k, and the elbow is at k=4. What does this indicate?

Accepted Answer

Increasing k beyond 4 would not significantly reduce WCSS.. The elbow method plots the within-cluster sum of squares (WCSS) against the number of clusters k. The 'elbow' point indicates where the rate of decrease in WCSS sharply changes, meaning that adding more clusters beyond that point yields diminishing returns. Therefore, at k=4, increasing k further would not significantly reduce WCSS, suggesting 4 is a reasonable choice for the number of clusters.

Answer

The data naturally forms 4 clusters with no noise.

Answer

The algorithm converged to a local minimum.

Answer

The model has overfit the data.

Question 6

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

Accepted Answer

K-means clustering. K-means clustering is an unsupervised learning algorithm that partitions observations into k groups based on similarity across multiple features, making it ideal for segmenting customers by purchase history and demographics. It doesn't require labeled outcomes, which matches the exploratory nature of customer segmentation. The algorithm iteratively assigns points to the nearest centroid and recalculates centroids until convergence.

Answer

Linear regression

Answer

Chi-square test

Answer

Logistic regression

Question 7

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?

Accepted Answer

Min-max normalization. Min-max normalization (A) is correct because it rescales each feature to a common range, typically [0, 1], via (x - min) / (max - min), so that no feature dominates the Euclidean distance calculation due to a larger scale. Z-score standardization (B) is also correct because it transforms each feature to mean 0 and standard deviation 1 using (x - μ) / σ, which equalizes the variance and ensures all features contribute equally to Euclidean distance. Log transformation (C) is not a scale-equalizing technique; it only compresses skewed distributions and does not guarantee equal feature contribution. Principal component analysis (D) is a dimensionality-reduction method, not a preprocessing step for equalizing feature scales. One-hot encoding (E) is for converting categorical variables into binary vectors and does not address differing numeric scales.

Answer

Log transformation

Answer

Principal component analysis

Answer

One-hot encoding

Question 8

A data analyst is evaluating the quality of a customer database. Which THREE of the following are dimensions of data quality?

Accepted Answer

Completeness. Accuracy, completeness, and timeliness are standard data quality dimensions.

Answer

Correlation

Answer

Variance

Question 9

A researcher is designing an A/B test to compare two website layouts. Which TWO elements are essential for determining the required sample size?

Accepted Answer

Statistical power. Statistical power (B) is essential because it defines the probability of detecting a true effect when one exists, and standard sample-size formulas require a target power (typically 0.80) as an input. Desired effect size (D) is also essential because the minimum detectable or expected difference between the two layouts directly determines how many observations are needed—smaller effects require larger samples. Together with the significance level (alpha), power and effect size are the core parameters in sample-size calculations for A/B tests. The sample mean (A) is an outcome estimated from data, not an input for planning sample size. Confidence interval width (C) is a result that depends on sample size rather than a prerequisite for computing it. The p-value (E) is also a post-hoc result of the test, not a design parameter used to determine required sample size.

Answer

Sample mean

Answer

Confidence interval width

Answer

P-value

Question 10

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?

Accepted Answer

Two-sample t-test. The scenario compares the means of two independent groups (Layout A vs. Layout B) on a continuous outcome (sales revenue), which is precisely what the two-sample t-test is designed for. With 30 stores per group, the sample size is adequate for the t-test's assumptions, and the groups are independent (different stores). This is the standard test for a two-group mean comparison.

Answer

ANOVA

Answer

Chi-square test

Answer

Paired t-test

Question 11

In a regression analysis, the coefficient of determination (R²) is 0.85. How should this value be interpreted?

Accepted Answer

85% of the variance in the dependent variable is explained by the model. R², the coefficient of determination, measures the proportion of variance in the dependent variable that is explained by the independent variables in the regression model. An R² of 0.85 means 85% of the variance in the dependent variable is accounted for by the model, indicating a strong fit.

Answer

85% of the data points lie on the regression line

Answer

The slope of the regression line is 0.85

Answer

85% of the independent variables are significant

Question 12

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?

Accepted Answer

k=5. The elbow method plots WCSS against k and looks for the 'elbow' — the point where the rate of decrease sharply changes from steep to shallow. Since WCSS drops sharply until k=5 and then levels off, k=5 is the optimal choice because additional clusters beyond that yield diminishing returns.

Answer

k=6

Answer

k=4

Answer

k=3

Question 13

A stock analyst is analyzing monthly sales data for a retail company and observes a consistent pattern of high sales every December. This pattern is most likely an example of which time series component?

Accepted Answer

Seasonality. A consistent spike every December is a calendar-driven repeating pattern, which is the definition of seasonality in time series analysis. The fixed annual period (12 months) and predictable timing make this a classic seasonal component.

Answer

Irregular

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Cyclical

Answer

Trend

Question 14

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?

Accepted Answer

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

Answer

Accept the alternative hypothesis; the treatment is better

Answer

The test is inconclusive

Answer

Fail to reject the null hypothesis; no significant difference

Question 15

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?

Accepted Answer

For each additional support call, the log-odds of churn increase by 0.8, and this effect is statistically significant.. In logistic regression, coefficients are expressed in log-odds units. A coefficient of 0.8 for 'customer support calls' means each additional call increases the log-odds of churn by 0.8, holding other predictors constant. The p-value of 0.001 is below the conventional 0.05 threshold, so the effect is statistically significant.

Answer

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

Answer

Support calls have no significant effect on churn.

Answer

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