DA0-002 Data Analysis Practice Test 4 — 15 Questions

Question 1

After training a decision tree, the tree has depth 20 and 100% accuracy on training data but only 60% on test data. Which hyperparameter adjustment is most likely to improve generalization?

Accepted Answer

Increase minimum samples per leaf. The model is overfitting: 100% training accuracy vs. 60% test accuracy with a depth-20 tree. Increasing minimum samples per leaf forces the tree to be simpler by requiring more samples in each leaf, reducing variance and improving generalization. This directly combats the overfitting caused by the overly deep tree.

Answer

Increase number of estimators

Answer

Decrease minimum samples per split

Answer

Increase maximum depth

Question 2

A data analyst is performing hypothesis testing to compare the mean sales of two store locations. Which TWO conditions must be satisfied to use a two‑sample t‑test? (Select TWO.)

Accepted Answer

The data is approximately normally distributed. The two-sample t-test assumes that the data in each group are approximately normally distributed. This is a key parametric assumption; if the sample sizes are large (typically n > 30), the Central Limit Theorem can relax this requirement, but for smaller samples, normality must hold to ensure valid test statistics and p-values.

Answer

The data is paired between the two locations

Answer

The sample sizes are equal

Answer

The variances of the two populations are equal

Question 3

A data scientist is cleaning a dataset and notices missing values in several columns. Which THREE techniques are appropriate for handling missing data? (Select THREE.)

Accepted Answer

Replace missing values with the mean or median. Replacing missing values with the mean (for normally distributed data) or median (for skewed data) is a standard imputation technique that preserves the central tendency of the dataset without introducing bias. This method is appropriate when the missingness is random and the proportion of missing data is low, as it maintains the sample size for analysis.

Answer

Ignore missing values and proceed with analysis

Answer

Always replace missing values with zero

Question 4

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

Accepted Answer

Correlation coefficient. 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.

Answer

P-value

Answer

Regression coefficient

Question 5

Refer to the exhibit. An analyst runs the following query:
SELECT product_id, AVG(quantity)
FROM sales
GROUP BY product_id
HAVING AVG(quantity) > 8;
Which product_id(s) will be returned?

Accepted Answer

P001 and P003. The query groups sales by product_id and filters groups where the average quantity exceeds 8. From the exhibit (not shown but implied), only product_ids P001 and P003 have an AVG(quantity) > 8, so they are returned. The HAVING clause operates on aggregated data after GROUP BY, unlike WHERE which filters rows before aggregation.

Answer

P001 only

Answer

P002 only

Answer

P003 only

Question 6

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?

Accepted Answer

The median remains unchanged. 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.

Answer

The mean decreased significantly

Answer

The standard deviation increased

Answer

The minimum value decreased

Question 7

Refer to the exhibit. A data analyst wants to grant read access to an entire cloud storage bucket named 'data-lake'. Which of the following best describes what this policy does?

Accepted Answer

Allows read access to all objects in the data-lake bucket. This policy grants read access to all objects within the 'data-lake' bucket. In cloud storage, a bucket-level policy that allows the 'GetObject' action without a condition restricting the resource to a specific prefix or folder effectively permits reading every object in the bucket. Option D correctly identifies this behavior.

Answer

Allows both read and write access to the bucket

Answer

Allows only specific users to read objects

Answer

Allows read access to a specific folder within the bucket

Question 8

A data analyst wants to predict customer churn based on categorical features like region and plan type, and continuous features like usage and tenure. Which regression type should be used?

Accepted Answer

Logistic regression. Logistic regression is the correct choice because the target variable, customer churn, is binary (churn vs. no churn). Logistic regression models the probability of a binary outcome using a sigmoid function, making it suitable for classification tasks with both categorical and continuous predictors.

Answer

Ridge regression

Answer

Linear regression

Answer

Lasso regression

Question 9

A retail company wants to forecast monthly sales for the next 12 months. Sales data shows a clear upward trend and seasonal patterns that repeat yearly. Which time series model is most appropriate?

Accepted Answer

Holt-Winters exponential smoothing. The Holt-Winters exponential smoothing model (option C) is the most appropriate because it explicitly captures both trend and seasonality components, which are present in the sales data (upward trend and yearly seasonal patterns). Unlike simple exponential smoothing, Holt-Winters includes additive or multiplicative seasonal terms, making it ideal for data with clear, repeating seasonal cycles over a 12-month horizon.

Answer

SARIMA

Answer

Simple exponential smoothing

Answer

ARIMA

Question 10

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?

Accepted Answer

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

Answer

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

Answer

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

Answer

Remove outliers using Z-score, then apply standard scaling

Question 11

During data exploration, an analyst notices that the target variable has a heavily right-skewed distribution. Which data transformation would be most appropriate to make the distribution more symmetric?

Accepted Answer

Log transformation. Log transformation is appropriate for heavily right-skewed distributions because it compresses the high values and spreads out the low values, making the distribution more symmetric. Square root transformation is better for moderate skew, and reciprocal transformation is for severe skew. Therefore, option A (Log transformation) is correct.

Answer

Reciprocal transformation

Answer

No transformation needed

Answer

Square root transformation

Question 12

A marketing team wants to segment customers into groups based on purchasing behavior without prior labels. Which algorithm should the data analyst use?

Accepted Answer

K-means clustering. K-means clustering is the correct choice because it is an unsupervised learning algorithm that groups unlabeled data into clusters based on feature similarity. Since the marketing team has no prior labels for customer segments, K-means can partition customers by purchasing behavior patterns, such as frequency and monetary value, without needing predefined categories.

Answer

K-nearest neighbors

Answer

Linear regression

Answer

Decision tree

Question 13

After building a binary classification model, the data analyst obtains the following confusion matrix: True Positives=80, True Negatives=100, False Positives=20, False Negatives=30. What is the F1 score?

Accepted Answer

0.76. The F1 score is the harmonic mean of precision and recall. Precision = TP/(TP+FP) = 80/(80+20) = 0.80. Recall = TP/(TP+FN) = 80/(80+30) ≈ 0.7273. F1 = 2 * (0.80 * 0.7273) / (0.80 + 0.7273) ≈ 0.7619, which rounds to 0.76. Option A is correct.

Answer

0.73

Answer

0.80

Answer

0.69

Question 14

A dataset contains a column 'Income' with values in different scales (some in thousands, some in hundreds). What is the best way to standardize this column for use in a machine learning model?

Accepted Answer

Apply standard scaling (Z-score normalization). Standard scaling (Z-score normalization) is the best approach because it transforms the data to have a mean of 0 and a standard deviation of 1, making values on different scales (thousands vs hundreds) directly comparable. Min-max scaling also rescales to [0,1], but it is sensitive to outliers and does not handle different scales as effectively when the distribution is not uniform. Log transformation is for reducing skewness, not for standardizing scales. Removing the column discards useful information.

Answer

Apply min-max scaling to range [0,1]

Answer

Apply log transformation

Answer

Remove the column

Question 15

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?

Accepted Answer

Independent samples t-test. 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.

Answer

Chi-square test for independence

Answer

ANOVA

Answer

Paired t-test