DA0-002 Data Analysis Practice Test 5 — 15 Questions

Question 1

A data scientist is working with a dataset containing 1000 features and 500 samples. The goal is to build a predictive model. Which technique should be used to reduce the number of features while retaining most of the variance?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Principal Component Analysis (PCA). Principal Component Analysis (PCA) is an unsupervised dimensionality reduction technique that transforms the original features into a set of orthogonal components, ordered by the variance they capture. Given 1000 features and only 500 samples, PCA is ideal because it reduces the feature space while retaining the maximum variance, helping to avoid overfitting and the curse of dimensionality.

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Ridge regression

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Forward selection

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Lasso regression

Question 2

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

Accepted Answer

Mean imputation. 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.

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Mode imputation

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Dropping columns with missing data

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Dropping rows with missing data

Question 3

Which THREE of the following are assumptions of linear regression? (Select THREE).

Accepted Answer

Independence of errors. Independence of errors is a core assumption of linear regression, meaning the residuals (errors) should not be correlated with each other. This is critical for valid inference because correlated errors violate the Gauss-Markov theorem, leading to biased standard errors and unreliable hypothesis tests. In time series data, this assumption is often violated due to autocorrelation, which can be detected using the Durbin-Watson test.

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Normal distribution of independent variables

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Multicollinearity among independent variables

Question 4

Which TWO of the following are true about correlation and causation? (Select TWO).

Accepted Answer

Correlation does not imply causation. Correlation measures the strength and direction of a linear relationship between two variables, but it does not imply that one variable causes the other. Causation requires controlled experiments with randomization to rule out confounding variables and establish a cause-effect relationship.

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Correlation measures both linear and nonlinear relationships

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Causation can always be inferred from a controlled experiment without randomization

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If two variables are highly correlated, one must cause the other

Question 5

The exhibit shows an SQL query executed on an 'orders' table that contains 'order_id', 'customer_id', and 'order_date'. What is the purpose of this query?

Accepted Answer

Identify customers who placed more than 5 orders in 2023. The query groups orders by customer_id and filters using a HAVING clause with COUNT(*) > 5, which counts the number of orders per customer. The WHERE clause restricts orders to those placed in 2023, so the result identifies customers who placed more than 5 orders in that year. This matches option D exactly.

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Count total orders per customer regardless of date

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Calculate average order count per customer for 2023

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Find products with more than 5 orders in 2023

Question 6

Given the linear regression output, which independent variable has the strongest effect on price, based on standardized coefficients?

Accepted Answer

sqft_living. Standardized coefficients (beta weights) allow comparison of the relative strength of independent variables by measuring the number of standard deviations the dependent variable changes per one standard deviation change in the predictor. In the regression output, sqft_living has the highest absolute standardized coefficient, indicating it has the strongest effect on price. The intercept is not an independent variable and its coefficient is not standardized for comparison.

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bathrooms

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Intercept

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bedrooms

Question 7

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

Accepted Answer

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

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It throws an error because 'total' column already exists

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It reads the data and displays all rows

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It reads the data and displays summary statistics

Question 8

A data analyst needs to summarize customer satisfaction scores. The data contains a few extremely low scores that skew the distribution. Which measure of central tendency is most appropriate?

Accepted Answer

Median. The median is the most appropriate measure of central tendency when data contains extreme outliers, such as the very low customer satisfaction scores described. Unlike the mean, the median is resistant to skew because it depends only on the middle value(s) of the sorted dataset, not on the magnitude of extreme values. This makes it the standard choice for summarizing ordinal or skewed interval/ratio data in data analysis.

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Range

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Mode

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Mean

Question 9

A retail company wants to predict sales based on advertising spend and season. Which data modeling technique should the analyst use?

Accepted Answer

Multiple linear regression. Multiple linear regression is the correct technique because the analyst needs to model a continuous outcome (sales) based on two or more predictor variables: advertising spend (continuous) and season (categorical, typically encoded as dummy variables). This allows the model to capture the independent effect of each predictor on sales, which simple linear regression cannot do because it only handles one predictor.

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Simple linear regression

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Logistic regression

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K-means clustering

Question 10

A data analyst trains a complex model that achieves 99% accuracy on training data but only 65% on new data. What is the most likely issue?

Accepted Answer

Overfitting. The model performs exceptionally well on training data (99% accuracy) but poorly on new data (65% accuracy), which is the classic symptom of overfitting. Overfitting occurs when the model learns noise and specific patterns in the training data rather than generalizing to unseen data, often due to excessive complexity (e.g., too many parameters or deep layers). This results in high variance and poor performance on validation or test sets.

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Underfitting

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Multicollinearity

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High bias

Question 11

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?

Accepted Answer

Diagnostic analysis. 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.

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Predictive analysis

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Prescriptive analysis

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Descriptive analysis

Question 12

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?

Accepted Answer

Normalization (scaling). 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.

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Feature selection

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Dimensionality reduction

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One-hot encoding

Question 13

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?

Accepted Answer

Linearity. 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.

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Independence of residuals

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Homoscedasticity

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Normality of residuals

Question 14

A data analyst calculates a correlation coefficient of -0.85 between temperature and heating costs. What does this indicate?

Accepted Answer

Strong negative correlation. A correlation coefficient of -0.85 indicates a strong negative linear relationship between temperature and heating costs. As temperature increases, heating costs decrease significantly, and the magnitude of 0.85 (close to -1) confirms the strength of this inverse association.

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No correlation

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Strong positive correlation

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Weak negative correlation

Question 15

A company wants to segment its customers into distinct groups based on purchasing behavior. Which algorithm is best suited for this task?

Accepted Answer

K-means clustering. K-means clustering is an unsupervised learning algorithm that partitions data into K distinct clusters based on feature similarity, making it ideal for segmenting customers by purchasing behavior without predefined labels. It groups customers who exhibit similar purchasing patterns, enabling the company to identify natural segments for targeted marketing.

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Decision tree

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Logistic regression

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Linear regression