DA0-002 Data Analysis Practice Test 2 — 15 Questions

Question 1

A data analyst is building a linear regression model to predict sales based on advertising spend across TV, radio, and newspaper channels. Which TWO diagnostics should the analyst perform to validate the model assumptions?

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

Accepted Answer

Q-Q plot to assess normality of residuals. A Q-Q plot is used to assess whether the residuals of a linear regression model are approximately normally distributed, which is a key assumption for valid inference (e.g., p-values and confidence intervals). Option E is correct because residual plots (e.g., fitted vs. residuals) are the standard diagnostic to check for homoscedasticity—constant variance of errors across all levels of the independent variables—another core assumption of ordinary least squares regression.

Answer

Durbin-Watson test for autocorrelation

Answer

Variance inflation factor (VIF) for multicollinearity

Answer

Cook's distance to identify influential points

Question 2

A data analyst is preparing a logistic regression model to predict customer churn. After examining the exhibit, which data quality issue should the analyst address first?

Accepted Answer

Inconsistent data in total_charges. Inconsistent data formats, such as mixed numeric representations in a column like 'total_charges', will cause parsing errors when loading data into a logistic regression model. Inconsistent data must be standardized (e.g., to a uniform numeric type) before any other preprocessing steps, as it prevents the model from executing. This issue is more critical to address first than missing values or outliers, which can be handled later through imputation or transformation without blocking model execution.

Answer

Duplicate customer IDs

Answer

Missing values in total_charges

Answer

Outliers in monthly_charges

Question 3

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?

Accepted Answer

Apply SMOTE to oversample the readmission class. 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.

Answer

Remove features with low variance to reduce noise

Answer

Use accuracy as the evaluation metric to monitor improvement

Answer

Switch to a random forest model with default settings

Question 4

Drag and drop the steps to normalize a database table from 1NF to 3NF in the correct order.

Accepted Answer

Step 1: Achieve 1NF by removing repeating groups and ensuring atomic values. Step 2: Achieve 2NF by removing partial dependencies. Step 3: Achieve 3NF by removing transitive dependencies.. Normalization proceeds from 1NF to 2NF to 3NF, then table creation and foreign keys.

Answer

Step 1: Achieve 3NF by removing transitive dependencies. Step 2: Achieve 2NF by removing partial dependencies. Step 3: Achieve 1NF by removing repeating groups.

Answer

Step 1: Achieve 2NF by removing partial dependencies. Step 2: Achieve 1NF by removing repeating groups. Step 3: Achieve 3NF by removing transitive dependencies.

Answer

Step 1: Achieve 1NF by removing repeating groups. Step 2: Achieve 3NF by removing transitive dependencies. Step 3: Achieve 2NF by removing partial dependencies.

Question 5

Drag and drop the steps to implement a data classification policy in the correct order.

Accepted Answer

Define classification levels, assign ownership, label data, enforce access control, train employees. Classification involves defining levels, assigning ownership, labeling, access control, and training.

Answer

Train employees, define classification levels, assign ownership, label data, enforce access control

Answer

Label data, define classification levels, assign ownership, enforce access control, train employees

Answer

Define classification levels, label data, assign ownership, enforce access control, train employees

Question 6

Match each data governance role to its responsibility.

Accepted Answer

Data Steward: Responsible for ensuring data quality, defining data standards, and managing metadata.. 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.

Answer

Data Steward: Implements technical security controls.

Answer

Data Owner: Ensures data quality and manages metadata.

Question 7

Match each data sampling method to its description.

Accepted Answer

Simple Random Sampling: Every member of the population has an equal chance of being selected.. 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.

Answer

Simple Random Sampling: Population is divided into clusters, and entire clusters are randomly selected.

Answer

Stratified Sampling: Every kth member is selected from a list after a random start.

Question 8

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?

Accepted Answer

Star schema. 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.

Answer

Entity-relationship model

Answer

Snowflake schema

Answer

3NF normalized model

Question 9

A data scientist is building a predictive model to forecast monthly sales. The data shows a linear trend with no seasonality. Which regression technique is most appropriate?

Accepted Answer

Linear regression. Linear regression is the most appropriate technique because the data shows a linear trend with no seasonality, making a straight-line model the simplest and most effective fit. It directly models the relationship between the independent variable (e.g., time) and the dependent variable (monthly sales) using a linear equation, minimizing the sum of squared residuals.

Answer

Polynomial regression

Answer

Logistic regression

Answer

Ridge regression

Question 10

A data analyst is working with a dataset containing house prices. After building a multiple linear regression model, the analyst observes that the model performs well on training data but poorly on validation data. Which technique is most appropriate to address this issue?

Accepted Answer

Apply L2 regularization (Ridge). The model is overfitting the training data, as evidenced by high performance on training data but poor performance on validation data. L2 regularization (Ridge) adds a penalty term proportional to the square of the coefficients, which shrinks them and reduces model complexity, thereby improving generalization to unseen data.

Answer

Decrease the training data size

Answer

Use a polynomial transformation

Answer

Increase the number of features

Question 11

A marketing team wants to segment customers into distinct groups based on purchasing behavior. The data includes numeric features such as frequency, monetary value, and recency. Which unsupervised learning algorithm should be used?

Accepted Answer

K-means clustering. K-means clustering is the correct choice because it is an unsupervised learning algorithm that partitions data into K distinct clusters based on feature similarity. For segmenting customers by purchasing behavior (frequency, monetary value, recency), K-means groups customers with similar numeric patterns without requiring labeled outcomes, making it ideal for exploratory segmentation.

Answer

Decision tree

Answer

Linear regression

Answer

Association rules

Question 12

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?

Accepted Answer

Standardization (z-score). 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.

Answer

Min-max normalization

Answer

Log transformation

Answer

Box-Cox transformation

Question 13

A data analyst is building a binary classification model to predict customer churn. The dataset is imbalanced, with only 10% churners. The analyst wants to evaluate model performance with a focus on correctly identifying churners. Which metric is most appropriate?

Accepted Answer

Recall (sensitivity). Recall (sensitivity) is the most appropriate metric because it measures the proportion of actual churners correctly identified by the model. Since the dataset is imbalanced (only 10% churners) and the analyst's focus is on correctly identifying churners, recall directly addresses the cost of missing positive cases (false negatives). Accuracy would be misleading due to class imbalance, while precision and F1-score prioritize different trade-offs.

Answer

F1-score

Answer

Precision

Answer

Accuracy

Question 14

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?

Accepted Answer

LEFT JOIN. 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.

Answer

RIGHT JOIN

Answer

FULL OUTER JOIN

Answer

INNER JOIN

Question 15

A data analyst is analyzing survey responses where respondents rated satisfaction on a scale of 1-5. The analyst wants to visualize the distribution of responses. Which chart type is most appropriate?

Accepted Answer

Histogram. A histogram is the most appropriate chart for visualizing the distribution of a single discrete variable, such as satisfaction ratings on a 1-5 scale. It groups the responses into bins (each rating value) and displays the frequency of each bin using bars, clearly showing the shape, central tendency, and spread of the data.

Answer

Box plot

Answer

Scatter plot

Answer

Line chart