DA0-002 Data Analysis Practice Test 3 — 15 Questions

Question 1

A data scientist is tuning a decision tree model to prevent overfitting. The model currently has a high variance. Which hyperparameter adjustment is most effective?

Accepted Answer

Reduce maximum depth. Reducing maximum depth limits the number of splits in the decision tree, which directly reduces model complexity and variance. A high-variance model is overfitting to training data, and capping depth prevents the tree from learning overly specific patterns that do not generalize.

Answer

Increase minimum samples split

Answer

Increase number of leaves

Answer

Use a smaller dataset

Question 2

Which TWO of the following are dimensional modeling techniques commonly used in data warehouses?

Accepted Answer

Snowflake schema. The snowflake schema is a dimensional modeling technique where dimension tables are normalized into multiple related tables, reducing data redundancy. This structure is commonly used in data warehouses to improve query performance and maintainability for complex analytical queries.

Answer

Entity-relationship diagram

Answer

Scatter plot

Answer

Histogram

Question 3

Which THREE of the following are common steps in data cleaning?

Accepted Answer

Imputing missing values. Imputing missing values is a common data cleaning step because real-world datasets often have gaps due to data collection errors or system failures. Techniques like mean/median imputation, regression imputation, or using algorithms like k-NN help preserve sample size and avoid bias that would result from simply dropping rows. This ensures the dataset remains usable for analysis without introducing significant distortion.

Answer

Removing outliers without justification

Answer

Increasing sample size

Question 4

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

Accepted Answer

Train-test split. 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.

Answer

Bootstrapping

Answer

Feature scaling

Answer

Increasing training data

Question 5

Refer to the exhibit. Which clause is used to aggregate the data by department?

Accepted Answer

GROUP BY. The GROUP BY clause is used to aggregate data by department because it groups rows that have the same values in the specified column(s), allowing aggregate functions like SUM, AVG, or COUNT to be applied per group. In SQL, without GROUP BY, aggregate functions would operate on the entire result set, not per department.

Answer

HAVING

Answer

WHERE

Answer

ORDER BY

Question 6

Refer to the exhibit. Which type of ensemble method is being used?

Accepted Answer

Bagging. The exhibit shows multiple base models (Model 1, Model 2, Model 3) trained in parallel on bootstrap samples of the data, and their predictions are combined via averaging (regression) or majority voting (classification). This parallel training with resampled data and equal-weight aggregation is the defining characteristic of bagging (Bootstrap Aggregating).

Answer

Boosting

Answer

Stacking

Answer

Voting

Question 7

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

Accepted Answer

Consistency. 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.

Answer

Uniqueness

Answer

Timeliness

Answer

Completeness

Question 8

A data analyst is building a linear regression model to predict sales based on advertising spend. The analyst notices that the residuals are not normally distributed and have a non‑constant variance. Which of the following transformations is most appropriate to apply to the dependent variable?

Accepted Answer

Logarithmic transformation. The logarithmic transformation is the most appropriate choice because it stabilizes non‑constant variance (heteroscedasticity) and helps make the residuals more normally distributed, which are key assumptions for linear regression. By compressing the scale of the dependent variable (sales), it reduces the impact of large values and often linearizes multiplicative relationships, such as diminishing returns from advertising spend.

Answer

Standardization (z-score)

Answer

Normalization (min-max scaling)

Answer

Square root transformation

Question 9

A company’s marketing team wants to segment customers based on purchase history, demographics, and website behavior. The data includes both numeric and categorical variables. Which clustering algorithm is best suited for handling mixed data types?

Accepted Answer

Hierarchical clustering with Gower distance. Hierarchical clustering with Gower distance is best suited for mixed data types because Gower distance computes a dissimilarity measure that handles both numeric and categorical variables by normalizing numeric differences and using a simple matching coefficient for categorical ones. This allows the algorithm to create a distance matrix that equally weights all variable types, making it ideal for segmenting customers with purchase history, demographics, and website behavior data.

Answer

K-modes clustering

Answer

DBSCAN with Euclidean distance

Answer

K-means clustering

Question 10

A data scientist is building a classification model to predict customer churn. The dataset has 10,000 records with 500 churners. The scientist uses logistic regression and achieves 98% accuracy, but the precision for churn class is only 15%. Which of the following is the most likely cause?

Accepted Answer

Class imbalance. The dataset has only 500 churners out of 10,000 records (5% churn rate), which is a classic class imbalance. Logistic regression can achieve high accuracy by simply predicting the majority class (non-churn) for all records, yielding 95% accuracy even without learning anything about churn. The very low precision (15%) for the churn class indicates that most of the positive predictions are false positives, a direct consequence of the model being biased toward the majority class due to imbalance.

Answer

Non‑linear decision boundary

Answer

Multicollinearity among predictor variables

Answer

Overfitting due to too many features

Question 11

During ETL, a data analyst discovers that a date column contains values like '01/02/2023' and '2023-01-02'. Which of the following is the best practice to ensure consistent date format before analysis?

Accepted Answer

Apply a standardized date parsing function to convert all dates. Applying a standardized date parsing function (e.g., `TO_DATE` in SQL or `pd.to_datetime` in Python) ensures all date values are converted to a single, consistent format regardless of the original representation. This is a fundamental ETL best practice to avoid ambiguity and enable accurate date-based filtering, aggregation, and joins during analysis.

Answer

Keep both formats and handle during analysis

Answer

Use regular expressions to parse and convert each format

Answer

Remove records with inconsistent date formats

Question 12

A data analyst is reviewing a SQL query that joins three large tables. The query takes over an hour to run. The analyst notices that the WHERE clause filters on indexed columns in only two tables. Which of the following should the analyst do first to improve performance?

Accepted Answer

Check the query execution plan and optimize join order. The query execution plan reveals how the database engine processes joins and filters. By checking the plan, the analyst can identify the most selective filter and rearrange the join order to reduce the number of rows processed early, which is the most impactful first step. Optimizing join order leverages existing indexes without requiring schema changes or hardware upgrades.

Answer

Use subqueries instead of joins

Answer

Add indexes to all columns used in joins

Answer

Increase server memory

Question 13

An analyst is fitting a polynomial regression model and wants to choose the degree that minimizes overfitting. Which technique should the analyst use?

Accepted Answer

Ridge regression (L2). Ridge regression (L2) adds a penalty proportional to the square of the magnitude of coefficients, which shrinks them toward zero but does not eliminate them. This regularization reduces variance and helps prevent overfitting in polynomial regression by controlling the influence of higher-degree terms, making it the correct technique for minimizing overfitting while retaining all features.

Answer

Lasso regression (L1)

Answer

Principal component analysis (PCA)

Answer

Stepwise selection

Question 14

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

Accepted Answer

Stacked bar chart. 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.

Answer

Line chart

Answer

Scatter plot

Answer

Pie chart

Question 15

A company has a dataset with 100 features. The data analyst wants to reduce dimensionality while preserving as much variance as possible. Which technique should be used?

Accepted Answer

PCA (Principal Component Analysis). PCA is the correct choice because it is an unsupervised linear dimensionality reduction technique that projects the data onto orthogonal components ordered by the variance they capture. By selecting the top principal components, the analyst can retain the maximum possible variance in the dataset while reducing the number of features from 100 to a smaller set, directly addressing the goal of preserving variance.

Answer

LDA (Linear Discriminant Analysis)

Answer

Autoencoders

Answer

t-SNE