DA0-002 Practice Exam 51 — 20 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.

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Increase minimum samples split

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Increase number of leaves

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Use a smaller dataset

Question 2

A data analyst wants to compare the sales performance of four different stores over the same time period. Which chart type is most suitable?

Accepted Answer

Grouped bar chart. A grouped bar chart is the most suitable choice because it allows direct comparison of discrete categories (four stores) across a common time period, with each group representing a time interval and individual bars representing each store's sales. This chart type excels at side-by-side comparisons of multiple entities over the same categorical axis, making it ideal for the analyst's goal.

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Line chart with multiple lines

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Stacked bar chart

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Pie chart with multiple pies

Question 3

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.

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Ridge regression

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Linear regression

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Lasso regression

Question 4

You are a data analyst for an e-commerce company. Your team has built a dashboard to monitor daily sales performance across five regions: North, South, East, West, and Central. The dashboard includes a bar chart showing total sales per region, a line chart showing daily sales trend over the past 30 days, and a pie chart showing sales distribution by product category (Clothing, Electronics, Home, Books, Sports). Recently, stakeholders have complained that the pie chart is hard to interpret because the Sports category has very small sales and is barely visible. Also, the bar chart uses a rainbow color scheme that makes it difficult to compare bar heights because the colors are not ordered by magnitude. The line chart is fine. You need to redesign the dashboard to address these issues. Which combination of changes is most appropriate?

Accepted Answer

Group small categories into an 'Other' slice in the pie chart and use a sequential color scheme ordered by sales for the bar chart. Grouping small categories into an 'Other' slice directly addresses the pie chart's readability issue by consolidating negligible values, and using a sequential color scheme ordered by sales for the bar chart improves the ability to compare bar heights by encoding magnitude through color intensity. This combination follows best practices for data visualization: avoid cluttering with tiny slices and use ordered, perceptually uniform colors to facilitate accurate comparisons.

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Replace the pie chart with a stacked bar chart and use a categorical color scheme for the bar chart

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Explode the Sports slice in the pie chart and use a monochromatic color scheme for the bar chart

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Change the pie chart to a 3D pie chart and use a diverging color scheme for the bar chart

Question 5

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.

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Entity-relationship diagram

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Scatter plot

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Histogram

Question 6

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.

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K-modes clustering

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DBSCAN with Euclidean distance

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K-means clustering

Question 7

A data analyst needs to merge two customer tables from different sources. One table uses 'CUST_ID' as the primary key, the other uses 'CustomerID'. To ensure accurate merging, the analyst should first:

Accepted Answer

Normalize the key column names to a common format. Normalizing key column names to a common format (Option B) is the correct first step because the merge operation requires a consistent join key. Without aligning 'CUST_ID' and 'CustomerID' to a single name and data type, the database or ETL tool will treat them as different columns, resulting in a cross join or an error. This step ensures referential integrity and enables an accurate inner or outer join based on the primary key.

Answer

Perform a fuzzy match on names

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Remove duplicate rows from both tables

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Aggregate data by region

Question 8

A data scientist trains a regression model and observes high variance with low bias. Which technique is most appropriate to reduce variance?

Accepted Answer

Apply Ridge regularization. Ridge regularization (L2) reduces variance by adding a penalty term proportional to the square of the coefficients, which shrinks them toward zero without eliminating them. This directly addresses high variance (overfitting) by constraining the model's complexity, while low bias indicates the model fits the training data well. The regularization parameter λ controls the trade-off between bias and variance.

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Increase polynomial features

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Use a smaller training set

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Remove correlated features

Question 9

A sales manager receives a daily report at 8 AM via email showing yesterday's sales by region. This is an example of which report type?

Accepted Answer

Scheduled report. The report is automatically delivered on a regular schedule, which defines a scheduled report.

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Ad hoc report

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Self-service report

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Operational report

Question 10

A data analyst is creating a report to compare the performance of sales regions across multiple years. The report will be used by regional managers to identify trends. Which visualization approach best supports this?

Accepted Answer

Small multiples of line charts, one per region. Small multiples (trellis charts) allow comparison of multiple time series in a compact, consistent layout, making it easy to spot trends across regions.

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A single line chart with all regions overlaid

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A bar chart with years on x-axis and regions as grouped bars

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A stacked area chart with all regions

Question 11

Match each data visualization type to its best use case.

Accepted Answer

Bar chart: Compare quantities across categories. Correct matches: Bar chart compares categories, Line chart shows trends over time, Pie chart shows proportions. Common confusions: swapping time trend with bar chart or proportion with category comparison.

Answer

Bar chart: Show trends over time

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Pie chart: Compare quantities across categories

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Scatter plot: Show distribution of a single variable

Question 12

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

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Use a polynomial transformation

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Increase the number of features

Question 13

A data analyst is tasked with gathering data from a legacy system that only exports CSV files. The files contain headers but no data types. Which tool would best facilitate initial data exploration?

Accepted Answer

Python pandas. Python pandas. Python pandas is ideal for initial data exploration of CSV files because it provides Series and DataFrames for tabular data, automatic type inference, and functions like head(), info(), describe() to quickly understand the data. Option A (Hadoop) is overkill for a simple CSV file exploration. Option B (Tableau) is primarily for visualization and requires data to be already structured or imported. Option C (SQL database) is not the best tool for immediate exploration since it requires database setup and data import. Therefore, Python pandas is the most appropriate tool for initial exploration of CSV data.

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Hadoop

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Tableau

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SQL database

Question 14

During EDA, an analyst calculates the Z-score for each data point in a dataset. A data point with a Z-score of 3.5 is identified. What does this indicate?

Accepted Answer

The data point is likely an outlier. A Z-score of 3.5 means the value is 3.5 standard deviations from the mean, commonly considered an outlier (threshold often >3 or <-3).

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The data point has a high frequency

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The data point is exactly at the mean

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The data point is within the interquartile range

Question 15

Which TWO of the following are best practices when presenting data insights to a non-technical audience?

Accepted Answer

Focus on actionable insights and recommendations. Non-technical audiences need clear, actionable insights to make decisions. Presenting recommendations directly from the data ensures the insights are useful and drive business outcomes, which is a core principle of effective data communication.

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Use technical jargon to demonstrate expertise

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Present raw data tables for transparency

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Include detailed statistical formulas

Question 16

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.

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Duplicate customer IDs

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Missing values in total_charges

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Outliers in monthly_charges

Question 17

A data analyst wants to compare the average revenue per customer between two marketing campaigns (A and B). The analyst is unsure if the data follows a normal distribution. Which statistical test is most appropriate for comparing the means of the two groups?

Accepted Answer

Two-sample t-test. For comparing means of two independent groups, the t-test is the standard parametric test. If normality is violated, a non-parametric alternative like Mann-Whitney U could be used, but the t-test is robust for moderate sample sizes.

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Pearson correlation

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Chi-square test

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ANOVA

Question 18

A retail company is merging customer data from three separate systems: an e-commerce platform, a point-of-sale (POS) system, and a loyalty program. The e-commerce platform stores customer names in "FirstName LastName" format, the POS system stores names as "LastName, FirstName", and the loyalty program stores names in separate "first_name" and "last_name" fields. The data analyst needs to create a unified customer master table. After initial merging, there are 20% more records than expected, including duplicates with slight name variations (e.g., "John Smith" vs "John A. Smith"). To ensure accurate consolidation, which data concept should the analyst prioritize applying first?

Accepted Answer

Data standardization. Data standardization is the correct first step because it resolves the inconsistent name formats (e.g., 'FirstName LastName', 'LastName, FirstName', and separate fields) into a single, consistent representation. By applying a standardized format (e.g., 'FirstName LastName'), the analyst can then accurately identify and merge duplicates like 'John Smith' and 'John A. Smith' using fuzzy matching or exact matching on the standardized values. This ensures the unified customer master table has the correct number of records without the 20% inflation caused by formatting variations.

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Data profiling

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Data indexing

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Data encryption

Question 19

A report uses red and green colors to indicate performance; a stakeholder who is colorblind complains. What is the BEST remedy?

Accepted Answer

Add patterns or labels in addition to color. Adding patterns or labels alongside color ensures that the data encoding is accessible to colorblind viewers without removing the informational value of color for those who can see it. This approach follows WCAG (Web Content Accessibility Guidelines) 2.1 Success Criterion 1.4.1, which requires that color is not the sole means of conveying information. In data visualization tools like Tableau or Power BI, this is implemented by adding shape markers or text annotations to series, preserving the original color scheme for non-colorblind users while providing redundant encoding.

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Remove color entirely

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Change colors to blue and orange

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Use a different chart type

Question 20

A dataset contains a column 'Age' with values: [22, 25, 25, 30, 35, 40, 45]. What is the interquartile range (IQR)?

Accepted Answer

15. Q1 is median of lower half (22,25,25) = 25; Q3 is median of upper half (35,40,45) = 40; IQR = 40-25 = 15.

Answer

10

Answer

20

Answer

25