DA0-002 Practice Test 28 — 15 Questions

Question 1

An analyst creates a bar chart showing average sales by region. They want to ensure that the y-axis starts at zero to avoid misleading interpretation. This practice aligns with which dashboard design principle?

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

Accepted Answer

Appropriate precision. Starting the y-axis at zero is a fundamental practice to ensure that the visual representation of data (bar heights) accurately reflects the actual values, preventing exaggeration or minimization of differences. This principle directly supports 'Appropriate Precision' by maintaining the integrity of the data visualization and avoiding misleading interpretations, which is a key consideration in dashboard design.

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Visual hierarchy

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Clear labels and titles

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Data-ink ratio

Question 2

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.

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Bootstrapping

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Feature scaling

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Increasing training data

Question 3

A business analyst needs to explain to a sales director that the reported revenue has a 95% confidence interval of ±2%. Which concept is being communicated?

Accepted Answer

Uncertainty communication. Confidence intervals quantify the uncertainty around a metric, conveying that the true value may vary.

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Data governance

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Single version of truth

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Data lineage

Question 4

A data analyst is building a dashboard for executives and wants to ensure the most important metric, total revenue, is immediately visible. Which design principle should the analyst apply?

Accepted Answer

Visual hierarchy. Visual hierarchy is the design principle of arranging elements so the most important information draws the eye first, typically through size, position, color contrast, or whitespace. Placing total revenue prominently (e.g., top-left, largest font, high-contrast KPI card) ensures executives see it immediately. This directly addresses the requirement to make the key metric immediately visible.

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Appropriate precision

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Consistent color coding

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Data-ink ratio

Question 5

A data analyst is presenting a time-series chart of monthly sales to executives. The sales dropped sharply in March due to a one-time supply chain disruption. Which storytelling technique would best help the audience understand this anomaly?

Accepted Answer

Add an annotation explaining the supply chain disruption. Adding an annotation directly on the chart provides immediate context for the March sales drop, allowing executives to understand the anomaly without leaving the visualization. This technique follows the principle of 'contextual annotation' in data storytelling, where key events are marked to prevent misinterpretation of trends. It preserves data integrity while clarifying the cause, which is essential for accurate decision-making.

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Remove the March data point to avoid confusion

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Use a different chart type to hide the drop

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Use a moving average to smooth the drop

Question 6

A data analyst needs to show the relationship between advertising spend (in dollars) and the number of website visits. Both variables are continuous. Which chart type is most suitable?

Accepted Answer

Scatter plot. A scatter plot is the correct choice for visualizing the relationship between two continuous variables, such as advertising spend and website visits. It plots each observation as a point on a two-dimensional plane, allowing the analyst to observe correlation, trends, clusters, and outliers. This makes it ideal for bivariate continuous data analysis.

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Line chart

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Box plot

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Bar chart

Question 7

A data analyst is preparing to run an A/B test comparing two email subject lines. Which TWO of the following should the analyst define before the test begins?

Accepted Answer

The null and alternative hypotheses. Before A/B testing, define null and alternative hypotheses, and determine sample size needed for desired statistical power and effect size.

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The exact lift in conversion rate

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The time series decomposition

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The p-value after the test

Question 8

A scatter plot of advertising spend vs. revenue shows no clear correlation, but the analyst suspects a relationship exists. Which addition to the plot could help reveal a hidden trend?

Accepted Answer

Add a trendline. A trendline (regression line) added to a scatter plot can reveal a relationship that is not obvious from raw points alone, such as a nonlinear or weak correlation. It summarizes the overall direction and strength of the relationship, helping the analyst detect hidden trends. Changing chart type, marker size, or removing data points does not reveal underlying correlation.

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Change to a bar chart

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Increase the marker size

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Remove data points with low spend

Question 9

A data analyst is performing a chi-square test of independence on a 2x2 contingency table. The p-value is 0.04. At α=0.05, which THREE of the following statements are correct?

Accepted Answer

There is a statistically significant association between the two variables.. Option A is correct because with p = 0.04 < α = 0.05, the chi-square test of independence yields a statistically significant result, indicating evidence of an association between the two variables. Option C is correct because rejecting the null hypothesis of independence means the data support that the two variables are not independent (i.e., they are associated). Option D is correct because the decision rule for a chi-square test of independence is to reject the null hypothesis when p < α, and 0.04 < 0.05. Option B is not correct because statistical significance does not imply a strong association; strength would require an effect size such as Cramér's V or the phi coefficient. Option E is not correct because p = 0.04 is below α = 0.05, so the result is statistically significant, not non-significant.

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The test indicates a strong association between variables.

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The result is not statistically significant.

Question 10

A data analyst needs to present the results of a customer segmentation analysis to the marketing team. The analysis identified four segments based on purchasing behavior. Which visualization is most effective for showing the characteristics of each segment?

Accepted Answer

Radar chart. A radar chart is the most effective visualization for comparing multiple quantitative variables across different categories, such as the purchasing behavior characteristics of each customer segment. It allows the marketing team to see the profile of each segment at a glance by plotting each characteristic on a separate axis radiating from a central point, making it easy to identify strengths, weaknesses, and similarities between segments.

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Histogram

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Heatmap

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Scatter plot

Question 11

A company wants to determine if there is a significant difference in the average sales revenue between two different store layouts. They collect sales data from 30 stores with Layout A and 30 stores with Layout B. Which statistical test is most appropriate for comparing the means of these two independent groups?

Accepted Answer

Two-sample t-test. The scenario compares the means of two independent groups (Layout A vs. Layout B) on a continuous outcome (sales revenue), which is precisely what the two-sample t-test is designed for. With 30 stores per group, the sample size is adequate for the t-test's assumptions, and the groups are independent (different stores). This is the standard test for a two-group mean comparison.

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ANOVA

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Chi-square test

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Paired t-test

Question 12

A data analyst wants to assign a unique sequential integer to each row in a result set, starting at 1, based on the order of the 'sales_amount' column descending. Which window function should be used?

Accepted Answer

ROW_NUMBER() OVER (ORDER BY sales_amount DESC). ROW_NUMBER() assigns a unique sequential integer to each row starting at 1, with no ties and no gaps, based on the ORDER BY clause. Since the requirement is a unique sequential integer per row ordered by sales_amount DESC, ROW_NUMBER() is the correct function.

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DENSE_RANK() OVER (ORDER BY sales_amount DESC)

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RANK() OVER (ORDER BY sales_amount DESC)

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NTILE(1) OVER (ORDER BY sales_amount DESC)

Question 13

A data analyst at a retail company is building a multiple linear regression model to forecast weekly sales. The dataset contains 50 predictor variables, including store size, promotional spend, holiday indicators, and many others. After training the model, the analyst observes an R-squared of 0.99 on the training set but only 0.55 on the holdout test set. Which action should the analyst take first to address this discrepancy?

Accepted Answer

Remove highly correlated predictor variables and apply regularization (e.g., Ridge or Lasso).. The high R-squared of 0.99 on training data versus 0.55 on test data is a classic sign of overfitting, where the model has learned noise and specific patterns in the training set that do not generalize. Removing highly correlated predictors reduces multicollinearity and model complexity, while regularization (Ridge or Lasso) penalizes large coefficients, shrinking them to prevent overfitting. This is the most direct first step to improve generalization.

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Add more predictor variables to increase the training R-squared further.

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Use k-fold cross-validation with a different random seed to get a more reliable test set estimate.

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Increase the number of hidden layers in the model to capture more complexity.

Question 14

A company needs to store user session data for a web application. Each session has a unique session ID, and the data must be retrieved very quickly by session ID. The data does not require complex relationships or transactions. Which type of NoSQL database is most appropriate?

Accepted Answer

Key-value store (e.g., Redis). A key-value store like Redis is the most appropriate choice because it is optimized for extremely fast lookups by a unique key (session ID) and does not require complex relationships or transactions. Redis stores data in memory, providing sub-millisecond retrieval times ideal for session management, and supports built-in expiration (TTL) to automatically clean up stale sessions.

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Wide-column store (e.g., Cassandra)

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Document store (e.g., MongoDB)

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Graph database (e.g., Neo4j)

Question 15

An organization uses a data warehouse for analytics. The data team wants to load data from source systems into the warehouse. They choose to load raw data first and then perform transformations within the warehouse. Which approach are they using?

Accepted Answer

ELT. ELT (Extract, Load, Transform) is correct because the raw data is extracted from source systems and loaded directly into the data warehouse before any transformation occurs. Transformations are then performed inside the warehouse using its own compute engine (e.g., SQL, dbt, or Snowflake virtual warehouses). This is the defining characteristic of the ELT pattern, which leverages modern cloud warehouse scalability.

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Data lake

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Data mart

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ETL