DA0-002 Practice Test 9 — 10 Questions

Question 1

A data analyst is creating a dashboard in Looker Studio for an e-commerce company. They want to display the average order value by product category, and also allow users to filter by date range. Which combination of elements should be used?

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

Accepted Answer

Dimension: Product Category; Metric: Average Order Value; Control: Date Range. In Looker Studio, the field you group by is the dimension and the field you aggregate is the metric. To show average order value by product category, Product Category must be the dimension and Average Order Value the metric, with a Date Range control added for user filtering. This is the only option that assigns the roles correctly and includes the required control.

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Dimension: Order Value; Metric: Product Category; Control: Date Range

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Dimension: Date; Metric: Product Category; Control: None

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Dimension: Average Order Value; Metric: Product Category; Control: Date Range

Question 2

Refer to the exhibit. A database administrator notices that queries filtering on both CustomerID and OrderDate are slow. Which single change would most likely improve performance for such queries?

Accepted Answer

Add a composite index on (CustomerID, OrderDate). A composite index on (CustomerID, OrderDate) allows the database to satisfy queries that filter on both columns using a single index seek, avoiding a full table scan or multiple index lookups. The column order matters: CustomerID first supports equality filtering, and OrderDate second supports range or sort operations within each customer. This is the most direct performance improvement for the described query pattern.

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Partition the table by OrderDate

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Convert TotalAmount to VARCHAR

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Remove the primary key constraint

Question 3

In simple linear regression, the coefficient of determination R² measures:

Accepted Answer

The proportion of variance in the dependent variable explained by the independent variable. R², the coefficient of determination, quantifies the proportion of variance in the dependent variable that is explained by the independent variable(s) in the regression model. It ranges from 0 to 1, where 1 means the model explains all variance and 0 means it explains none.

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The probability that the slope is zero

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The slope of the regression line

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The strength and direction of the linear relationship

Question 4

In a regression analysis, the coefficient of determination (R²) is 0.85. How should this value be interpreted?

Accepted Answer

85% of the variance in the dependent variable is explained by the model. R², the coefficient of determination, measures the proportion of variance in the dependent variable that is explained by the independent variables in the regression model. An R² of 0.85 means 85% of the variance in the dependent variable is accounted for by the model, indicating a strong fit.

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85% of the data points lie on the regression line

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The slope of the regression line is 0.85

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85% of the independent variables are significant

Question 5

A data analyst wants to compare the sales revenue of five different product categories for the current month. Which chart type is most suitable for this comparison?

Accepted Answer

Bar chart. A bar chart encodes each product category as a discrete bar whose length maps to revenue, making side-by-side comparison of five categorical values immediate and precise. Categorical comparisons are the canonical use case for bar charts because the human eye judges length differences far more accurately than angles or slopes.

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Histogram

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Pie chart

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Line chart

Question 6

A data analyst is creating a presentation for executives to explain why customer churn has increased over the last quarter. The analyst wants to present the story in a compelling way. Which narrative structure is most appropriate?

Accepted Answer

Situation, Complication, Resolution. The Situation-Complication-Resolution structure is ideal for executive presentations because it first establishes the context (situation), then introduces the problem (complication—increased churn), and finally proposes a solution (resolution). This narrative arc aligns with how executives process strategic issues, making the data story compelling and actionable. In contrast, other structures are better suited for technical reports or hypothesis testing, not high-level storytelling.

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Problem, Hypothesis, Test

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Background, Analysis, Recommendation

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Data, Visualization, Conclusion

Question 7

A data analyst calculates the mean, median, and mode of a dataset. Which measure of central tendency is most affected by extreme outliers?

Accepted Answer

Mean. The mean is sensitive to extreme values because it includes all data points in its calculation, whereas median and mode are more robust.

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Range

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Median

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Mode

Question 8

A data governance team is establishing policies to ensure data quality. They define rules for data accuracy, completeness, and consistency. Which data governance function is primarily responsible for defining and enforcing these rules?

Accepted Answer

Data quality management. Data quality management is the governance function specifically responsible for defining data quality dimensions (accuracy, completeness, consistency, timeliness, validity, uniqueness) and implementing the rules, profiling, and monitoring to enforce them. It translates governance policy into measurable quality controls and remediation workflows.

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Data stewardship

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Data ownership

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Master data management

Question 9

A data analyst is performing data acquisition from multiple source files. Which TWO data profiling tasks should the analyst complete before loading the data into the target system?

Accepted Answer

Verify data types and formats. Option B (Verify data types and formats) is correct because data profiling must confirm that each source column's actual data type (e.g., integer, string, date) and format (e.g., YYYY-MM-DD, decimal separators) match what the target system expects, preventing load failures or silent corruption. Option E (Identify missing values and nulls) is correct because profiling must detect NULLs, empty strings, and placeholder values so the analyst can decide on imputation, defaulting, or rejection rules before loading. Option A (Create a dashboard for stakeholders) is a downstream reporting activity, not a pre-load profiling task. Option C (Build a linear regression model) is predictive analytics performed after data is cleansed and loaded, not profiling. Option D (Perform cluster analysis) is an unsupervised modeling technique also done post-load, so it does not belong in pre-load data profiling.

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Create a dashboard for stakeholders

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Build a linear regression model

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Perform cluster analysis

Question 10

A data analyst needs to visualize the distribution of customer ages in a dataset containing 10,000 records. The analyst wants to see the shape of the distribution and identify any outliers. Which chart type is most appropriate?

Accepted Answer

Histogram. A histogram is most appropriate because it displays the full shape of a continuous distribution (e.g., normal, skewed, bimodal) across bins, and the tails of the histogram make extreme values visually apparent. With 10,000 records, binning ages reveals both the overall distribution shape and where outliers fall at the extremes. Box plots also show outliers, but they compress the distribution into five summary statistics and hide the shape.

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Line chart

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Box plot

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Pie chart