DA0-002 Practice Test 18 — 15 Questions

Question 1

A data analyst wants to show the distribution of customer ages for a retail store. The ages are continuous and the analyst needs to visualize the frequency of different age ranges. Which chart type is most appropriate?

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

Accepted Answer

Histogram. A histogram is used to display the distribution of a continuous variable by grouping data into bins.

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Bar chart

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Line chart

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Pie chart

Question 2

Which THREE of the following are components of Master Data Management (MDM)? (Select 3)

Accepted Answer

Data governance. MDM includes data governance, data integration, and data quality management to maintain a single source of truth.

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Data encryption

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Data archival

Question 3

A data analyst uses the following query: SELECT department, AVG(salary) AS avg_salary FROM employees GROUP BY department HAVING AVG(salary) > 50000. What is the purpose of the HAVING clause in this query?

Accepted Answer

To filter groups (departments) based on the average salary. The HAVING clause filters groups after aggregation, so in this query it keeps only departments whose average salary exceeds 50000. It operates on the result of GROUP BY, which is why it can reference aggregate functions like AVG(salary) that WHERE cannot.

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To ensure only departments with more than 50000 employees are shown

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To filter individual employee records before grouping

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To sort departments by average salary

Question 4

An analyst creates a scatter plot with three variables: X, Y, and a third variable represented by the size of the markers. This chart is called a:

Accepted Answer

Bubble chart. A bubble chart is a variation of a scatter plot where a third numeric variable is encoded by the size (area) of the markers. This allows the visualization of three dimensions of data simultaneously on a two-dimensional plane, making it the correct choice for the described chart.

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Heat map

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Treemap

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Waterfall chart

Question 5

Drag and drop the steps to perform a root cause analysis on data quality issues in the correct order.

Accepted Answer

Define the problem, Gather data, Identify potential causes, Determine root cause, Implement corrective actions. Root cause analysis involves defining the problem, gathering data, identifying causes, finding root cause, and acting.

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Gather data, Define the problem, Identify potential causes, Determine root cause, Implement corrective actions

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Define the problem, Identify potential causes, Determine root cause, Gather data, Implement corrective actions

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Implement corrective actions, Define the problem, Gather data, Identify potential causes, Determine root cause

Question 6

A retail company wants to test whether a new website layout increases the conversion rate compared to the current layout. They randomly assign visitors to either the control or treatment group. Which statistical test is most appropriate to compare the conversion rates?

Accepted Answer

Chi-square test. The outcome variable is conversion (yes/no), a categorical binary variable, and the predictor is group (control vs treatment), also categorical. The chi-square test of independence is designed to compare observed versus expected frequencies across categories, making it the correct choice for comparing two conversion rates.

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Two-sample t-test

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ANOVA

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Logistic regression

Question 7

A data engineer is designing a data pipeline for a retail company. The source system is an OLTP database that records sales transactions. The target is a data warehouse used for reporting. The engineer is evaluating whether to use ETL or ELT. Which three factors would favor using ELT over ETL? (Select THREE)

Accepted Answer

The business analysts need access to raw data for ad-hoc exploration. ELT loads raw data into the warehouse first, allowing business analysts to perform ad-hoc exploration directly on the source data without pre-transformation. This flexibility is a key advantage of ELT over ETL, where transformations are applied before loading.

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The transformation logic requires proprietary functions not available in the warehouse

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Data must be cleansed and validated before loading into the warehouse

Question 8

A data analyst uses the elbow method to determine the number of clusters for k-means. The plot shows a sharp bend at k=3 and a small bend at k=5. What is the recommended number of clusters?

Accepted Answer

3. The elbow method suggests choosing k where the decrease in inertia becomes marginal; the sharp bend at 3 indicates the optimal k.

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5

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The method is inconclusive.

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2

Question 9

A data analyst is examining the relationship between advertising spend (in thousands) and sales (in thousands). The Pearson correlation coefficient is computed as r = -0.85. Which of the following interpretations is correct?

Accepted Answer

There is a strong negative linear relationship.. Pearson r measures linear correlation: -0.85 indicates a strong negative linear relationship (as one increases, the other decreases). The magnitude |0.85| is close to 1, so strong.

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There is no linear relationship.

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There is a strong positive linear relationship between advertising spend and sales.

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There is a weak negative linear relationship.

Question 10

Which THREE actions improve the accessibility of data visualizations for users with visual impairments? (Select exactly three.)

Accepted Answer

Provide text alternatives for charts (e.g., data tables).. Option A is correct because providing text alternatives such as data tables gives screen reader users and others who cannot perceive the chart a non-visual equivalent of the data, satisfying WCAG 1.1.1 Non-text Content. Option C is correct because clear, descriptive labels (titles, axis names, legends, and data point labels) let users understand what the visualization represents without relying on visual inference or color alone. Option D is correct because sufficient color contrast between text/graphical elements and their background meets WCAG 1.4.3 and 1.4.11, making charts legible for users with low vision or color vision deficiencies. Option B does not belong because using only color to convey information fails WCAG 1.4.1 Use of Color and excludes users who cannot distinguish those colors. Option E does not belong because animated transitions can trigger vestibular issues and distract users, and they do not improve accessibility of the underlying data.

Answer

Use only color to convey information.

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Add animated transitions between views.

Question 11

A dataset contains a column 'Education Level' with values: 'High School', 'Bachelor', 'Master', 'PhD'. An analyst computes the average by assigning numbers 1-4. Which data concept is being violated?

Accepted Answer

Treating ordinal data as interval. The analyst assigned numeric values (1-4) to 'Education Level' categories and computed an average. This treats the ordinal data as if it were interval data, assuming equal spacing between categories (e.g., the difference between 'High School' and 'Bachelor' is the same as between 'Master' and 'PhD'), which is not valid. Ordinal data only preserves order, not magnitude or equal intervals, so calculating a mean is inappropriate.

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Misclassifying data as structured

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Treating nominal data as ordinal

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Treating ratio data as interval

Question 12

Which data quality dimension is most concerned with whether data values fall within a defined domain or acceptable range?

Accepted Answer

Validity. Validity refers to whether data values conform to defined rules or constraints.

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Completeness

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Consistency

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Accuracy

Question 13

A data analyst is designing a self-service reporting platform for business users. Which TWO practices will help ensure data consistency and trust? (Select TWO.)

Accepted Answer

Implement a single version of truth. Option A (Implement a single version of truth) is correct because consolidating reporting on one governed, authoritative dataset (e.g., a curated gold layer or semantic model) eliminates conflicting figures that arise when users pull from multiple duplicated sources, directly ensuring consistency and trust. Option E (Provide a data dictionary for all metrics) is correct because documenting each metric's exact definition, calculation, source column, and owner gives business users a shared, unambiguous meaning for every KPI, preventing misinterpretation and reinforcing confidence in the numbers. Options B, C, and D do not belong: letting users create their own data sources (B) proliferates ungoverned, inconsistent datasets; granting row-level security to all users (C) is an access-control measure that does not by itself guarantee metric consistency; and removing data lineage tracking (D) destroys the auditability and traceability that underpin trust in the data.

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Allow users to create their own data sources

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Grant row-level security to all users

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Remove all data lineage tracking

Question 14

You are using pandas in Python to clean a dataset. You notice several rows with missing values in the 'age' column. Which method would you use to remove those rows?

Accepted Answer

df.dropna(). The df.dropna() method is specifically designed to remove rows (or columns) that contain missing values (NaN). By default, it drops any row where at least one NaN is present, which directly addresses the requirement to remove rows with missing 'age' values. This is the standard pandas approach for handling incomplete records when deletion is preferred over imputation.

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df.drop_duplicates()

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df.fillna(0)

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df.isna()

Question 15

A data analyst is creating a report on customer satisfaction scores. The analyst wants to ensure that regional managers can only see data for their own region. Which security measure should be applied?

Accepted Answer

Row-level security. Row-level security (RLS) filters query results at the row level based on the identity of the user running the report, so each regional manager automatically sees only the rows belonging to their region. This is the standard mechanism in BI platforms (e.g., Power BI, Tableau, Salesforce) for enforcing per-user data segmentation without duplicating reports. Encryption, masking, and single-version-of-truth do not restrict which rows a user can retrieve.

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Data encryption

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Single version of truth

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Data masking