DA0-002 Data Governance Practice Test 4 — 15 Questions

Question 1

A company is designing a dashboard for real-time monitoring. Which THREE considerations are most critical?

Accepted Answer

Alert thresholds. Alert thresholds (B) are critical for real-time monitoring because they define the conditions that trigger notifications when metrics exceed or fall below acceptable ranges. Without thresholds, the dashboard cannot proactively alert operators to anomalies, defeating the purpose of real-time oversight. This directly supports the domain of communicating data insights by ensuring actionable alerts are delivered promptly.

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Color palette aesthetics

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Drill-down capability

Question 2

Which TWO are common pitfalls when communicating data insights?

Accepted Answer

Using misleading scales on charts. Using misleading scales on charts (e.g., truncating the y-axis or using non-zero baselines) distorts the visual representation of data, leading to incorrect interpretations. This violates the principle of data integrity in data visualization, as it can exaggerate or minimize trends, making it a common pitfall when communicating data insights.

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Explaining assumptions clearly

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Including a clear call to action

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Providing context for the data

Question 3

Refer to the exhibit. An analyst runs a query to count orders in June 2023 and gets 12,345. However, a dashboard shows 12,298 for the same month. What is the most likely cause?

Accepted Answer

The dashboard includes time zone conversion. The most likely cause is that the dashboard applies a time zone conversion to the order timestamps, while the analyst's query counts orders based on UTC or a different time zone. If the dashboard converts timestamps to a local time zone (e.g., US/Eastern), orders placed near midnight UTC may fall into a different calendar day or month, causing a discrepancy of 47 orders. This is a common issue when raw data is stored in UTC but reporting tools apply a time zone offset without adjusting the query logic.

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The query has a syntax error

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The query excludes orders that were canceled

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The dashboard is using a different data source

Question 4

Refer to the exhibit. A data analyst is unable to run a query on the customers table after October 1, 2023. What is the reason?

Accepted Answer

The policy allows access only before October 1, 2023. The policy explicitly allows access only before October 1, 2023, meaning any query attempt on or after that date is denied. This is a time-based access control condition often implemented using a policy engine that checks the current time against a condition. Since the query fails after October 1, 2023, the policy's effective date restriction is the direct cause.

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The resource name is incorrect

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The action should be READ not SELECT

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The policy denies access after October 1, 2023

Question 5

Refer to the exhibit. An ETL pipeline failed. What is the most likely root cause?

Accepted Answer

Network connectivity issues. The exhibit shows an ETL pipeline failure with a timeout error, which is a classic symptom of network connectivity issues. When the pipeline cannot reach the source or target system due to a network interruption, the process hangs until a timeout threshold is exceeded, causing the job to fail. This is the most likely root cause because timeouts are directly associated with network problems, not with data format, authentication, or storage issues.

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Data format mismatch

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Authentication failure

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Insufficient disk space

Question 6

A stakeholder asks for the exact number of customers who churned last month. Which metric should the analyst report?

Accepted Answer

Count of churned customers. The stakeholder explicitly asks for the 'exact number' of customers who churned, which is a discrete count. Option C, 'Count of churned customers,' directly provides this integer value without any normalization or ratio. The analyst should report the raw metric that matches the request's specificity.

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Churn trend

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Churn rate percentage

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Churn probability

Question 7

An analyst notices that a dashboard displays conflicting data from two sources. What is the first step in troubleshooting?

Accepted Answer

Check data transformation steps and join logic. The first step in troubleshooting conflicting data from multiple sources is to verify the data transformation steps and join logic. Conflicts often arise from incorrect joins (e.g., using INNER JOIN instead of LEFT JOIN), mismatched keys, or data type conversions that alter values. Checking these steps isolates the root cause before any other action, such as re-running queries or notifying stakeholders.

Answer

Re-run all queries

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Notify stakeholders of potential error

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Replace the data source with the one believed to be correct

Question 8

When designing a report for executive leadership, which aspect is most important?

Accepted Answer

High-level summaries with key insights. Executive leadership requires actionable insights, not raw data. High-level summaries with key insights (Option D) allow leaders to quickly grasp trends, make decisions, and align with business goals without getting bogged down in technical details. This aligns with the DA0-001 objective of communicating data insights effectively to non-technical stakeholders.

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Detailed technical notes

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Raw data tables

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All raw SQL queries

Question 9

A data analyst needs to communicate a forecast with uncertainty. Which visualization is best?

Accepted Answer

Line chart with confidence intervals. A line chart with confidence intervals is the best choice because it explicitly visualizes the forecast trend over time while also displaying the range of uncertainty (e.g., 95% confidence bands). This allows the data analyst to communicate both the central projection and the variability around it, which is essential for informed decision-making.

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Stacked bar chart

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Pie chart

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Histogram

Question 10

During a presentation, a stakeholder questions the validity of a correlation found. What is the best response?

Accepted Answer

Correlation does not imply causation, but we can perform further analysis.. It directly addresses the stakeholder's concern about validity by acknowledging the fundamental statistical principle that correlation does not imply causation. It then proposes a constructive next step—further analysis—which aligns with best practices in data communication, where validating insights requires additional testing (e.g., controlled experiments or causal inference methods). This response demonstrates both technical honesty and a commitment to rigorous data-driven decision-making.

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We can accept the correlation as true.

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We used a large sample so it's valid.

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The p-value is low, so it's significant.

Question 11

Which type of chart is most suitable for showing the distribution of a single continuous variable?

Accepted Answer

Histogram. A histogram is the most suitable chart for showing the distribution of a single continuous variable because it groups the data into bins (intervals) along the x-axis and displays the frequency or count of observations within each bin on the y-axis. This allows you to see the shape, spread, and central tendency of the data, such as whether it is normally distributed, skewed, or has multiple modes. For a single continuous variable, a histogram directly visualizes the probability density or frequency distribution, which is its primary purpose in data analysis.

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Line chart

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Scatter plot

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Bar chart

Question 12

A data team is creating a dashboard to monitor real-time sales. What design principle is critical?

Accepted Answer

Use auto-refresh and clear alert thresholds. For a real-time sales dashboard, the critical design principle is to ensure data freshness and immediate actionability. Option B is correct because auto-refresh keeps the dashboard current without manual intervention, and clear alert thresholds enable the team to instantly identify when sales metrics deviate from expected ranges, which is essential for real-time monitoring.

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Provide downloadable raw data

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Include all historical data

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Minimize use of color

Question 13

An analyst finds that a key metric drops significantly after a data pipeline update. How should the analyst proceed?

Accepted Answer

Compare data before and after update to identify discrepancies. The first step in diagnosing a sudden metric drop after a pipeline update is to perform a controlled comparison of pre- and post-update data. This involves validating data schemas, row counts, and distribution statistics to pinpoint whether the update introduced a transformation error, a filtering issue, or a data type mismatch. Without this comparison, the analyst cannot determine if the drop is due to a genuine data change or a pipeline defect.

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Assume it's a seasonal effect

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Revert the pipeline immediately

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Document the drop and report it

Question 14

An analyst is creating a report for both technical and executive audiences. Which two strategies are effective? (Choose TWO.)

Accepted Answer

Use visual summaries for executives and detailed tables for technical. It tailors the data presentation to the audience: visual summaries (e.g., dashboards with KPIs) allow executives to quickly grasp high-level trends, while detailed tables (e.g., pivot tables or raw query results) give technical users the granularity they need for deep analysis. This dual approach ensures both groups can derive actionable insights without being overwhelmed or underwhelmed by the data.

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Include all raw data in the appendix

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Avoid any technical terms

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Provide a single chart for all audiences

Question 15

When presenting data insights, which three practices help ensure clarity? (Choose THREE.)

Accepted Answer

Use consistent color coding. Consistent color coding across charts and dashboards reduces cognitive load, allowing viewers to quickly associate colors with specific categories or metrics. This practice aligns with data visualization best practices for clarity, as it leverages pre-attentive processing to speed up interpretation without requiring the audience to re-learn the mapping for each chart.

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Include all data points in the chart

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Overlay multiple chart types on one axis