GMAT Quiz: Evaluate Cross Source Consistency
11 questions · exam conditions
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Evaluate Cross Source ConsistencyQuestion 1 of 11

HR: 20 employees left. Payroll: 5 retired, 10 resigned, 6 dismissed. Consistent?

Yes; all are departures
No; components total 21
Yes; 5+10+6 equals 20
No; retirees do not leave
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GMAT Quiz

GMAT Quiz: Evaluate Cross Source Consistency

Practice Evaluate Cross Source Consistency in GMAT with focused quiz questions that help you check what you know, review explanations, and build confidence with test-style prompts.

What this quiz covers

This quiz focuses on Evaluate Cross Source Consistency, giving you a quick way to practice the rules, question types, and explanations that matter most for GMAT.

How to use this quiz

Try each quiz question before looking at the correct answer. Use the explanations to review missed ideas, then come back to similar questions until the pattern feels familiar.

All questions

Question 1

HR: 20 employees left. Payroll: 5 retired, 10 resigned, 6 dismissed. Consistent?

  1. Yes; all are departures
  2. No; components total 21 (correct answer)
  3. Yes; 5+10+6 equals 20
  4. No; retirees do not leave
Explanation: Add the components: 5 retired plus 10 resigned plus 6 dismissed equals 21, not 20. So the categories don't match the stated total of employees who left. The tempting wrong answer is the one that says 5+10+6 equals 20, but it actually sums to 21.

Question 2

Survey: 90% of buyers repurchased. Finance: repeat orders are 10% of sales. Are sources consistent?

  1. No conflict; metrics differ (correct answer)
  2. Conflict; rates should match
  3. No conflict; if bases match
  4. Conflict; both measure repeats
Explanation: These numbers don't measure the same thing: 90% of customers made another purchase, while repeat orders are only 10% of total sales volume. A large share of buyers can account for a small share of orders if most customers order once or repeat buyers order less often. The tempting mistake is calling it a conflict because 90 and 10 seem far apart, but the metrics differ, so they can both be true.

Question 3

Report A: 60% of 1,000 patients improved. Report B: 75% of the 800 who returned improved. Assess consistency.

  1. No conflict; counts are 600 (correct answer)
  2. Conflict; rates differ
  3. Conflict; bases differ
  4. No conflict; B is current
Explanation: Both reports give the same count: 60% of 1,000 is 600, and 75% of 800 is also 600. The tempting mistake is to compare the rates directly, but the percentages apply to different base groups. Since both report 600 improved, there is no conflict.

Question 4

Year-end inventory log: 120 units. June delivery invoice: 120 units. Support same balance?

  1. Yes; the figures match
  2. No; the dates differ
  3. No; the units differ
  4. Cannot tell; sales may occur (correct answer)
Explanation: The two numbers being 120 only shows the invoice and log match numerically. The invoice is from June while the log is year-end, so months of sales or purchases could happen in between; a June delivery doesn't prove the year-end balance is still 120. The tempting error is choosing 'Yes; the figures match' because equal numbers look consistent, but the different dates mean you cannot conclude.

Question 5

Segment report: profit $10M. Corporate model lists contribution $12M and allocated overhead $2M. Consistent?

  1. Yes; 12 minus 2 equals 10
  2. No; profit should equal $12M
  3. Cannot tell; costs may exist (correct answer)
  4. No; overhead reduces profit
Explanation: Contribution of $12M and allocated overhead of $2M give $10M only if those are the only costs. Other unlisted costs could exist, so segment profit of $10M may or may not be consistent. The tempting error is assuming 12 - 2 = 10 proves it; that ignores possible additional costs.

Question 6

A financial services company is reconciling customer account data from three different systems following a recent system migration:

Source A - Customer Database: Lists 34,720 active accounts with total assets under management of $847,300,000. The database shows 18,960 individual accounts and 15,760 business accounts.

Source B - Transaction Processing: Records show 2,847,200 transactions processed in Q4, generating $12,680,000 in fee revenue. Average fee per transaction was $4.45.

Source C - Compliance Reporting: Filed reports for 34,650 accounts with combined assets of $851,200,000. Individual accounts represent 54.8% of total accounts and 43.2% of total assets.

Based on the three data sources, which statement about cross-source consistency is most accurate?

  1. Source A and Source C show minor account count differences but significant asset valuation discrepancies that require immediate investigation.
  2. Source B's fee calculations are internally consistent, but the transaction volume appears inconsistent with the customer base size from Sources A and C.
  3. Sources A and C are largely consistent in their account distributions and asset values, while Source B provides independent transaction data without conflicts.
  4. Source C's percentage breakdowns contradict Source A's explicit account counts, indicating classification errors in either individual or business account categories. (correct answer)
Explanation: Source A explicitly states 18,960 individual accounts out of 34,720 total, which equals 54.6%. Source C states individual accounts represent 54.8% of 34,650 accounts, which would be 18,988 individual accounts. The percentages are close but the absolute numbers don't align properly. More importantly, Source C says individual accounts hold 43.2% of $851,200,000 = 367,718,400inassets.IfSourceA′sindividualaccounts(367,718,400 in assets. If Source A's individual accounts (847,300,000 total) had the same proportion, they'd hold about 43.2% as well, but Source A shows a different total asset base. This suggests classification inconsistencies between individual/business categories. Choice A misses the classification issue. Choice B incorrectly suggests transaction volume is inconsistent (2.8M transactions for 34K accounts is reasonable). Choice C incorrectly claims consistency exists.

Question 7

A logistics company is analyzing delivery performance using data from three operational systems:

Source 1 - Order Management: In October, 8,940 orders were processed with promised delivery times averaging 4.2 days. Orders were distributed as follows: 35% same-city, 45% regional, 20% national.

Source 2 - Fleet Tracking: Vehicle logs show 2,150 same-city deliveries averaging 1.8 days, 3,420 regional deliveries averaging 3.5 days, and 1,680 national deliveries averaging 7.1 days.

Source 3 - Customer Feedback: Survey responses from 1,247 customers indicate actual delivery times of 2.1 days for same-city, 3.8 days for regional, and 7.3 days for national deliveries. Response rate was consistent across all delivery types.

What is the most significant consistency issue when comparing these three data sources?

  1. Source 2 delivery counts don't align with Source 1 distribution percentages, suggesting incomplete fleet tracking or order classification errors. (correct answer)
  2. Source 3 customer feedback shows delivery times that are consistently longer than Source 1 promises, but this reflects realistic expectation management.
  3. Sources 2 and 3 show similar actual delivery times, but both contradict Source 1's promised delivery average in ways that suggest systematic timing calculation errors.
  4. The survey response rate mentioned in Source 3 is insufficient to validate the delivery time claims against the operational data from Sources 1 and 2.
Explanation: Source 1 indicates order distribution of 35% same-city (3,129 orders), 45% regional (4,023 orders), and 20% national (1,788 orders) based on 8,940 total orders. Source 2 shows 2,150 + 3,420 + 1,680 = 7,250 total deliveries, which is significantly less than 8,940 orders processed. Additionally, the proportions don't match: Source 2 shows 2,150/7,250 = 29.7% same-city vs. 35% expected. This suggests incomplete tracking or classification problems. Choice B incorrectly characterizes the relationship between promises and actual times. Choice C is wrong because the weighted average of actual times roughly matches promises. Choice D misses the more fundamental counting discrepancy.

Question 8

A pharmaceutical company is reviewing clinical trial data from multiple tracking systems:

Source X - Patient Enrollment: 480 patients enrolled across 3 treatment groups (160 per group). Enrollment period: January 2023 to March 2023.

Source Y - Dosage Administration Log: Shows 43,200 total doses administered over 12 weeks of treatment. Each patient received daily doses throughout the trial period.

Source Z - Outcome Assessment: Reports that 456 patients completed the full trial protocol. Of these, 152 were in Group A, 148 in Group B, and 156 in Group C. Assessment period: January 2023 to June 2023.

Based on the information provided, which analysis of cross-source consistency is most accurate?

  1. Source Y's dosage data is inconsistent with the patient enrollment from Source X, indicating systematic under-dosing or incomplete logging. (correct answer)
  2. Source Z's completion data aligns perfectly with Source X enrollment, but the timeline discrepancy suggests data collection errors.
  3. All sources are consistent when accounting for the 24 patients who withdrew before trial completion and standard clinical trial timelines.
  4. Source Y's administration period contradicts Source Z's assessment timeline, creating an irreconcilable temporal inconsistency in the trial design.
Explanation: To check consistency: If 480 patients were enrolled and received daily doses for 12 weeks (84 days), the expected total doses would be 480 × 84 = 40,320 doses. However, Source Y reports 43,200 doses administered, which exceeds this maximum possible amount, indicating a significant inconsistency. Choice B is wrong because 456 ≠ 480 (24 patients didn't complete). Choice C is wrong due to the dosage inconsistency identified above. Choice D is wrong because a 12-week treatment period within a January-June assessment timeline is reasonable for clinical trials.

Question 9

A healthcare network is analyzing patient flow data from three integrated systems to optimize resource allocation:

Source Red - Appointment Scheduling: Scheduled 18,940 patient appointments across all departments in March. Emergency department handled 4,680 appointments, Primary care handled 8,920 appointments, and Specialty clinics handled 5,340 appointments.

Source Blue - Patient Check-in System: Recorded 17,850 actual patient visits in March. No-show rate was 12.3% across all departments, with Emergency having 2.1% no-shows, Primary care having 15.7% no-shows, and Specialty clinics having 18.9% no-shows.

Source Green - Billing Records: Generated bills for 17,735 patient encounters in March. Emergency encounters: 4,582, Primary care encounters: 7,523, Specialty clinic encounters: 5,630.

Which analysis of cross-source consistency is most accurate based on this healthcare data?

  1. Source Blue's overall no-show rate is mathematically inconsistent with the department-specific rates and actual visit counts reported.
  2. Source Green's billing records align closely with Source Blue's visit records, but show concerning discrepancies in specialty clinic encounter counts. (correct answer)
  3. All three sources demonstrate good consistency when accounting for normal variations in patient flow and administrative processing delays.
  4. Source Red's scheduled appointments and Source Blue's actual visits show expected patterns, but Source Green reveals systematic billing capture problems.
Explanation: Let's verify the data: Source Blue shows 17,850 actual visits vs Source Green's 17,735 billed encounters - only 115 difference (good alignment). However, examining specialty clinics specifically: Source Blue indicates specialty no-show rate of 18.9%, so actual visits = 5,340 × (1 - 0.189) = 4,331 visits. But Source Green shows 5,630 specialty encounters billed, which exceeds even the original 5,340 scheduled appointments. This is impossible and indicates a significant discrepancy. Choice A is wrong - the overall no-show calculation is consistent: (4,680×0.021 + 8,920×0.157 + 5,340×0.189) ÷ 18,940 = 12.2%, close to reported 12.3%. Choice C misses the specialty clinic discrepancy. Choice D incorrectly characterizes this as systematic billing problems rather than the specific specialty clinic inconsistency.

Question 10

A consulting firm is analyzing employee productivity across three departments. They have collected data from multiple sources:

Source 1 - HR Database: Marketing department has 45 employees with average tenure of 3.2 years. Sales department has 38 employees with average tenure of 2.8 years. Operations department has 52 employees with average tenure of 4.1 years.

Source 2 - Productivity Report: In Q3, Marketing generated 180 projects, Sales closed 228 deals, and Operations processed 312 orders. The report notes that each Marketing project requires 0.8 person-months, each Sales deal requires 0.5 person-months, and each Operations order requires 0.4 person-months.

Source 3 - Financial Summary: Q3 departmental costs were Marketing $486,000, Sales $456,000, and Operations $624,000. This includes all salaries and overhead allocated proportionally to headcount.

Based on the three data sources, which statement about cross-source consistency is most accurate?

  1. The productivity metrics are consistent across sources, but the financial data contains discrepancies that cannot be reconciled with the staffing information.
  2. All three sources are internally consistent and support each other when accounting for different productivity requirements per department.
  3. Source 2 productivity data is inconsistent with Source 1 staffing levels, indicating either underreporting of work or overestimation of time requirements. (correct answer)
  4. The financial allocation method described in Source 3 contradicts the actual productivity output ratios calculated from Sources 1 and 2.
Explanation: To evaluate consistency, we need to check if the reported work output aligns with available capacity. Marketing: 180 projects × 0.8 person-months = 144 person-months needed, but with 45 employees over 3 months = 135 person-months available. Sales: 228 deals × 0.5 = 114 person-months needed vs. 38 × 3 = 114 available (consistent). Operations: 312 orders × 0.4 = 124.8 person-months needed vs. 52 × 3 = 156 available (consistent). Marketing shows a capacity shortfall, indicating inconsistency in Source 2. Choice A is wrong because financial data is proportional to headcount as stated. Choice B is wrong due to the Marketing inconsistency. Choice D is wrong because the allocation method is clearly described and doesn't contradict productivity ratios.

Question 11

A retail analytics team has gathered customer data from three different systems:

Source A - E-commerce Platform: Shows 15,420 unique customers made purchases in November, with average order value of $67.30 and total revenue of $1,037,646.

Source B - Customer Service Database: Records 2,184 support tickets in November, with 1,847 tickets from existing customers and 337 from new customers. The database shows 14,965 active customer accounts.

Source C - Email Marketing System: Indicates 16,890 customers received promotional emails in November, with 3,378 customers making purchases after email engagement (20% conversion rate).

When evaluating these three data sources for consistency, what is the most significant discrepancy that requires investigation?

  1. Source A's total revenue calculation doesn't match the product of unique customers and average order value, suggesting data aggregation errors.
  2. Source C's customer count exceeds Source A's purchasing customers, but this is explained by customers who received emails but didn't purchase.
  3. Source B's active customer count is lower than Source A's purchasing customers, which violates the logical relationship between these metrics. (correct answer)
  4. Source C's post-email purchase count significantly exceeds what Source A's revenue figures could support at the stated average order value.
Explanation: The critical inconsistency is that Source B shows 14,965 active customers while Source A shows 15,420 customers made purchases. This is impossible since customers who made purchases must be active customers. The number of purchasing customers cannot exceed the number of active customers. Choice A is incorrect: 15,420 × $67.30 = $1,037,646 (matches exactly). Choice B describes a normal relationship - more customers received emails than purchased. Choice D is incorrect: 3,378 post-email purchases × $67.30 average = $227,339, which is easily supported within the total revenue of $1,037,646.