GMAT Data Insights Quiz: Evaluate Cross Source Consistency

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 Data Insights.

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.

Question 1

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 = $367,718,400 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 2

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 3

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 4

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 5

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 6

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.