All 114 DILR Sets Solved | CAT 2017–2024 | CAT 2022 Slot 3: Quant-Based Reasoning | Set 19 #cat2025
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All 114 DILR Sets Solved | CAT 2017–2024 | CAT 2022 Slot 3: Quant-Based Reasoning | Set 19 #cat2025
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GOAT CAT
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5 837 просмотров · 1 год назад
Master DILR with 114 Fully Solved Sets from CAT 2017 to CAT 2024!
In this video, we dive deep into CAT 2022 Slot 3 Quant-Based Reasoning with complete explanations.
The Set: All the first-year students in the computer science (CS) department in a university take both the courses (i) AI and (ii) ML. Students from other departments (non-CS students) can also take one of these two courses, but not both. Students who fail in a course get an F grade; others pass and are awarded A or B or C grades depending on their performance. The following are some additional facts about the number of students who took these two courses this year and the grades they obtained.
1. The numbers of non-CS students who took AI and ML were in the ratio 2 : 5.
2. The number of non-CS students who took either AI or ML was equal to the number of CS students.
3. The numbers of non-CS students who failed in the two courses were the same and their total is equal to the number of CS students who got a C grade in ML.
4. In both the courses, 50% of the students who passed got a B grade. But, while the numbers of students who got A and C grades were the same for AI, they were in the ratio 3 : 2 for ML.
5. No CS student failed in AI, while no non-CS student got an A grade in AI.
6. The numbers of CS students who got A, B and C grades respectively in AI were in the ratio 3 : 5 : 2, while in ML the ratio was 4 : 5 : 2.
7. The ratio of the total number of non-CS students failing in one of the two courses to the number of CS students failing in one of the two courses was 3 : 1.
8. 30 students failed in ML.
Whether you're targeting CAT 2025 or revising key concepts, this is your go-to DILR prep resource.
👉 What you’ll get:
All original CAT DILR sets from 2017–2024
Detailed walk through of CAT 2022 Slot 3
Proven strategies for cracking Quant-based reasoning sets
Expert guidance from aptiGOAT mentor
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