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The Data Interpretation and Logical Reasoning section of the CAT exam has been the pain-point for lakhs of aspirants who have taken the test in the past. Unlike the QA section, the DI-LR section doesn't differentiate between engineers and non-engineers - its wrath is showered upon everyone equally, as was the case in CAT 2017.
The biggest challenge one faces while preparing for this section is that there is no defined syllabus or one 'bible' that can be referred to feel comfortable and completely prepared during those 60 minutes in the actual exam. Recommendations to prepare for this section range from solving Sudoku puzzles every day to daily solving sets from books prescribed by coaching centers. While this may help you prepare, it will pay off to first ask the question - What exactly does CAT test in the DI-LR section? This analysis of past five years' CAT papers can help you find the answer.
Note - This article will not provide a prediction of what you will see in the DI-LR section of the CAT exam but is an analysis of trends that have emerged in 10 CAT papers in the past five years.
We know that DI questions in CAT can be visually daunting, but they can also be made complex by segmenting information into two or more complementary sets of data, as was the case in this question where information was split into a stacked bar chart as well as a table, followed by difficult questions based on the provided information.
The table below shows the ranks of the ten states/UTs mentioned above among ALL states/UTs of India in terms of the number of cases registered in each of the three category of crimes. A state/UT is given rank r for a category of crimes if there are (r‐1) states/UTs having a larger number of cases registered in that category of crimes. For example, if two states have the same number of cases in a category, and exactly three other states/UTs have larger numbers of cases registered in the same category, then both the states are given rank 4 in that category. Missing ranks in the table are denoted by *.
The CAT exam, as recent as CAT 2018, has also tested topics that are usually found in relatively easier exams like NMAT and MAH-CET, such as this set in Slot-2 of CAT 2018 on coding-decoding. However, the level of difficulty is many notches above than what can be seen in aptitude tests like NMAT. This particular question has been categorized under the 'Difficult' section by both TIME and IMS, along with other coaching institutes.
CAT 2019 | CAT 2018 | CAT 2017 | CAT 2016 | CAT 2015 | |
Level of Difficulty | Number of Questions | Slot 1, Slot 2 | ||||
Difficult | 8,8 | 8,8 | 8,4 | 0,4 | 0,16 |
Moderate-Difficult | 4,0 | 0,4 | 16,8 | 0,0 | 16,4 |
Moderate | 8,16 | 0,8 | 8,4 | 8,12 | 16,12 |
Easy-Moderate | 12,0 | 8,8 | 0,4 | 16,8 | 0,0 |
Easy | 0,8 | 16,4 | 0,12 | 8,8 | 0,0 |
CAT 2019 | CAT 2018 | CAT 2017 | |
Percentile | Score | ||
99th Percentile | 52 | 46 | 45 |
98th Percentile | 48 | 41 | 40 |
95th Percentile | 39 | 35 | 33 |
90th Percentile | 32 | 29 | 28 |
CAT 2019 | CAT 2018 | CAT 2017 | CAT 2016 | CAT 2015* | |
Arrangements | 13% | 19% | 25% | 0% | 13% |
Grouping & Conditionality | 6% | 6% | 0% | 6% | |
Games & Tournaments | 13% | 0% | 0% | 0% | 0% |
Puzzles | 19% | 19% | 25% | 44% | 38% |
Scheduling | 6% | 0% | 0% | 0% | 0% |
Miscellaneous | 6% | 6% | 0% | 0% | 0% |
DI - Reasoning Based | 19% | 19% | 31% | 25% | 38% |
DI - Calculation Based | 13% | 31% | 19% | 25% | |
DI - Observation Based | 6% | 0% | 0% | 0% | 13% |
*For CAT 2015, topics have been combined due to the nature of questions asked. |
Observation based DI sets are, as the name suggests, sets that can be solved simply by observing presented data. A good example is a DI set based on a scatter graph describing millimetres of rainfall in various locations across various years, followed by questions like "Which year saw the maximum increase in overall rainfall across all 6 regions?", or, "Which of the following pairs of regions correspond to regions with maximum and minimum percentages increase in rainfall from 2016-2018?"
Jitesh Gupta
A California-based travel writer, lover of food, oceans, and nature.