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2.1 [GATE 1994 : 1 Mark] Question:State True or False with reason:There is always a decomposition into Boyce-Codd Normal Form (BCNF) that is lossless and dependency preserving. Answer: False solution:BCNF में हमेशा Lossless decomposition मिल जाता है, लेकिन Dependency Preserving हर बार नहीं मिलता। Example: R(A,B,C) with AB->C and C->A. ये 3NF में है लेकिन

2.25 [2007: 2 Marks] – Handle in LR Parsing Question: Which of the following describes a handle (as applicable to LR-parsing) appropriately?(a) It is the position in a sentential form where the next shift or reduce operation will occur(b) It is a non-terminal whose production will be used for reduction in the next step(c) It

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3.1 [1994: 1 Mark]Question: Why is intermediate code for an abstract machine preferred over direct machine code?(a) To make lexical analysis easier(b) To make syntax analysis easier(c) To enhance portability of front-end of compiler(d) To improve error recovery Answer: (c) it enhances the portability of the front end of the compiler Explanation: Intermediate code front-end

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Q 31. [GATE 2026 Set-1 : 2M]Consider the following program snippet. Assume that the program compiles and runs successfully. Further, assume that the fork() system call is always successful in creating a process. int main() {int i;for(i=0; i<3; i++){if(fork() == 0){continue;}break;}printf(“Hello!”);return 0;} The total number of times that the print statement gets executed is—-(answer in

Q 11[GATE 2023 – MSQ]Which algorithms can cause starvation?(a) FIFO (b) RR (c) Priority (d) SJFAns: (c) Priority and (d) SJFReason: FIFO and RR are starvation free. Priority me low priority wala kabhi execute nahi ho sakta agar high priority wale aate rahe. SJF bhi ek tarah ka priority hai isliye usme bhi starvation hota

PART 1: NUMPY – “Python का Calculator + Turbo Engine” Python की normal list धीमी होती है। Numpy array 100x तेज़ है क्योंकि ये C language में बना है। 1.Numpy क्या है? Numpy क्या है? बड़े-बड़े numbers, Matrix, Scientific calculation के लिए। Data Science की नींव। Install: pip install numpy 2.Numpy Array बनाना import numpy

12.1 Problem: 100 Cr Row पर Normal SQL क्यों Fail होता है? तुम्हारा Laptop वाला MySQL: SELECT COUNT(*) FROM orders WHERE order_date = ‘2026-07-01’; 100 Cr row पर = 40 मिनट लगेगा 😵 Reason: 1.Single Machine: 1 CPU, 16GB RAM 2.Full Table Scan: हर row पढ़ेगा 3.No Parallelism: 1 काम एक बार में Solution: BigQuery

11.1 Feature Engineering क्या है और SQL क्यों? Machine Learning model को सिर्फ कच्चा data नहीं चाहिए। उसे “Features” चाहिए।Example:कच्चा Data: order_id, customer_id, order_date, amountFeature: last_7_day_spend, avg_order_value, days_since_last_order ML में 80% टाइम Feature Engineering में जाता है। और 90% company में ये काम SQL से ही होता है क्योंकि data पहले से Data Warehouse में