C Floating Point Issues Questions with Answers | C Programming MCQ | Page 3

Handling floating-point numbers in C can be tricky due to precision limits, rounding errors, and data representation issues. This section on C Floating Point Issues questions with answers is designed to help you master these concepts effectively. In interviews and placement tests by Infosys, TCS, and Wipro, such questions are commonly asked to test your understanding of floating-point arithmetic and memory storage. Practicing C programming MCQs on this topic will improve your accuracy in calculations and code debugging. Each question includes detailed reasoning to clarify why floating-point comparisons or conversions behave unexpectedly. Strengthen your programming foundation and get ready for technical assessments with this comprehensive guide.

Showing 10 of 28 questions

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Q21

Which technique helps reduce floating-point errors in summation operations?

A Bubble sort algorithm
B Kahan summation algorithm
C Binary search
D Quick sort algorithm

Answer: Option B

Explanation: Kahan summation uses compensation to track and correct lost low-order bits during addition.

Q22

What is the "unit in the last place" (ULP) error?

A Error in the first digit
B Spacing between floating-point numbers
C Memory allocation error
D Compiler optimization issue

Answer: Option B

Explanation: ULP measures the spacing between floating-point numbers, representing the worst-case rounding error.

Q23

Which of these values represents positive infinity in IEEE 754?

A 0
B 1
C Maximum finite value
D A special bit pattern

Answer: Option D

Explanation: Positive infinity is represented by a specific bit pattern with all exponent bits set to 1 and mantissa to 0.

Q24

What is "gradual underflow" in floating-point systems?

A Immediate zero assignment for small numbers
B Smooth transition using denormalized numbers
C Rounding all small numbers to nearest integer
D Using integers instead of floats

Answer: Option B

Explanation: Gradual underflow uses denormalized numbers to provide a smooth transition to zero.

Q25

Which operation is generally safe from catastrophic cancellation?

A a - b where a ≈ b
B a + b where a ≈ -b
C a * b where a ≈ b
D (a + b) - a where b is small

Answer: Option C

Explanation: Multiplication of numbers with similar magnitudes doesn't suffer from the same cancellation issues as subtraction.

Q26

What does the "sticky bit" help with in floating-point rounding?

A Detecting overflow
B Improving rounding accuracy
C Speeding up division
D Handling NaN values

Answer: Option B

Explanation: The sticky bit preserves information about trailing bits beyond what can be represented, improving rounding accuracy.

Q27

In floating-point comparison, why should direct equality checks be avoided?

A They are too slow
B They consume more memory
C Rounding errors make exact equality unlikely
D They cause compiler errors

Answer: Option C

Explanation: Direct equality fails due to accumulated rounding errors making exactly equal results unlikely.

Q28

What is the "hidden bit" in IEEE 754 normalized numbers?

A A bit that is always zero
B An implied leading 1 in mantissa
C The sign bit
D The least significant bit

Answer: Option B

Explanation: The hidden bit is an implied leading 1 in the mantissa that isn't stored explicitly, providing extra precision.

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