- A
Change the data type of readings to int and multiply all values by 10 to preserve one decimal place, then compare with 300.
Correct. By scaling to integers, floating-point errors are eliminated. The threshold 30.0 becomes 300, and any reading whose scaled integer value is greater than 300 is strictly above 30.0.
- B
Change the condition to (readings[i] - 30.0) > Double.MIN_VALUE.
Why wrong: Wrong. The condition (readings[i] - 30.0) > Double.MIN_VALUE is functionally equivalent to readings[i] > 30.0 because any positive double is larger than Double.MIN_VALUE. It does not provide a meaningful tolerance and fails to address floating-point imprecision.
- C
Change the condition to Math.abs(readings[i] - 30.0) > 0.0.
Why wrong: Wrong. Math.abs(diff) > 0.0 is true for any nonzero difference, including negative differences that arise from readings slightly below 30.0 due to imprecision. This would incorrectly count such readings.
- D
Change the threshold literal to 30.0f (float) and compare using Float.compare(readings[i], threshold) > 0.
Why wrong: Wrong. Converting to float may introduce additional rounding errors and does not reliably fix the imprecision. Float.compare only compares the numeric values, so a reading that is exactly 30.0 in double may still compare as equal to the float threshold, but the original issue of readings being slightly above 30.0 due to double imprecision remains.
1Z0-811 Floating-point imprecision Practice Question
This 1Z0-811 practice question tests your understanding of java basics and syntax. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. A key principle to apply: floating-point imprecision. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
You are tasked with debugging a Java application that processes temperature sensor readings. The application reads an array of double values representing temperatures in Celsius, and should output the number of readings that exceed a threshold of 30.0°C. The current implementation is:
public class TemperatureAnalyzer {
public static void main(String[] args) {double[] readings = {25.5, 32.1, 30.0, 28.9, 35.7};
int count = 0;
for (int i = 0; i < readings.length; i++) {
if (readings[i] > 30.0) {count++;
}
}System.out.println("Count: " + count);
} }
However, the output is "Count: 3" instead of the expected 2 (since 30.0 is not above the threshold). The developer believes the issue is that the condition should be '>=' to include 30.0, but the specification says strictly above 30.0. After further investigation, you discover that the problem is due to floating-point imprecision when comparing with the literal 30.0. Which of the following changes would correctly fix the comparison to reliably count readings strictly greater than 30.0, considering floating-point representation?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Change the data type of readings to int and multiply all values by 10 to preserve one decimal place, then compare with 300.
Option A is correct because it avoids floating-point imprecision entirely by using integer arithmetic. Multiplying the readings by 10 and converting to int preserves one decimal place exactly, allowing a direct comparison with 300 (which corresponds to 30.0). This ensures that only readings strictly greater than 30.0 are counted, without risk of tiny rounding errors affecting the result.
Key principle: Floating-point imprecision
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Change the data type of readings to int and multiply all values by 10 to preserve one decimal place, then compare with 300.
Why this is correct
Correct. By scaling to integers, floating-point errors are eliminated. The threshold 30.0 becomes 300, and any reading whose scaled integer value is greater than 300 is strictly above 30.0.
Related concept
Floating-point imprecision
- ✗
Change the condition to (readings[i] - 30.0) > Double.MIN_VALUE.
Why it's wrong here
Wrong. The condition (readings[i] - 30.0) > Double.MIN_VALUE is functionally equivalent to readings[i] > 30.0 because any positive double is larger than Double.MIN_VALUE. It does not provide a meaningful tolerance and fails to address floating-point imprecision.
- ✗
Change the condition to Math.abs(readings[i] - 30.0) > 0.0.
Why it's wrong here
Wrong. Math.abs(diff) > 0.0 is true for any nonzero difference, including negative differences that arise from readings slightly below 30.0 due to imprecision. This would incorrectly count such readings.
- ✗
Change the threshold literal to 30.0f (float) and compare using Float.compare(readings[i], threshold) > 0.
Why it's wrong here
Wrong. Converting to float may introduce additional rounding errors and does not reliably fix the imprecision. Float.compare only compares the numeric values, so a reading that is exactly 30.0 in double may still compare as equal to the float threshold, but the original issue of readings being slightly above 30.0 due to double imprecision remains.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap is that Double.MIN_VALUE seems like a small epsilon, but it's actually the smallest positive number, so any positive difference exceeds it, making the condition equivalent to >. Candidates mistakenly believe this handles imprecision, but it does not provide a meaningful tolerance.
Detailed technical explanation
How to think about this question
Floating-point numbers in Java (IEEE 754 standard) cannot represent all decimal values exactly; for example, 30.0 is exactly representable, but calculations like `32.1 - 30.0` may yield a tiny rounding error (e.g., 2.0999999999999977). Using `Double.MIN_VALUE` as a tolerance ensures that only truly positive differences are counted, while ignoring negligible negative errors that could arise from arithmetic. In real-world sensor data, repeated arithmetic operations can accumulate errors, making such epsilon-based comparisons critical for reliability.
KKey Concepts to Remember
- Floating-point imprecision
- Fixed-point arithmetic
- Epsilon comparison
- Integer scaling
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Floating-point imprecision
Real-world example
How this comes up in practice
A practitioner preparing for the 1Z0-811 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Floating-point imprecision Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
Review floating-point imprecision, then practise related 1Z0-811 questions on the same topic to reinforce the concept.
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FAQ
Questions learners often ask
What does this 1Z0-811 question test?
Java Basics and Syntax — This question tests Java Basics and Syntax — Floating-point imprecision.
What is the correct answer to this question?
The correct answer is: Change the data type of readings to int and multiply all values by 10 to preserve one decimal place, then compare with 300. — Option A is correct because it avoids floating-point imprecision entirely by using integer arithmetic. Multiplying the readings by 10 and converting to int preserves one decimal place exactly, allowing a direct comparison with 300 (which corresponds to 30.0). This ensures that only readings strictly greater than 30.0 are counted, without risk of tiny rounding errors affecting the result.
What should I do if I get this 1Z0-811 question wrong?
Review floating-point imprecision, then practise related 1Z0-811 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Floating-point imprecision
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Last reviewed: Jun 25, 2026
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