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Linux Foundation PCA Exam - Topic 3 Question 5 Discussion

Actual exam question for Linux Foundation's PCA exam
Question #: 5
Topic #: 3
[All PCA Questions]

Which function would you use to calculate the 95th percentile latency from histogram data?

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Suggested Answer: B

To calculate a percentile (e.g., 95th percentile) from histogram data in Prometheus, the correct function is histogram_quantile(). It estimates quantiles based on cumulative bucket counts.

Example:

histogram_quantile(0.95, sum(rate(http_request_duration_seconds_bucket[5m])) by (le))

This computes the 95th percentile request duration across all observed instances over the last 5 minutes.


by Tamesha at Nov 20, 2025, 05:31 PM

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Joana
11 hours ago
Not sure about B, can you really sum rates like that?
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Cristal
5 days ago
Wait, is there really a function called histogram_quantile?
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Carissa
24 days ago
I thought it was A at first, but B makes more sense.
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Carmen
29 days ago
Haha, Dahlia, you're a riot! But I think I'll stick with the more reliable option B. Can't go wrong with the histogram_quantile function.
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Dahlia
1 month ago
D) topk(0.95, http_request_duration_seconds) is clearly the best choice. Who needs fancy percentile calculations when you can just get the top 95% of the values?
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Raina
1 month ago
I'm going with B as well. The "sum(rate(...))" part is to convert the cumulative histogram into a proper histogram, which is necessary for the quantile calculation.
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Vanesa
1 month ago
Option B looks good, but I'm a bit confused about the "sum(rate(...))" part. Shouldn't we just use the raw histogram data?
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Alayna
2 months ago
I don’t think it’s C or D since they seem more general and not specifically for histograms.
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Linsey
2 months ago
I’m a bit confused between A and B. I know they both deal with quantiles, but I can't recall the specifics of their usage.
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Larae
2 months ago
I remember practicing with histogram data, and I feel like `histogram_quantile` was a key function for calculating percentiles.
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Karan
2 months ago
I think the answer might be B, but I'm not entirely sure how the `sum(rate(...))` part works in this context.
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Jean
2 months ago
I think B is the way to go. The histogram_quantile function looks like it's the best fit for calculating the 95th percentile from the given histogram data.
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Pearline
3 months ago
I'm not sure about this one. The question mentions "histogram data," so I'm thinking maybe B or C could work. I'll have to review the details of each function to decide.
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Helaine
3 months ago
Okay, I'm pretty confident that B is the correct answer here. The histogram_quantile function is specifically designed for calculating percentiles from histogram data, which is exactly what this question is asking for.
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Willow
3 months ago
B is the right choice for histograms!
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Ria
3 months ago
I think B is the right choice.
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Eleonore
3 months ago
B) histogram_quantile(0.95, sum(rate(http_request_duration_seconds_bucket[5m])) by (le)) is the correct answer. It calculates the 95th percentile latency from histogram data.
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Mila
4 months ago
Definitely B, it's the standard way to get percentiles from histograms.
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Cassandra
4 months ago
Hmm, this one's tricky. I'm leaning towards B, but I'm a bit confused about the difference between the quantile_over_time and histogram_quantile functions. I'll have to double-check the documentation on those.
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Georgeanna
4 months ago
I think B is the right answer, but I'm not 100% sure. The histogram_quantile function looks like it might be the way to go for calculating the 95th percentile from the histogram data.
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Caprice
4 months ago
I thought A might work too, but B is more accurate.
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