PCA Promql Practice Question
Which function evaluates the predictable linear extrapolation of a gauge metric over time to predict future values?
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
✓
predict_linear(node_filesystem_free_bytes[1h], 4 * 3600)
The predict_linear() function predicts the value of a gauge metric 't' seconds into the future based on a range vector using simple linear regression.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
predict_linear(node_filesystem_free_bytes[1h], 4 * 3600)
Why this is correct
Correct. predict_linear uses linear regression on a range vector to predict values in the future.
- ✗
holt_winters(node_filesystem_free_bytes[1h], 5m, 1m)
Why it's wrong here
holt_winters produces a smoothed time series based on exponential smoothing, not direct linear prediction.
- ✗
deriv(node_filesystem_free_bytes[1h])
Why it's wrong here
deriv calculates the per-second derivative, but does not accept a future time offset parameter.
- ✗
extrapolate_linear(node_filesystem_free_bytes[1h], 4 * 3600)
Why it's wrong here
extrapolate_linear is not a valid PromQL function name.
About these practice questions
One of 304 original PCA practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed August 2026 · checked against the official CNCF / Linux Foundation exam blueprint
This PCA practice question is part of Courseiva's free CNCF / Linux Foundation certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PCA exam.