Quick Answer
The answer is Expected Calibration Error (ECE). This metric directly measures model overconfidence by quantifying the alignment between a model’s predicted confidence scores and its actual accuracy across different confidence bins. When a model assigns high confidence to factually incorrect answers, it indicates miscalibration, and ECE captures this mismatch by computing the average absolute difference between accuracy and confidence per bin. On the Google Cloud Generative AI Leader exam, this question tests your understanding of calibration as a critical evaluation dimension for LLMs in production, often appearing alongside metrics like BLEU or ROUGE but specifically targeting trustworthiness. A common trap is confusing ECE with accuracy or precision, which don’t assess confidence alignment. Remember the memory tip: “ECE equals error in confidence estimation”—if your model is overconfident, ECE will be high, signaling a need for recalibration.
This Generative AI Leader practice question tests your understanding of fundamentals of generative ai. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. 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.
A team is tuning a large language model for a question-answering task. They notice the model gives high confidence scores to answers that are factually incorrect. Which evaluation metric should they primarily use to detect this overconfidence problem?
Perplexity
Why wrong: Perplexity indicates model surprise, not confidence in correctness.
Expected Calibration Error (ECE)
ECE directly quantifies how well confidence scores reflect actual correctness.
BLEU score
Why wrong: BLEU evaluates text similarity, not confidence calibration.
ROUGE-L
Why wrong: ROUGE-L measures recall of longest common subsequence, not calibration.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Expected Calibration Error (ECE)
Expected Calibration Error (ECE) directly measures the alignment between a model's predicted confidence and its actual accuracy. In this scenario, high confidence on incorrect answers indicates miscalibration, and ECE quantifies this mismatch by binning predictions by confidence and computing the average absolute difference between accuracy and confidence per bin.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Perplexity
Why it's wrong here
Perplexity indicates model surprise, not confidence in correctness.
Expected Calibration Error (ECE)
Why this is correct
ECE directly quantifies how well confidence scores reflect actual correctness.
Related concept
Read the scenario before looking for a memorised answer.
BLEU score
Why it's wrong here
BLEU evaluates text similarity, not confidence calibration.
ROUGE-L
Why it's wrong here
ROUGE-L measures recall of longest common subsequence, not calibration.
Common exam traps
Google Cloud often tests the distinction between intrinsic evaluation metrics (like perplexity) and calibration metrics, leading candidates to mistakenly choose perplexity when the core issue is confidence miscalibration rather than general model uncertainty.
Trap categories for this question
Similar concept trap
BLEU evaluates text similarity, not confidence calibration.
Detailed technical explanation
ECE is computed by partitioning predictions into M equally-spaced confidence bins (e.g., 10 bins of width 0.1), then for each bin calculating |accuracy(bin) - confidence(bin)|, weighted by the number of samples in the bin. In practice, a well-calibrated model should have accuracy close to confidence across all bins; a high ECE (e.g., >0.1) signals overconfidence, often addressed by techniques like temperature scaling or Platt scaling during post-hoc calibration.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Fundamentals of Generative AI — This question tests Fundamentals of Generative AI — Read the scenario before looking for a memorised answer..
The correct answer is: Expected Calibration Error (ECE) — Expected Calibration Error (ECE) directly measures the alignment between a model's predicted confidence and its actual accuracy. In this scenario, high confidence on incorrect answers indicates miscalibration, and ECE quantifies this mismatch by binning predictions by confidence and computing the average absolute difference between accuracy and confidence per bin.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
This Generative AI Leader practice question is part of Courseiva's free Google Cloud 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 Generative AI Leader exam.
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