AI0-001 AI Implementation and Operations Practice Question
An AI operations team is monitoring a deployed image classification model. They notice a gradual increase in prediction confidence but a drop in accuracy. Which THREE actions should they take to diagnose the issue?
⚠ Common exam trap
CompTIA often tests the distinction between diagnostic actions and corrective actions—candidates mistakenly jump to retraining (Option E) or data collection (Option B) instead of first analyzing calibration and data distribution (Options A, C, D) to identify the specific type of drift or miscalibration.
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
✓
Analyze the model's calibration curve to see if confidence scores align with actual accuracy.
A calibration curve (reliability diagram) directly compares predicted confidence scores against actual accuracy. In this scenario, increasing confidence with dropping accuracy indicates miscalibration—the model is becoming overconfident. Analyzing the calibration curve reveals whether the confidence scores systematically deviate from true probabilities, which is the core diagnostic step for this specific symptom.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Analyze the model's calibration curve to see if confidence scores align with actual accuracy.
Why this is correct
Calibration analysis reveals if model is overconfident due to drift.
- ✗
Increase the size of the training dataset by collecting more unlabeled data.
Why it's wrong here
More unlabeled data does not help without labels; it may even worsen drift if distribution differs.
- ✓
Compare the distribution of input features between training and recent production data.
Why this is correct
This detects data drift that could cause confidence miscalibration.
- ✓
Evaluate model performance on a held-out test set collected at deployment time.
Why this is correct
Comparing performance on original test set vs. current data quantifies accuracy drop.
- ✗
Retrain the model immediately with the most recent data.
Why it's wrong here
Retraining without diagnosis may waste resources; first understand the drift.
About these practice questions
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI0-001 practice question is part of Courseiva's free CompTIA 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 AI0-001 exam.