AI0-001 AI Implementation and Operations Practice Question
Exhibit
Refer to the exhibit.
```
2024-09-17 10:15:23 ERROR Model inference failed: ValueError: Input contains NaN, infinity or a value too large for dtype('float64').
2024-09-17 10:15:23 WARNING Preprocessing step 'normalize' received missing values for feature 'age'.
2024-09-17 10:15:23 INFO Current input row: {'age': nan, 'income': 50000, 'score': 0.75}
2024-09-17 10:15:23 ERROR Batch processing halted after 1000 successful rows.
```Refer to the exhibit. A batch inference job fails with the given logs. What is the most likely root cause of the failure?
⚠ Common exam trap
CompTIA often tests the distinction between data quality issues (missing values) and model compatibility issues (version mismatches or feature encoding), so candidates may incorrectly choose option D because they assume a version mismatch is the cause, when the logs clearly point to a preprocessing failure.
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
✓
The input data contains missing values that are not handled in preprocessing
The logs indicate a 'ValueError' or similar exception when the batch inference job attempts to process the input data. This error typically arises when the preprocessing pipeline encounters missing values (e.g., NaN or None) that it cannot handle, causing the job to fail. Option B is correct because missing values not handled in preprocessing are a common root cause for such failures, especially when the training data had no missing values but the inference data does.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The input data has values that exceed the model's expected range
Why it's wrong here
The error is about NaN, not values too large.
- ✓
The input data contains missing values that are not handled in preprocessing
Why this is correct
The log clearly shows a NaN value for 'age' causing an error in normalization.
- ✗
The model was not trained to handle categorical features
Why it's wrong here
The error is about NaN, not categorical encoding.
- ✗
The model version is outdated and incompatible with the current preprocessing pipeline
Why it's wrong here
There is no indication of version mismatch.
About these practice questions
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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.