AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✗

  Scheduling regular maintenance based on a fixed calendar without using any AI

  Why it's wrong here

  Fixed-calendar maintenance is a preventive maintenance strategy—service occurs at scheduled intervals (e.g., every 90 days) regardless of the asset's actual condition, so machines can fail before their scheduled check or be serviced while still healthy. Predictive maintenance is fundamentally different because it uses AI algorithms on real-time sensor streams to estimate remaining useful life and trigger maintenance only when a failure is imminent. The absence of AI means there is no data-driven prediction, so this option is the opposite of what the question describes.

• ✓

  Using AI to predict equipment failures before they occur, enabling timely maintenance

  Why this is correct

  Predictive maintenance is the correct AI use case because it analyzes sensor data from physical equipment (vibration, temperature, acoustics) to identify patterns that precede failure. Machine learning models trained on historical failure data can flag anomaly signatures early, letting maintenance teams intervene just before a breakdown occurs. This shifts maintenance from reactive or calendar-based timing to condition-based, data-driven timing, minimizing unplanned downtime and avoiding unnecessary part replacements.

• ✗

  Maintaining an AI model's accuracy by regularly retraining on new data

  Why it's wrong here

  Retraining an AI model on new data is a legitimate machine learning practice, but it belongs to the MLOps or model-lifecycle domain—it manages model drift and keeps predictions current. Predictive maintenance, by contrast, is specifically about forecasting the failure of physical industrial assets, not about maintaining the AI system itself. The two are complementary but different: one addresses software model health, the other addresses machinery health.

• ✗

  Using AI to automatically fix bugs in software systems without human intervention

  Why it's wrong here

  Automatically fixing bugs in software is an application of automated program repair or self-healing code, which uses techniques like static analysis, symbolic execution, or reinforcement learning to generate patches. That scenario involves digital artifacts (source code, binaries), whereas predictive maintenance targets physical equipment and industrial machinery such as motors, pumps, and conveyor belts. The goal of predictive maintenance is to foresee and mitigate mechanical failure, not to repair software defects without a human in the loop.

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