ViTrox Quiz

*1. To activate the SMART auto judgement feature, what specific performance metrics must the A.I. maintain for a duration of one month?

Accuracy ≥ 70% and Recall ≥ 100%

Accuracy ≥ 90% and Recall ≥ 70%

Accuracy ≥ 70% and Escape Rate ≤ 0.1%

Accuracy ≥ 95% and Precision ≥ 90%

*2. What is a primary advantage of the ViTrox A.I. Layered Workflow compared to Conventional Classification A.I.?

It provides strong zero-shot generalization for untrained scenarios.

It reduces workflow complexity for easier implementation.

It results in lower false calls and escapees through sequential model stages.

It requires no hyperparameter tuning or constant retraining.

*3. Which hardware component serves as the centralized manager for configuring part numbers and monitoring real-time A.I. status across multiple stations?

AI Training Platform

AI Control Centre

VDSPC (AI Analysis Tool)

VVTS Workstation

*4. What is the primary difference between Maintenance Option A and Maintenance Option B for the AOI VVTS AI model?

Option A is a yearly subscription, while Option B is a one-time investment for self-maintenance.

Option A includes hardware, whereas Option B is a software-only license.

Option A supports multiple lines, while Option B is limited to a single VVTS line.

Option A requires on-premise training, while Option B utilizes cloud-based training.

*5. During the 'A.I. Training' stage, what is the purpose of the 'Body Box' mentioned in the source material?

It is a diagnostic tool for measuring the heat output of the GPU.

It is a physical safety enclosure used on the factory floor.

It is a label drawn on component images to define the area for model training.

It is an automated threshold adjustment for the inspection camera.

*6. Which implementation phase is characterized by the adjustment of thresholds and A.I. Inferencer settings to reduce human error and cost by approximately 70%?

Phase 2: Monitoring

Phase 1: Integration

Phase 3: Fine Tuning

Phase 5: Optimisation

*7. What is a documented disadvantage of using a Large Language Model (LLM A.I.) for defect analysis according to the A.I. Modelling comparison?

It cannot handle complex failure analysis or generate human-readable reports.

It is less flexible and lacks configurability compared to other models.

It is prone to frequent 'untrained' scenario failures requiring constant retraining.

It requires high computing power and suffers from slow inference speeds.

*8. For the High-Tier Server specified for the AI Training Platform, what is the GPU configuration?

NVIDIA GeForce RTX 5060 (8 GB)

Dual NVIDIA RTX 3080 (10 GB)

NVIDIA RTX Pro 4000 Blackwell (24 GB)

NVIDIA L4 (24 GB)

*9. What does the 'A.I. Impact' metric represent in the 'Success Stories' section of the report?

The speed increase of the SMT line conveyor system.

The reduction in manual effort and operator resource allocation.

The total number of defects caught by the system per hour.

The percentage of the circuit board that the camera is capable of seeing.

*10. In the 'Defect Mapping' use case, how does the user resolve an A.I. escapee where a 'bridging' defect was missed because the AOI failed 'Right Offset'?

By deleting the Right Offset template from the central database.

By training a new LLM model to replace the existing classification logic.

By increasing the camera resolution on the 3D AOI machine.

By adding a conditional mapping in the AI Control Centre to check bridging even if Right Offset fails.