AI-900 Practice Question: Describe features of computer vision workloads on Azure
What is 'Azure Custom Vision's training iterations' and why would you train multiple iterations?
⚠ Common exam trap
It's easy for candidates to confuse 'iteration' with a technical term like 'epoch' or 'inference pass,' when in Custom Vision it specifically means a versioned training run that can be compared and published.
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
✓
Versioned training runs — each iteration trains on all current tagged images and can be compared and published
In Azure Custom Vision, a training iteration is a versioned model produced by training on the current set of tagged images. Each iteration captures the model's learned patterns at a specific point in time. Training multiple iterations allows you to compare performance across different hyperparameters, data splits, or image sets, then publish the best-performing iteration to a prediction endpoint for production use.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Iterations represent attempts to upload images before one succeeds due to network issues
Why it's wrong here
Upload retries are transient network-level actions handled by the client SDK and storage service; a failed or retried image upload does not create any Custom Vision iteration. An iteration exists only after an explicit training run, which ingests all successfully uploaded and tagged images at that moment. Network upload attempts never appear as model versions.
- ✓
Versioned training runs — each iteration trains on all current tagged images and can be compared and published
Why this is correct
In Azure Custom Vision, an iteration is a versioned model artifact produced each time you click Train. Each iteration is trained on the entire current set of tagged images, so it reflects whatever image and label changes you have made since the previous training run. You can compare iterations on classification accuracy metrics (precision, recall, and mean Average Precision) and publish the best one to the prediction endpoint.
- ✗
The number of times the model scans the same image for different object types
Why it's wrong here
Multi-class detection or multi-label classification processes an input image once and returns all bounding boxes or class labels for that image; the model does not rescan the same image multiple times per object type. Iterations, in contrast, are complete training runs that produce a new model version and are unrelated to the number of object types an image may contain.
- ✗
A pricing unit where each API call consumes one iteration from your monthly quota
Why it's wrong here
Azure Custom Vision pricing meters prediction API calls (transactions) and, for some methods, compute time during training; it never deducts 'iterations' from a monthly quota. An iteration is a model snapshot, not a billing token, and publishing a model does not consume quota. You can create unlimited iterations within your training compute limits without affecting prediction billing.
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Key term
Model
In IT and AI, a model is a trained mathematical representation that learns patterns from data to make predictions or decisions.
Key term
Prediction
Prediction is the process of using data and algorithms to forecast future outcomes or identify patterns without explicit programming for each scenario.
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