Question 1mediummultiple choice
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Complete AI-102 Implement computer vision solutions question bank — all 0 questions with answers and detailed explanations.
Refer to the exhibit.
```json
{
"url": "https://example.com/image.jpg",
"maxCandidates": 1,
"language": "en"
}
```
Response:
```json
{
"captionResult": {
"text": "a person holding a smartphone",
"confidence": 0.89
},
"metadata": {
"height": 600,
"width": 800
},
"modelVersion": "2024-02-01"
}
```Refer to the exhibit.
{
"url": "https://example.com/image.jpg",
"features": [
"tags",
"objects",
"caption"
],
"language": "en",
"model-version": "latest",
"gender-neutral-caption": true
}Refer to the exhibit.
{
"$schema": "https://schema.management.azure.com/schemas/2019-04-01/deploymentTemplate.json#",
"contentVersion": "1.0.0.0",
"resources": [
{
"type": "Microsoft.CognitiveServices/accounts",
"apiVersion": "2023-05-01",
"name": "MyVisionService",
"location": "[resourceGroup().location]",
"sku": {
"name": "S1"
},
"kind": "ComputerVision",
"properties": {
"customSubDomainName": "myvision",
"networkAcls": {
"defaultAction": "Deny"
}
}
}
]
}{
"version": "1.0",
"recordings": [
{
"recordingId": "rec123",
"sourceLanguage": "en-US",
"targetLanguages": ["fr-FR", "de-DE"],
"insightsToExtract": ["ocr", "faces", "labels"]
}
]
}{
"kind": "CognitiveServices",
"sku": {
"name": "S0"
},
"properties": {
"customSubDomainName": "myvision"
}
}Refer to the exhibit.
```json
{
"url": "https://example.com/image.jpg",
"visualFeatures": ["Categories", "Tags", "Description", "Faces", "ImageType", "Color", "Adult"],
"details": ["Celebrities", "Landmarks"],
"language": "en"
}
```Refer to the exhibit.
```json
{
"name": "MyCustomVisionProject",
"description": "Project for logo detection",
"domainId": "0732100f-1a38-4e49-a514-c9b44d6978f0",
"classificationType": "Multilabel",
"targetExportPlatforms": ["DockerFile", "TensorFlow"]
}
```Refer to the exhibit.
```json
{
"source": "/subscriptions/{subscription-id}/resourceGroups/{resource-group}/providers/Microsoft.MachineLearningServices/workspaces/{workspace-name}",
"properties": {
"model": {
"name": "logo-detector",
"version": 1,
"type": "customvision"
},
"endpoint": {
"name": "logo-endpoint",
"authMode": "Key",
"properties": {
"compute": "CPU",
"scoringTimeout": "PT5M"
}
}
}
}
```{
"cognitiveServices": {
"vision": {
"analyze": {
"visualFeatures": ["Categories", "Tags", "Description", "Objects", "Brands"],
"details": ["Celebrities", "Landmarks"],
"language": "en",
"modelVersion": "latest"
}
}
}
}{
"customvision": {
"project": {
"name": "DefectDetection",
"type": "ObjectDetection",
"domain": "General",
"exportable": true
},
"training": {
"iteration": {
"name": "Iteration 5",
"publishName": "defect-model",
"status": "Completed",
"performance": {
"precision": 0.85,
"recall": 0.78,
"mAP": 0.82
}
}
}
}
}{
"face": {
"apiVersion": "2024-02-01",
"parameters": {
"returnFaceId": true,
"returnFaceLandmarks": false,
"returnFaceAttributes": ["age", "emotion", "hair"]
}
}
}You are a senior AI engineer at a manufacturing company. The company has a production line that uses cameras to capture images of assembled products. The current system uses a set of rule-based heuristics to detect defects, but it has high false-positive rates. You have been tasked to design a new computer vision solution using Microsoft Azure AI services. The solution must:
- Detect defects such as scratches, dents, and misalignments in real-time as products move on the conveyor belt (frame rate of 30 fps). - Support continuous learning: when a new defect type is discovered, the model should be updated without retraining the entire model from scratch. - Operate with low latency (<100 ms per inference) to keep up with the production speed. - Use only fully managed services (no custom containers or edge devices). - The factory network has limited internet bandwidth, so the solution must minimize data transfer.
Which approach should you recommend?
Refer to the exhibit.
{
"url": "https://example.com/image.jpg",
"features": [
"description",
"tags",
"objects",
"brands"
],
"language": "en",
"model-version": "latest",
"smartcrops-aspect-ratios": [1.0, 1.5]
}