* Azure Metrics Advisor builds on Anomaly Detector but adds root cause analysis, correlation across multiple signals, incident detection, and alerting out-of-the-box.
* D. Anomaly Detector # Works for single-signal anomaly detection but lacks correlation and root cause analysis.
* C. Azure Machine Learning # Would work but requires custom model development (more dev effort).
* B. Form Recognizer # Used for document processing, not sensor telemetry.
The answer: A
Reference: Azure Metrics Advisor overview

Explanation:

To create a resource that can generate fictional stories from prompts, you need an Azure OpenAI account (-- kind OpenAI).
To protect data with a customer-managed key (CMK) , you must pass the key information (Key Vault URI, key name, key version) via the --encryption parameter when creating the Cognitive Services account. The JSON shown (with keyName, keyVersion, and keyVaultUri) is exactly the payload expected by --encryption.
A complete example pattern:
az cognitiveservices account create \
-n myresource -g myResourceGroup -l westeurope \
--kind OpenAI --sku S \
--assign-identity \
--encryption ' {
" keyName " : " keyname " ,
" keyVersion " : " secretVersion " ,
" keyVaultUri " : " https:// < your-kv > .vault.azure.net/ "
} '
(Adding --assign-identity allows the service to access the Key Vault key; the hotspot specifically asks for the parameter that takes the JSON block and the resource kind, which are --encryption and OpenAI.) Microsoft References Azure CLI: Create Cognitive Services account with customer-managed keys using --encryption.
https://learn.microsoft.com/azure/ai-services/cognitive-services-encryption-keys#configure-customer- managed-keys Azure Cognitive Services (Azure AI services) CLI parameters including --kind OpenAI .
https://learn.microsoft.com/azure/ai-services/cognitive-services-management-api-cli#create-a-cognitive- services-account

Scenario Recap:
* Application: Flower classification.
* Solution: Add new images and labels to the existing model. Retrain and publish the model.
Analysis:
* This is the best practice workflow:
* Add new labeled training images.
* Retrain the model with updated dataset.
* Publish the retrained model.
* This clearly meets the goal.
The answer: A. Yes

You need to detect PPE compliance (masks or safety glasses) on a periodic schedule (every 15 minutes) while minimizing development effort and cost.
Why Computer Vision (Azure AI Vision)
Azure AI Vision (formerly "Computer Vision") lets you analyze still images with prebuilt Image Analysis and-when needed-train a lightweight custom object detection model directly in Vision Studio without building ML pipelines. You can simply grab a frame (snapshot) from your camera feed every 15 minutes and call the Vision API-this keeps costs low because you pay per image analyzed rather than continuously processing video. Microsoft's current Vision stack supports image analysis and custom object detection via Vision Studio and v4.0 APIs, designed for scenarios like PPE detection. Microsoft Azure +1 Why not Face The Face service is intended for face detection/recognition workflows. While Face can return some accessory attributes (e.g., glasses/mask confidence), Microsoft has scaled back several face attributes under Responsible AI guidance, and Face is not positioned as a general PPE-compliance detector across a scene (multiple people, full body PPE). It's therefore a poorer fit and can increase false decisions for PPE checks beyond the face region. Azure Docs +3 Why not Azure Video Analyzer for Media (Video Indexer) Video Indexer is optimized for extracting insights from full videos (speech, people, labels, brands). Running it just to check PPE every 15 minutes is overkill and more expensive than analyzing a single still frame with Vision. In addition, Microsoft has been adjusting video services post-Azure Media Services retirement; using snapshot + Vision avoids those complexities and keeps costs minimal.
How this meets the requirements
Identify masks/safety glasses: Use Image Analysis or a quick custom object detector in Vision Studio trained on a small set of PPE images. Microsoft Learn Every 15 minutes: Capture a single frame from the camera and call the Vision endpoint-simple scheduling with a timer-triggered Function or Logic App, minimal code. (General Azure pattern; Vision API is stateless per image.) Microsoft Azure Minimize development effort: Vision Studio provides no-code/low-code training and testing for custom detectors if the prebuilt model isn't sufficient. Microsoft Learn Minimize costs: Pay-per-call image analysis is cheaper than indexing entire video segments; no continuous streaming required. Microsoft Azure The Answer: B. Computer Vision Microsoft References Azure AI Vision (product overview): image and video analysis with OCR, object detection, Image Analysis.
Microsoft Azure
What's new in Azure AI Vision: custom image classification/object detection in Vision Studio and v4.0 APIs.
Microsoft Learn
Face detection & attributes (accessories including glasses and mask)-not the right tool for scene-level PPE compliance. Microsoft Learn Face Detect REST API (attributes list, including glasses/mask). Microsoft Learn Responsible AI updates indicating changes to facial analysis attributes; facial detection/accessories remain but are limited in scope. Azure Docs Azure AI Video Indexer release notes/updates (video-centric use cases, ongoing adjustments post-AMS retirement).

Comprehensive Detailed Explanation
The method shown uses the Read API of the Azure Computer Vision client library. The ReadAsync call submits the OCR job and returns an Operation-Location header, which contains the operationId. Then GetReadResultAsync(Guid.Parse(operationId)) is called to fetch the result.
The Issue
The Read API is asynchronous.
Submitting the read request does not mean the OCR has finished.
If you immediately call GetReadResultAsync , the operation may still be in progress, resulting in incomplete or empty results.
Required Fixes
Check the results.Status property
The response from GetReadResultAsync includes a status property.
Possible values: notStarted , running , succeeded , failed .
You must wait until the status is succeeded before processing results.
This ensures you don't attempt to read lines before OCR has completed.
This corresponds to B.
Wrap in a loop with delay
Since OCR takes time, you need to poll the service until the status becomes succeeded or failed .
A typical pattern is:
ReadOperationResult results;
do
{
results = await client.GetReadResultAsync(Guid.Parse(operationId));
await Task.Delay( 1000 ); // wait 1 second before retrying
}
while (results.Status == OperationStatusCodes.Running || results.Status == OperationStatusCodes.NotStarted); This corresponds to D.
Option Analysis
A). Remove the Guid.Parse(operationId) parameter
Incorrect. The operationId is required to fetch the OCR results.
B). Add code to verify the results.Status value
Correct. You must check whether the operation has completed.
C). Add code to verify the status of the txtHeaders.Status value
Incorrect. txtHeaders only contains metadata and the operation location. The OCR completion status comes from the results.Status in the GetReadResultAsync response, not from txtHeaders .
D). Wrap the call to GetReadResultAsync within a loop that contains a delay Correct. This is how you poll until the operation has finished.
The Answer: B and D
Microsoft References
Azure Computer Vision Read API
Read text using the Computer Vision client library