AI-103 Computer Vision Practice Test 2 — 15 Questions

Question 1

You are integrating Azure AI Vision Read API into a high-throughput workflow. You receive an HTTP 202 Accepted response. What action should your application take next?

Accepted Answer

Poll the URL specified in the Operation-Location response header until the status is succeeded.. An HTTP 202 Accepted response from the Azure AI Vision Read API indicates that the request has been accepted for asynchronous processing, and the response includes an Operation-Location header containing a URL to poll for the result. The application must poll that URL (typically with a GET request) until the status field returns 'succeeded', at which point the extracted text is available in the response body. This is the documented asynchronous pattern for the Read API.

Answer

Switch to synchronous Read API endpoint.

Answer

Parse the response body directly for the extracted text results.

Answer

Immediately retry the POST request with an increased timeout value.

Question 2

You have trained a Custom Vision classification model. You need to export the model to run locally on an edge device using ONNX format. Which project domain must you select when creating the project to support export?

Accepted Answer

General (compact). To export a Custom Vision classification model to ONNX for edge deployment, the project must be created with a compact domain — specifically 'General (compact)' — because only compact domains support model export. The compact domain produces a smaller, optimized model suitable for local/edge inference.

Answer

Retail

Answer

Food (compact) for edge deployment

Answer

General

Question 3

You are developing a .NET application that extracts bounding boxes of text from scanned documents using Azure AI Vision 4.0. Which asynchronous API operation should you call first to initiate the OCR process?

Accepted Answer

POST /computervision/imageanalysis:analyze?api-version=2024-02-01. The Azure AI Vision 4.0 Image Analysis analyze endpoint is POST /computervision/imageanalysis:analyze with api-version=2024-02-01, and it supports the 'read' feature for OCR with bounding boxes. This is the correct modern endpoint for initiating asynchronous OCR in the 4.0 API surface.

Answer

POST /vision/v3.2/ocr

Answer

POST /vision/v2.1/recognizeText

Answer

POST /customvision/v3.3/Prediction

Question 4

Your company wants to generate descriptive captions for thousands of product images automatically using Azure AI Vision. Which visual feature parameter should you specify in the analyze request?

Accepted Answer

features=caption. The `caption` feature in Azure AI Vision's Analyze Image API generates a human-readable, one-sentence description of the entire image content. It is specifically designed for scenarios like automatically describing product images at scale. The API returns a `captions` array with `text` and `confidence` values, making it the correct choice for descriptive captioning.

Answer

features=read

Answer

features=smartCrops

Answer

features=objects

Question 5

You need to determine the primary dominant colors and whether an image is predominantly black-and-white using Azure AI Vision. Which visual feature parameter provides this information?

Accepted Answer

features=color. The features=color parameter in Azure AI Vision returns color-related analysis, including the dominant foreground and background colors and whether the image is black-and-white (isBWImg). This directly provides the dominant colors and monochrome determination requested. Other features return captions, people detection, or tags, not color metadata.

Answer

features=denseCaptions

Answer

features=people

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features=tags

Question 6

Your application uploads an image to Azure AI Vision for analysis. You need to generate thumbnail images with specific aspect ratios while maintaining the primary region of interest. Which API endpoint or feature should you use?

Accepted Answer

Call the Get Thumbnail API passing width, height, and smartCropping parameters.. The Get Thumbnail API in Azure AI Vision is specifically designed to generate thumbnails with a specified width and height, and it supports a smartCropping parameter. When smartCropping is set to true, the service analyzes the image to identify the primary region of interest (e.g., faces, salient objects) and crops the image to that region while preserving the requested aspect ratio. This directly fulfills the requirement without needing custom models or local processing.

Answer

Use Custom Vision Training API to train a cropping model.

Answer

Call the Analyze Image API with features=objects and crop the image locally.

Answer

Use Azure AI Document Intelligence layout analysis.

Question 7

Your enterprise application uses Custom Vision. Due to compliance policies, you must ensure that your training data and model endpoints reside within a specific Azure region with strict data residency guarantees. Which resource configuration should you use?

Accepted Answer

Create a Custom Vision training and prediction resource in the targeted Azure region.. Custom Vision data residency is controlled by the region of the training and prediction resources you create. To guarantee that training data and model endpoints stay in a specific Azure region, you must create both a Custom Vision training resource and a prediction resource in that target region. The service then stores and processes data only within that region.

Answer

Use the Global resource endpoint for Custom Vision.

Answer

Use Azure AI Vision multi-service account with global routing.

Answer

Store images in Azure Blob Storage with geo-replication.

Question 8

You are designing a multimodal solution that combines Azure AI Vision dense captions with Azure OpenAI GPT-4 Vision. You want to extract precise bounding box coordinates for multiple objects in complex scenes and pass them as structured context. Which API feature should you invoke?

Accepted Answer

features=denseCaptions. The denseCaptions feature of Azure AI Vision returns a caption for each detected region along with bounding box coordinates, making it ideal for extracting precise object locations in complex scenes. It provides both a natural-language description and the bounding box for each caption, which can be passed as structured context to GPT-4 Vision. This is the only feature in the list that pairs captions with bounding boxes for multiple objects.

Answer

features=objects

Answer

features=smartCrops

Answer

features=tags

Question 9

Your application analyzes high-resolution engineering blueprints using Azure AI Vision Read API. Users report that small text annotations are occasionally missed. How can you optimize the input image or API configuration to improve recognition of small text?

Accepted Answer

Ensure the image is submitted at full resolution without lossy compression, and verify contrast.. The Read API's OCR accuracy for small text depends heavily on the effective pixel density of the characters. Submitting the blueprint at full resolution without lossy compression preserves the fine strokes of small annotations, and verifying sufficient contrast ensures the OCR engine can distinguish text from the background. Lossy compression (JPEG) introduces artifacts that blur small glyphs, directly causing missed detections.

Answer

Convert the blueprint to grayscale and reduce color depth to 1-bit monochrome.

Answer

Switch from Read API to Tagging API.

Answer

Downsample the image resolution to under 1000x1000 pixels before submission.

Question 10

You are evaluating a Custom Vision classification model. You want to programmatically test the model against a validation dataset without using the portal UI. Which endpoint should your script call?

Accepted Answer

POST /customvision/v3.3/Prediction/{projectId}/classify/iterations/{publishedName}/image. To programmatically test a published Custom Vision classification model, you call the prediction endpoint POST /customvision/v3.3/Prediction/{projectId}/classify/iterations/{publishedName}/image. This endpoint accepts an image and returns the predicted tags with confidence scores for the specified published iteration. It is the correct REST path for classification predictions outside the portal.

Answer

POST /computervision/imageanalysis:analyze

Answer

POST /customvision/v3.3/Training/{projectId}/images

Answer

GET /vision/v3.2/models

Question 11

Your application uploads images to Azure AI Vision via URLs. One of your URLs is failing with an invalid image error, even though the image opens correctly in web browsers. What is the most likely reason?

Accepted Answer

The URL requires HTTP Basic Authentication or redirects that the vision service cannot resolve.. Azure AI Vision's URL-based image input requires the service to fetch the image directly from the provided URL. If the URL is behind HTTP Basic Authentication, requires a token, or returns a redirect (3xx) that the service cannot follow, the fetch fails and the service returns an 'invalid image' error even though a browser (which handles auth prompts and redirects) can open it fine. The fix is to make the image publicly accessible or upload the binary bytes instead.

Answer

Azure AI Vision does not support URL-based inputs; you must upload binary bytes.

Answer

The image file size exceeds 500 MB.

Answer

Image URLs are restricted to PNG format only.

Question 12

You are troubleshooting a Custom Vision object detection model that has low precision and high false-positive rates for a specific object class. During data analysis, you notice that negative images (images without the object) lack background variety. What is the recommended remediation step?

Accepted Answer

Add diverse negative images containing background environments similar to where false positives occur.. Low precision with high false positives on a specific class almost always means the model has not learned to distinguish the target object from background contexts it mistakenly associates with that class. Adding diverse negative images that mirror the environments where false positives occur teaches the model that those backgrounds alone do not constitute the object, directly reducing false positives and improving precision.

Answer

Increase the probability threshold in the prediction API call.

Answer

Remove all tagged bounding boxes and retrain as a multi-class classification model.

Answer

Apply aggressive data augmentation to duplicate existing positive images.

Question 13

You are building an automated asset management system that processes millions of images annually. To optimize costs and latency, you want to minimize the number of API calls while extracting tags, bounding box objects, and OCR text simultaneously. How should you structure your API request?

Accepted Answer

Include multiple comma-separated values in the features parameter, such as features=tags,objects,read.. The Image Analysis 4.0 analyze endpoint accepts a comma-separated features parameter, so a single POST with features=tags,objects,read returns all three result sets in one response. This minimizes billable transactions and network round-trips versus issuing separate calls. The response JSON contains separate sections (tags, objects, readResult) that the client parses independently.

Answer

Submit three separate asynchronous requests for tags, objects, and read.

Answer

Use the legacy Computer Vision v3.2 batch analysis endpoint.

Answer

Train a Custom Vision model to combine all three tasks.

Question 14

You are deploying a Custom Vision object detection model as a Docker container on an Azure Kubernetes Service (AKS) cluster. During load testing, you notice high memory consumption and container restarts. Which configuration setting in the deployment manifest should you review to ensure stability?

Accepted Answer

Kubernetes resource requests and limits for memory and CPU.. High memory consumption and container restarts in Kubernetes are classic symptoms of missing or misconfigured resource requests and limits. Setting appropriate memory and CPU requests/limits in the deployment manifest ensures the scheduler places the pod correctly and prevents OOMKills.

Answer

Billing endpoint URL syntax.

Answer

Azure Blob Storage connection string persistence.

Answer

Custom Vision iteration publishing name environment variable.

Question 15

You are using Azure AI Vision to analyze an image. You want to receive the output in Spanish instead of English. Which query parameter should you add to your request?

Accepted Answer

language=es. Azure AI Vision's Analyze Image API accepts a `language` query parameter that specifies the language for the returned results, such as captions and tags. Setting `language=es` returns the output in Spanish.

Answer

translation=spanish

Answer

output-lang=es

Answer

locale=es-ES