AI-103 Computer Vision Practice Test 4 — 15 Questions

Question 1

You are using Custom Vision and want to programmatically publish a trained iteration so that prediction endpoints can use it. Which API operation should you call?

Accepted Answer

POST /customvision/v3.3/Training/{projectId}/iterations/{iterationId}/publish. Publishing a trained Custom Vision iteration so prediction endpoints can consume it is done by calling POST /customvision/v3.3/Training/{projectId}/iterations/{iterationId}/publish. The publish operation makes the iteration available at the prediction URL, optionally under a named prediction resource. This is the required step before any prediction calls can target that iteration.

Answer

GET /customvision/v3.3/Training/{projectId}/iterations/{iterationId}/make-default

Answer

PUT /customvision/v3.3/Prediction/{projectId}/iterations/{iterationId}/activate

Answer

PATCH /customvision/v3.3/Training/{projectId}/projects/{projectId}

Question 2

Your application calls Azure AI Vision API and receives an HTTP 429 Too Many Requests response. What does this error signify, and how should your application handle it?

Accepted Answer

You have exceeded your pricing tier rate limits. Implement exponential backoff and retry the request.. HTTP 429 Too Many Requests indicates that the application has exceeded the rate limits for its Azure AI Vision pricing tier (e.g., transactions per second or per minute). The correct handling is to implement exponential backoff with jitter and retry the request after a delay, respecting the 'Retry-After' header if provided. This prevents overwhelming the service and allows the request to succeed once the rate limit resets.

Answer

The service is undergoing maintenance. Fail over to a secondary region.

Answer

The image file format is unsupported. Convert to PNG.

Answer

Your API key is invalid. Refresh your subscription key immediately.

Question 3

You are analyzing multi-language documents containing mixed English and Japanese text using Azure AI Vision Read API. How should you specify the language parameter to ensure optimal OCR accuracy?

Accepted Answer

Omit the language parameter or set language=unk to enable automatic language detection across supported languages.. The Read API supports automatic language detection when the language parameter is omitted or set to unk, allowing it to identify and process multiple languages within the same document, including mixed English and Japanese text. This is the recommended approach for multi-language documents because forcing a single language can degrade recognition of the other language's characters.

Answer

Use the legacy OCR v3.0 endpoint with multi-language flag.

Answer

You must submit English and Japanese pages separately in distinct API calls.

Answer

Set language=en-US exclusively.

Question 4

You are designing an edge solution where a Custom Vision exported model runs inside a Linux container on an IoT Edge device without internet connectivity. How must you configure billing for the container?

Accepted Answer

Provide the container with a billing endpoint URL and API key, and ensure periodic connectivity to Azure to report billing metrics.. Custom Vision exported containers (Docker for Linux or Windows) require two environment variables at runtime: a billing endpoint URL and an API key from a Custom Vision resource, plus periodic connectivity to Azure to report billing metrics. The container can run offline for inference, but it must periodically reach the billing endpoint to validate the key and report usage; otherwise it stops serving predictions.

Answer

Use Custom Vision Training API keys for container runtime billing.

Answer

Purchase an Azure Storage account to store offline billing logs locally.

Answer

Deploy the container with no environment variables; billing is handled automatically by IoT Hub.

Question 5

You are testing an image analysis application in Python. You want to retrieve object bounding boxes using Azure AI Vision 4.0. Which feature string should you pass to the client?

Accepted Answer

VisualFeatures.OBJECTS. In the Azure AI Vision 4.0 SDK, object detection is requested by passing VisualFeatures.OBJECTS to the analyze call, which returns bounding boxes and labels for detected objects. The enum value maps to the 'objects' feature string on the wire. This is distinct from TAGS (whole-image labels), CAPTION (single-sentence description), and READ (OCR text).

Answer

VisualFeatures.TAGS

Answer

VisualFeatures.CAPTION

Answer

VisualFeatures.READ

Question 6

Which Azure AI service should you use if you need to train a model to recognize your company's proprietary product packaging using custom-labeled images with minimal machine learning expertise?

Accepted Answer

Azure AI Custom Vision. Azure AI Custom Vision is purpose-built for training custom image classification and object detection models using your own labeled images, and it abstracts away the ML pipeline (data splitting, hyperparameter tuning, model selection) behind a simple portal and SDK. It requires no ML expertise — you upload images, tag them, and click Train. This makes it the correct choice for recognizing proprietary product packaging with custom labels.

Answer

Azure AI Vision Prebuilt API

Answer

Azure Machine Learning Automated ML

Answer

Azure AI Document Intelligence

Question 7

Your application processes images submitted by users. You want to ensure that images containing inappropriate or explicit content are rejected before further processing. Which Azure AI Vision feature should you evaluate first?

Accepted Answer

Analyze Image with features=adult. The Analyze Image API with features=adult returns adult and racy content scores (0-1) plus gory and suggestive flags, which is exactly what you need to reject inappropriate images. This is the built-in content moderation capability of Azure AI Vision. Custom Vision and OCR do not perform content moderation, and Azure AI Language Content Moderation is a text-based service, not an image service.

Answer

Custom Vision Object Detection

Answer

Image Analysis Read feature

Answer

Azure AI Language Content Moderation

Question 8

You are managing a multi-tenant SaaS application that uses Azure AI Vision. Each tenant has their own isolated data, and you want to track costs per tenant. How should you provision Azure AI services to achieve granular cost attribution?

Accepted Answer

Deploy a dedicated Azure AI Services resource per tenant and use Azure Cost Management for billing attribution.. Deploying a dedicated Azure AI Services resource per tenant is the only approach that provides true isolation and granular cost attribution, because Azure Cost Management tracks costs at the resource level. Each tenant's resource appears as a separate line item in cost analysis, enabling per-tenant billing. This also satisfies the data-isolation requirement mentioned in the scenario.

Answer

Use a single global Azure AI Services resource and parse tenant IDs from request user-agent headers for billing.

Answer

Store tenant IDs in Custom Vision project tags.

Answer

Use Azure Blob Storage cost allocation tags to cover AI API costs.

Question 9

You are troubleshooting a performance issue where your application's calls to Azure AI Vision experience high latency. Your application is hosted in Azure West US, but your Azure AI Vision resource was accidentally created in Azure Southeast Asia. What is the recommended remediation?

Accepted Answer

Recreate the Azure AI Vision resource in the West US region close to your application hosting.. Azure AI Vision is a regional service, and network latency between the application and the service endpoint is dominated by geographic distance. Recreating the resource in West US colocates the service with the application, minimizing round-trip time and eliminating cross-region egress. This is the standard remediation for latency caused by region mismatch.

Answer

Increase the timeout threshold in the HTTP client configuration.

Answer

Enable Azure ExpressRoute between West US and Southeast Asia.

Answer

Switch from REST API to gRPC protocol.

Question 10

You are designing an image analysis solution using Azure AI Vision 4.0. Which TWO visual features can be requested in a single Image Analysis API analyze request? (Choose two.)

Accepted Answer

tags. The Image Analysis 4.0 analyze request accepts a 'visualFeatures' parameter that can include 'tags' to return content tags describing the image. Option C (read) is also correct because 'read' is a valid visual feature in the same 'visualFeatures' list, enabling OCR text extraction within the same analyze call. Both features belong to the Azure AI Vision Image Analysis 4.0 API surface and can be combined in one request, e.g., visualFeatures=tags,read. Option B (Custom Vision Classification) is not part of the Image Analysis API; it is a separate Custom Vision service with its own prediction endpoint. Option D (Azure AI Face Analyze) is a distinct Face service API, not a visual feature of Image Analysis. Option E (Azure AI Document Intelligence Layout) is a separate Document Intelligence model/API and cannot be requested through the Image Analysis analyze operation.

Answer

Custom Vision Classification

Answer

Azure AI Face Analyze

Answer

Azure AI Document Intelligence Layout

Question 11

Your enterprise requires strict security and governance over your Azure AI Vision resources. Which THREE security configurations should you implement? (Choose three.)

Accepted Answer

Configure Azure Private Endpoints to restrict access to your Virtual Network.. Option C is correct because Azure Private Endpoints create a private IP address for the Azure AI Vision resource inside your Virtual Network, eliminating exposure to the public internet and enforcing network-level isolation. Option D is correct because enabling diagnostic logging and streaming audit records to Log Analytics provides the governance and auditability required for strict security monitoring, retention, and compliance reporting. Option E is correct because Microsoft Entra ID RBAC provides fine-grained, identity-based authorization with conditional access and managed identities, which is more secure and governable than shared API keys alone. Option A is wrong because storing API keys in plain text on public GitHub repositories exposes credentials to anyone and violates every security best practice. Option B is wrong because disabling TLS removes encryption in transit, exposing data to interception and violating compliance requirements; latency gains do not justify this risk.

Answer

Store API keys in plain text configuration files on public GitHub repositories.

Answer

Disable TLS encryption to improve API response latency.

Question 12

You are preparing training images for a Custom Vision object detection project. Which THREE data preparation practices should you follow to ensure high model accuracy? (Choose three.)

Accepted Answer

Vary object angles, lighting, and scale across your training images.. Option A is correct because varying object angles, lighting, and scale exposes the model to the natural variability it will encounter at inference time, which is essential for a robust Custom Vision object detector. Option C is correct because Microsoft's Custom Vision documentation recommends a minimum of 15 tagged images per tag for object detection, and more images generally yield better accuracy. Option D is correct because including varied backgrounds for the same object class prevents the model from overfitting to a single context and helps it generalize to new environments. Option B is incorrect because using identical backgrounds for all samples reduces variability and encourages overfitting to that background rather than learning the object itself. Option E is incorrect because forcing all images under 100x100 pixels discards detail and resolution that object detection models need to localize and classify objects accurately.

Answer

Use identical background images for all training samples to maintain consistency.

Answer

Keep all training images under 100x100 pixels to speed up training.

Question 13

You are troubleshooting a Custom Vision object detection model that exhibits poor performance in production. Which THREE actions can you take using the Custom Vision portal or APIs to diagnose and improve the model? (Choose three.)

Accepted Answer

Examine prediction suggestions to find low-confidence or incorrectly predicted images and add them to training data.. Option B is correct because the Custom Vision portal's Predictions view surfaces images the deployed model scored with low confidence or misclassified, and feeding those hard examples back into the training set as labeled data is the standard active-learning loop for improving an object detection model. Option C is correct because the Performance tab reports per-tag precision, recall, and mean average precision (mAP), letting you identify which object classes the model detects poorly and target them with more or better-labeled images. Option D is correct because the Performance page includes a probability threshold slider that recomputes precision and recall at different confidence cutoffs, so you can pick an operating point that balances false positives and false negatives for your production scenario. Option A is wrong because Custom Vision does not expose iteration history deletion as a diagnostic or improvement action, and resetting to random weights would destroy the trained model rather than improve it. Option E is wrong because Custom Vision is a managed service that does not expose neural network weights for manual editing through the REST API; you can only influence the model through training data, tags, domains, and training parameters.

Answer

Delete all project iteration history to reset model weights to random initialization.

Answer

Manually edit model neural network weights via the REST API.

Question 14

Which TWO image file formats are natively supported by Azure AI Vision for image analysis? (Choose two.)

Accepted Answer

JPEG. Azure AI Vision's Image Analysis API natively accepts JPEG (option D) and PNG (option E) as image payloads, since these are the standard raster formats the service decodes for feature extraction such as captioning, OCR, and object detection. JPEG is supported because it is the most common compressed photographic format, and PNG is supported because it preserves lossless detail and transparency for graphics and text-heavy images. The remaining options do not belong: MP3 (A) and WAV (C) are audio codecs, and MKV (B) is a video container format, none of which are image file formats accepted by the Image Analysis endpoint.

Answer

MP3

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MKV

Answer

WAV

Question 15

You are deploying Custom Vision containers on-premises using Docker. Which THREE prerequisites and configuration steps are required? (Choose three.)

Accepted Answer

Ensure internet connectivity or periodic connection to Azure for billing metering.. Option A is correct because Custom Vision containers must periodically connect to Azure (or maintain internet connectivity) to send usage/billing metering data; without this metering connection the container will not run. Option B is correct because the container requires the Billing Endpoint URI (e.g., https://<region>.api.cognitive.microsoft.com) and the associated API Key, supplied as environment variables such as Billing and ApiKey, so Azure can validate and meter the resource. Option C is correct because the container image requires explicit acceptance of the End-User License Agreement via the EULA=accept environment variable before it will start. Option D is not required because the Azure CLI is not installed inside the prediction container; billing is handled through the endpoint and key. Option E is not required and is not a supported configuration; no root access to the Azure billing portal from inside the container is needed.

Answer

Install the full Azure CLI inside every prediction container.

Answer

Grant root access to the Azure subscription billing portal from inside the container.