AI-103 Computer Vision Practice Test 5 — 15 Questions

Question 1

You are creating a Custom Vision project and need to select a domain. Your project involves classifying images of various food dishes. Which domain should you select to optimize model accuracy for food classification?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Food. The Food domain in Custom Vision is a specialized model pretrained on food imagery, so it produces higher accuracy for classifying dishes than a generic domain. Selecting the domain that matches the image category is the recommended way to optimize model performance in Custom Vision.

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Landmark

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General

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Retail

Question 2

You need to extract text from a multi-page PDF document using Azure AI Vision Read API. How are pages represented in the JSON response structure?

Accepted Answer

They are organized under a 'readResult.pages' or 'pages' array in the analyze result.. The Read API (and the newer Image Analysis/Read model) returns an analyze result that includes a pages array (or readResult.pages in older SDK shapes), where each page contains lines, words, and their bounding polygons plus angle and dimensions. This page-level structure lets you map extracted text back to specific pages of a multi-page PDF.

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All text from all pages is merged into a single unstructured string without page separation.

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Pages are returned as separate JPEG files in a ZIP archive.

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Each page requires a separate API transaction starting with a new POST request.

Question 3

Your enterprise application uses Azure AI Vision. Security policies require that all API keys are rotated every 30 days without causing application downtime. How should you manage the key rotation?

Accepted Answer

Switch client applications to Key 2, regenerate Key 1 in the Azure portal, switch clients to Key 1, and then regenerate Key 2.. Azure AI services (including Azure AI Vision) issue two API keys per resource specifically to enable zero-downtime rotation. The correct pattern is to move all clients to Key 2 while Key 1 is still valid, regenerate Key 1, cut clients back over to the new Key 1, then regenerate Key 2 — so at no point is a client using a key that has just been invalidated. This dual-key design is the documented Microsoft approach for credential rotation without service interruption.

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Restart the Azure App Service hosting the client application.

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Delete the Azure AI service resource and create a new one every 30 days.

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Disable authentication on the resource during the rotation window.

Question 4

You want to extract gender and age attributes from human faces in photos. While Azure AI Face service handles facial analysis, which base Azure AI Vision 4.0 feature detects the presence and bounding boxes of people?

Accepted Answer

features=people. In Azure AI Vision 4.0 image analysis, the 'people' feature detects human presence and returns bounding boxes for each person in the image, which is the base capability you need before applying Face service for gender/age attributes. The 'objects' feature detects common objects (not specifically people), and 'tags'/'read' serve different purposes. So 'features=people' is the correct query parameter.

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features=objects

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features=tags

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features=read

Question 5

You are testing a Custom Vision model via the REST API. You receive an HTTP 400 Bad Request error with the message 'InvalidProjectId'. What is the most likely cause?

Accepted Answer

The project ID GUID in the request URL is incorrect or does not exist in this Custom Vision account.. The Custom Vision REST API returns HTTP 400 with 'InvalidProjectId' when the project ID GUID supplied in the request URL path (e.g., /customvision/v3.0/training/projects/{projectId}/...) cannot be resolved to a project in the account tied to the submitted training or prediction key. This is a client-side request error, meaning the GUID is malformed, belongs to a different Custom Vision resource/account, or the project was deleted. Because the error is specifically named 'InvalidProjectId', the failure is at the identifier-validation stage, before any image payload is parsed.

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The image file format is invalid.

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The subscription has reached its monthly quota limit.

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The prediction key header was omitted.

Question 6

You are implementing a disaster recovery strategy for a critical image processing application that relies on Azure AI Vision. If the primary Azure region experiences an outage, how should you configure failover?

Accepted Answer

Deploy a secondary Azure AI Vision resource in a paired Azure region and configure your application to fail over to the secondary endpoint if errors occur.. Azure AI Vision is a regional PaaS service, so disaster recovery requires deploying a secondary resource in a paired region and having the application fail over to that endpoint when the primary returns errors. There is no automatic cross-region replication of the service itself, so the application must implement the failover logic. This matches Azure's shared responsibility model for PaaS DR.

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Use geo-replication on the Azure Blob Storage account to automatically fail over the AI service endpoints.

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Configure Azure Site Recovery to replicate the Azure AI Vision service backend VMs.

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Rely on automatic synchronous replication of Custom Vision models across all global Azure datacenters without application changes.

Question 7

You are using Azure AI Vision 4.0. You want to obtain a concise, single-sentence human-readable description of an image. Which feature parameter should you request?

Accepted Answer

features=caption. The features=caption parameter in Azure AI Vision 4.0 returns a single, concise, human-readable sentence describing the image — exactly what the question asks for. It is the direct match for 'concise, single-sentence description.'

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features=tags

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features=denseCaptions

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features=read

Question 8

You are using Custom Vision to train an object detection model. You want to evaluate model performance before publishing an iteration. Which metric represents the overall accuracy of the model across all classes at various confidence thresholds?

Accepted Answer

Mean Average Precision (mAP). Mean Average Precision (mAP) is the standard metric for object detection models because it aggregates precision across all classes and Intersection over Union (IoU) thresholds. It computes the average precision (AP) for each class—the area under the precision-recall curve—and then averages those AP values. This gives a single score that reflects how well the model localizes and classifies objects across different confidence thresholds, which is exactly what Custom Vision reports for object detection iterations.

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Perplexity

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Word Error Rate (WER)

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BLEU Score

Question 9

When configuring a Custom Vision project, which TWO project types are available? (Choose two.)

Accepted Answer

Classification. Azure Custom Vision supports exactly two project types: Classification (option C) and Object Detection (option E). Classification (C) is correct because it trains a model to assign one or more labels to an entire image (e.g., "cat" vs. "dog"), which is a core Custom Vision project type. Object Detection (E) is correct because it trains a model to locate and classify multiple objects within an image by returning bounding boxes with labels, the other supported Custom Vision project type. The remaining options do not belong: Speech Synthesis (A) is an Azure Speech service capability, Form Extraction (B) is handled by Form Recognizer, and Text Recognition/OCR (D) is provided by Computer Vision or Read API, not Custom Vision.

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Speech Synthesis

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Form Extraction

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Text Recognition (OCR)

Question 10

You are optimizing an application that calls Azure AI Vision Read API. Which THREE techniques help reduce latency and improve overall throughput? (Choose three.)

Accepted Answer

Resize extremely large images to optimal dimensions while maintaining readability.. Option C is correct because the Read API's OCR accuracy and speed depend on image resolution; downscaling extremely large images to dimensions that keep text legible reduces upload size and server-side processing time, lowering latency. Option D is correct because the Read API is asynchronous: you submit an image, receive an Operation-Location URL, and must poll the GET result endpoint; using a sensible interval such as 1 second avoids throttling (HTTP 429) and wasted requests that aggressive busy-waiting causes, improving effective throughput. Option E is correct because placing the calling application and the Azure AI Vision resource in the same Azure region minimizes network round-trip time for both the initial POST and the subsequent polling GETs, directly reducing latency. Option A is not appropriate because the Read API accepts JPEG, PNG, BMP, PDF, and TIFF, and sending uncompressed RAW bitmap data would inflate payload size and transfer time rather than bypass meaningful decoding overhead. Option B is not appropriate because disabling SSL/TLS verification weakens security, does not meaningfully speed up TCP handshakes, and is not a supported optimization for the Azure AI Vision Read API.

Answer

Send uncompressed RAW bitmap data to bypass image decoding overhead.

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Disable SSL/TLS verification to speed up TCP handshakes.

Question 11

You are building a document processing pipeline that reads printed English text from scanned invoices. The invoices often contain noisy backgrounds and low-contrast text. You need to extract the text as accurately as possible using the Azure AI Vision Image Analysis 4.0 Read capability. Which approach should you take?

Accepted Answer

Preprocess the image with contrast enhancement and denoising, then call the Read operation and use the returned words and lines.. The Read capability extracts text from images but performs best when the input is clear. For scanned invoices with noise and low contrast, preprocessing steps such as contrast enhancement and denoising significantly improve recognition accuracy. After preprocessing, calling Read and using the returned words and lines provides the most accurate text extraction, making this the correct approach.

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Call the Read operation on the raw image without preprocessing, then post-process the returned text lines using custom regex.

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Enable the OCR feature with a custom model trained on invoice fonts using Azure AI Vision Studio.

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Use the Dense Captions feature to generate descriptions of the invoice text, then parse the descriptions for key fields.

Question 12

You are using Azure AI Vision Image Analysis 4.0 to analyze a batch of images. You need to extract both the caption and the detected objects with their bounding boxes. You are writing code that calls the Analyze Image API. Which two parameters must you include in your request? (Choose two.)

Accepted Answer

visualFeatures=caption. To obtain both captions and object bounding boxes from a single Analyze Image call, the visualFeatures query parameter must include both caption and objects. These features are requested together, and the response will contain the corresponding sections. Other parameters like details, language, or model-version are optional and do not enable these specific outputs.

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details=landmarks

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language=en

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model-version=latest

Question 13

You are developing a web application that allows users to upload images and receive a list of detected objects with their bounding boxes. You want to use the Azure AI Vision Image Analysis 4.0 SDK for Python. Which method should you call to analyze an image from a URL and retrieve object detection results?

Accepted Answer

analyze_image. The analyze_image method is the primary entry point for image analysis in the Azure AI Vision SDK. By specifying visual features such as objects, it returns detected objects with their bounding boxes and labels. Other methods like generate_thumbnail, get_area_of_interest, and tag_image serve different purposes and do not provide object detection with coordinates.

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get_area_of_interest

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tag_image

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generate_thumbnail

Question 14

You are building a Python application that uses the Azure AI Vision Image Analysis 4.0 SDK to read text from scanned invoices. The invoices are stored in an Azure Blob Storage container, and each blob has a public URL. You need to extract all visible text from each invoice with a single REST API call, without downloading the image bytes to your application. Which API call should you make?

Accepted Answer

POST to the Image Analysis 4.0 endpoint with the URL parameter set to the blob URL and the features query parameter set to read. The read feature is the OCR capability of Image Analysis 4.0, and the url query parameter lets the service fetch the image directly from Blob Storage. Combining them satisfies both constraints: extracting all visible text and avoiding a local download. Caption, denseCaptions, and tags describe or classify the image rather than returning its textual content.

Answer

POST to the Image Analysis 4.0 endpoint with the URL parameter set to the blob URL and the features query parameter set to caption

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POST to the Image Analysis 4.0 endpoint with the URL parameter set to the blob URL and the features query parameter set to denseCaptions

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POST to the Image Analysis 4.0 endpoint with the URL parameter set to the blob URL and the features query parameter set to tags

Question 15

A retail analytics team wants to count how many people enter a store each hour. They plan to analyze frames from an existing ceiling-mounted camera feed using Azure AI Vision Image Analysis 4.0. They need the count of detected people and the bounding box for each person, without storing the images long term. Which approach should they use?

Accepted Answer

Call the Image Analysis 4.0 analyze endpoint with the features parameter set to people, and read the peopleResult object from the response. The people feature is purpose-built for detecting humans and returns both a count and bounding boxes in peopleResult. That maps exactly to the hourly footfall metric and the per-person coordinates. The other features describe objects, tags, or suggested crops and do not provide dependable person-level detection or counting.

Answer

Call the Image Analysis 4.0 analyze endpoint with the features parameter set to tags, and count tags named person

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Call the Image Analysis 4.0 analyze endpoint with the features parameter set to smartCrops, and use the crop regions as person locations

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Call the Image Analysis 4.0 analyze endpoint with the features parameter set to objects, and filter the results for the person tag