AI-102 Practice Test 28 — 15 Questions

Question 1

You are creating a new Custom Vision project with the above JSON. The domainId corresponds to the 'Logo' domain. Which type of model will this project train?

Accepted Answer

A multilabel image classification model for logo detection. Option A is correct because the 'Logo' domain is optimized for logo detection. The classificationType 'Multilabel' indicates image classification (multiple labels per image), not object detection. Option B is wrong because object detection requires a different classificationType. Option C is wrong because OCR is not a Custom Vision domain. Option D is wrong because the domain is 'Logo', not 'General'.

Answer

An object detection model for logo detection

Answer

An optical character recognition model

Answer

A general image classification model

Question 2

You have a computer vision solution that analyzes security camera feeds to detect people and vehicles. The solution uses Azure AI Vision Spatial Analysis. You need to ensure compliance with privacy regulations by blurring detected faces. Which feature should you enable?

Accepted Answer

Enable face blurring in the Spatial Analysis configuration. Azure AI Vision includes face blurring (or anonymization) as a built-in capability for privacy compliance in spatial analysis scenarios.

Answer

Use Azure AI Content Safety to filter faces

Answer

Post-process frames with Azure AI Face client SDK

Answer

Enable face detection and redact faces using Azure AI Video Indexer

Question 3

You are building a document processing solution that extracts information from invoices. The invoices come in various formats and languages. You need to extract line items, totals, and supplier names. Which THREE services should you combine?

Accepted Answer

Azure AI Translator. Document Intelligence extracts structured data. AI Vision OCR handles images. AI Translator translates if needed. These three work together.

Answer

Azure AI Custom Vision

Answer

Azure AI Content Safety

Question 4

A company uses the Face API for identity verification. During testing, they find that the similarity scores between two images of the same person are lower than expected. Which factor is most likely causing this?

Accepted Answer

The images have different lighting conditions (e.g., one is brightly lit, the other is dark).. Option B is correct because large differences in lighting can significantly affect similarity scores. Option A is wrong because moderate difference in image size does not affect facial features. Option C is wrong because background mismatch does not affect the face comparison. Option D is wrong because compression artifacts may have a minor effect but not as much as lighting.

Answer

The images are compressed with different quality levels.

Answer

The backgrounds of the images are different.

Answer

The images have different dimensions (e.g., 500x500 vs 1000x1000).

Question 5

You are deploying a Custom Vision model to a production environment. The model must handle 100 predictions per second with low latency. Which deployment option should you choose?

Accepted Answer

Use a paid tier prediction endpoint with sufficient capacity.. Option B is correct because a dedicated prediction endpoint with a paid tier ensures sufficient throughput and low latency. Option A is wrong because the Free tier has very limited transactions per second. Option C is wrong because the Export feature exports the model for offline use, not for real-time cloud serving. Option D is wrong because the Training API is for training, not prediction.

Answer

Use the Free tier prediction endpoint.

Answer

Export the model as a Docker container and run it on Azure Container Instances.

Answer

Use the Training API to make predictions.

Question 6

You are building a solution to detect if a person is wearing a hard hat in construction site images. You have a small dataset of labeled images. Which Azure service should you use?

Accepted Answer

Azure Custom Vision. Option B is correct because Custom Vision allows you to train a custom object detection model with your own images. Option A is wrong because Azure AI Vision prebuilt models do not include hard hat detection. Option C is wrong because Video Indexer is for video analysis, not static images. Option D is wrong because Azure AI Document Intelligence is for document processing.

Answer

Azure AI Vision Image Analysis

Answer

Azure Video Indexer

Answer

Azure AI Document Intelligence

Question 7

Your organization uses Azure AI Vision to analyze surveillance images for security threats. The solution must comply with GDPR. Which TWO actions should you take?

Accepted Answer

Disable logging of request and response data.. Options B and D are correct because disabling logging ensures personal data is not stored in logs, and using data residency ensures data stays within the EU. Option A is wrong because CMK does not affect logging compliance. Option C is wrong because private endpoints improve security but do not directly address GDPR logging requirements. Option E is wrong because geo-redundant storage may cause data to leave the EU.

Answer

Enable geo-redundant storage for the Azure AI Vision resource.

Answer

Enable Customer-Managed Keys (CMK) for encryption.

Answer

Use private endpoints to access the service.

Question 8

You have a real-time video processing pipeline using Azure AI Video Indexer. You need to detect when a specific person appears in archived video footage. Which approach minimizes latency and cost?

Accepted Answer

Use Video Indexer's face detection and indexing, then search. Using face detection and indexing in Video Indexer allows efficient search. Re-indexing only when needed avoids unnecessary processing.

Answer

Extract keyframes and use Custom Vision to detect the person

Answer

Run face detection on every frame using Azure AI Face and store results

Answer

Use Azure AI Vision to detect faces in video frames and compare against a database

Question 9

A retail company uses Azure AI Vision to analyze shelf images for inventory management. They notice that the Object Detection model sometimes misses small items. What is the most effective way to improve detection of small objects?

Accepted Answer

Train a custom object detection model with annotated images that include small objects.. Option C is correct because training a custom model with images containing small objects annotated properly will improve the model's ability to detect them. Option A is wrong because increasing resolution may help but can also increase cost and latency without targeted training. Option B is wrong because background removal does not improve detection of small objects. Option D is wrong because the Background Removal API is for image editing, not object detection.

Answer

Preprocess images to remove background noise.

Answer

Use the Background Removal API to isolate items.

Answer

Increase the image resolution before sending to the API.

Question 10

You are analyzing an image using the Azure AI Vision REST API with the JSON request above. The response includes a description: 'a person holding a smartphone'. However, the response does not include any brand information even though the smartphone is clearly visible. What is the most likely reason?

Accepted Answer

The smartphone brand is not in the Microsoft brand catalog.. Option D is correct. The brands feature is included, but brand detection only works for well-known brands; if the smartphone brand is not in Microsoft's brand catalog, it won't be detected. Option A is wrong because 'brands' is included. Option B is wrong because model-version 'latest' supports brand detection. Option C is wrong because the API can detect multiple brands.

Answer

The 'brands' feature was not included in the request.

Answer

The 'model-version' is set to 'latest' which does not support brand detection.

Answer

The API only detects one brand per image, and a different brand was detected.

Question 11

Which THREE factors should you consider when selecting a pricing tier for Azure Computer Vision in a production environment?

Accepted Answer

Data residency requirements. A, B, and D are correct. Transactions per second (TPS) is a key constraint. Data residency requirements may influence region selection and tier features. Latency requirements may lead to choosing a tier with guaranteed performance. C is wrong because storage type is not a factor for Computer Vision. E is wrong because the free tier is not suitable for production.

Answer

Availability of free tier

Answer

Type of storage account for images

Question 12

Your team is building a mobile app that uses Azure Custom Vision to classify plant species. The app must work offline and sync labeled images when connectivity is restored. Which SDK feature should you use?

Accepted Answer

Export the model as a TensorFlow or CoreML model for on-device inference. Option B is correct because Custom Vision provides an offline prediction capability by exporting the model to run locally on device. Option A is wrong because continuous deployment is for cloud deployments. Option C is wrong because API Management is for managing APIs, not offline operation. Option D is wrong because IoT Edge is for edge devices, not mobile apps.

Answer

Azure IoT Edge runtime on the phone

Answer

Azure API Management with caching

Answer

Continuous deployment integration

Question 13

You are building an application that needs to extract printed text from scanned invoices. Which Azure AI service should you use?

Accepted Answer

Azure AI Vision OCR. Azure AI Vision's OCR reads printed text from images. Azure AI Document Intelligence is for forms and structured documents, but OCR is the core capability for printed text extraction.

Answer

Azure AI Document Intelligence

Answer

Azure AI Search

Answer

Azure AI Language

Question 14

You need to design a computer vision solution that detects defects in manufactured parts on a conveyor belt. The solution must run in near real-time and adapt to new defect types without retraining from scratch. Which TWO approaches should you consider?

Accepted Answer

Use Azure AI Custom Vision with object detection and retrain with new defect images. Custom Vision with object detection can be retrained with new defect images. Transfer learning allows quick adaptation to new defect types.

Answer

Use Azure AI Face to detect anomalies

Answer

Use Azure AI Video Indexer to analyze video feeds

Answer

Use pre-built Azure AI Vision Image Analysis to classify images

Question 15

A company wants to extract key-value pairs from scanned invoices using Azure AI. Which service should they use?

Accepted Answer

Azure AI Document Intelligence. Option A is correct because Azure AI Document Intelligence (formerly Form Recognizer) is designed to extract key-value pairs from documents. Option B is wrong because Custom Vision is for image classification. Option C is wrong because OCR only extracts text without structure. Option D is wrong because the Read API extracts text but not key-value pairs.

Answer

Read API

Answer

Custom Vision

Answer

OCR API

AI-102 Practice Test 28 — 15 Questions

Exhibit

AI-102 Practice Test 28 — 15 Questions

Exhibit