AI-102 Implement computer vision solutions Practice Test 7 — 15 Questions

Question 1

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 2

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 3

You are tasked with creating a solution that can identify and count people in a retail store to analyze foot traffic. Which TWO Azure AI services can be used together?

Accepted Answer

Azure AI Video Indexer. Azure AI Vision's people detection counts people in images. Video Indexer can analyze video streams for people counting over time.

Answer

Azure AI Content Safety

Answer

Azure AI Document Intelligence

Answer

Azure AI Face

Question 4

You are a senior AI engineer at a manufacturing company. The company has a production line that uses cameras to capture images of assembled products. The current system uses a set of rule-based heuristics to detect defects, but it has high false-positive rates. You have been tasked to design a new computer vision solution using Microsoft Azure AI services. The solution must:

- Detect defects such as scratches, dents, and misalignments in real-time as products move on the conveyor belt (frame rate of 30 fps).
- Support continuous learning: when a new defect type is discovered, the model should be updated without retraining the entire model from scratch.
- Operate with low latency (<100 ms per inference) to keep up with the production speed.
- Use only fully managed services (no custom containers or edge devices).
- The factory network has limited internet bandwidth, so the solution must minimize data transfer.

Which approach should you recommend?

Accepted Answer

Train an object detection model using Azure AI Custom Vision, export it as a Docker container, and deploy it on an on-premises server with GPU. Azure AI Custom Vision with object detection can be exported and deployed to a Docker container on-premises for low latency. Continuous learning is supported by retraining with new images. However, Option C suggests using Azure AI Video Indexer, which is for video analysis and not real-time defect detection. Option A uses Azure AI Vision Spatial Analysis, which is for people tracking. Option B uses Azure AI Document Intelligence, which is for documents. Option D is the only one that meets all requirements: Custom Vision allows retraining, container deployment for low latency, and minimal bandwidth (only model updates).

Answer

Use Azure AI Vision Spatial Analysis to detect defects in real-time by analyzing video feeds

Answer

Use Azure AI Video Indexer to index and search for defects in recorded videos

Answer

Use Azure AI Document Intelligence to analyze images of products

Question 5

A company is building a solution to analyze customer reviews images using Azure AI Vision. They need to extract text from images that may contain both printed and handwritten text. Which feature should they use?

Accepted Answer

Read API. Option B is correct because the Read API is designed to extract printed and handwritten text from images. Option A is wrong because OCR API is older and less capable for handwritten text. Option C is wrong because Form Recognizer is for forms and documents. Option D is wrong because Custom Vision is for image classification and object detection.

Answer

Custom Vision

Answer

OCR API (optical character recognition)

Answer

Azure AI Document Intelligence

Question 6

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 7

A company wants to moderate user-generated images for adult content. Which Azure AI Vision feature should they use?

Accepted Answer

Analyze Image API with moderation categories. Option B is correct because the Analyze Image API includes adult content moderation. Option A is wrong because OCR is for text extraction. Option C is wrong because Custom Vision can be trained for moderation but is not the simplest solution. Option D is wrong because the Face API is for face detection and recognition.

Answer

Custom Vision with a custom adult classifier

Answer

Face API

Answer

OCR

Question 8

A healthcare organization uses Custom Vision to classify X-ray images. They have a small dataset of 200 images per class. Which strategy will most likely improve model accuracy?

Accepted Answer

Use data augmentation and transfer learning with a pre-trained model.. Option A is correct because data augmentation and transfer learning are standard techniques to improve accuracy with small datasets. Option B is wrong because increasing iterations without more data can lead to overfitting. Option C is wrong because reducing image size may lose important details. Option D is wrong because negative tagging is useful but does not replace the need for more training data.

Answer

Reduce the image dimensions to speed up training.

Answer

Add more negative samples to the dataset.

Answer

Increase the number of training iterations significantly.

Question 9

A company uses the Face API to detect and identify employees for building access. They need to ensure that the system complies with GDPR requirements for biometric data. Which action should they take?

Accepted Answer

Obtain explicit consent from each employee before enrollment.. Option C is correct because obtaining explicit consent is a key GDPR requirement for biometric data processing. Option A is wrong because deleting data after a period may not be sufficient without consent. Option B is wrong because encrypting data is a security measure but does not address consent. Option D is wrong because anonymizing data may circumvent the need for consent but is not always feasible for facial recognition.

Answer

Store faces in a secure database and delete after 30 days.

Answer

Anonymize the face data by blurring key features.

Answer

Use encryption for stored face templates.

Question 10

A company uses the Computer Vision Image Analysis API to generate captions for images. The captions are often too generic. How can they improve the descriptiveness of captions?

Accepted Answer

Use the Dense Captioning feature.. Option C is correct because using the 'dense' captioning feature provides more detailed descriptions. Option A is wrong because increasing confidence threshold reduces false positives but does not improve descriptiveness. Option B is wrong because object detection provides tags, not captions. Option D is wrong because Custom Vision can be trained for image classification, not captioning.

Answer

Use the Object Detection API instead.

Answer

Train a custom model with Custom Vision.

Answer

Increase the confidence threshold for captions.

Question 11

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

Question 12

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 13

Which TWO Azure AI services can be used to detect objects in images?

Accepted Answer

Custom Vision. Option A and B are correct because Custom Vision and Computer Vision Object Detection both support object detection. Option C is wrong because Face API detects faces, not generic objects. Option D is wrong because Document Intelligence extracts text and structure. Option E is wrong because Video Indexer analyzes video, not images.

Answer

Video Indexer

Answer

Face API

Answer

Azure AI Document Intelligence

Question 14

Which THREE factors should be considered when choosing between Azure AI Vision prebuilt models and Custom Vision?

Accepted Answer

Prebuilt models may not have high accuracy for industry-specific objects.. Options A, C, and E are correct. Prebuilt models have limited accuracy for niche domains (A). Custom Vision allows training on specific objects (C). Prebuilt models cover common objects, but Custom Vision is needed for custom categories (E). Option B is wrong because Custom Vision also supports images from various sources. Option D is wrong because prebuilt models do not require training data.

Answer

Custom Vision cannot process images from real-time video feeds.

Answer

Prebuilt models require a large training dataset.

Question 15

Which TWO Azure AI services can perform optical character recognition (OCR)?

Accepted Answer

Azure AI Document Intelligence. Options A and D are correct. The Read API provides OCR for images and documents. Azure AI Document Intelligence includes OCR for documents. Option B is wrong because Face API does not do OCR. Option C is wrong because Custom Vision is for image classification. Option E is wrong because Video Indexer extracts text from video, but the question is about OCR in general; however, the Read API and Document Intelligence are the primary OCR services.

Answer

Custom Vision

Answer

Face API

Answer

Video Indexer