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

Question 1

Your company wants to moderate user-uploaded images for adult content. Which Azure AI service should you use?

Accepted Answer

Azure AI Content Safety. Azure AI Content Safety is the correct service because it is specifically designed to detect and moderate inappropriate content, including adult content, in images and text. It provides severity-based classifications (safe, low, medium, high) for categories such as hate, self-harm, sexual, and violence, making it ideal for user-uploaded image moderation.

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Azure AI Face

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Azure AI Document Intelligence

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Azure AI Vision Image Analysis

Question 2

You are building a mobile app that allows users to take a photo of a product and get detailed information. The app uses Azure AI Custom Vision to classify products. You need to ensure low latency for inference. What should you do?

Accepted Answer

Export the Custom Vision model as a TensorFlow model and run on-device. Exporting the Custom Vision model as a TensorFlow model and running it on-device eliminates network latency entirely. Inference happens locally on the mobile device, which provides the lowest possible latency for real-time classification, especially when network connectivity is poor or inconsistent.

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Increase the number of training iterations

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Use the Azure AI Vision API directly

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Use Azure Front Door to cache results

Question 3

You are implementing a solution that uses Azure AI Vision to analyze images in a batch processing pipeline. The pipeline processes thousands of images daily. You need to minimize cost while ensuring that all images are processed within 24 hours. Which service tier should you choose?

Accepted Answer

Standard S1 tier. The Standard S1 tier is the correct choice because it provides the necessary throughput (10 transactions per second) and capacity to process thousands of images within 24 hours at the lowest cost among the paid tiers. The Free F0 tier is limited to 20 transactions per minute and 5,000 transactions per month, which is insufficient for batch processing at scale. The S0 and S2 tiers are either deprecated or offer higher throughput at a higher cost, making S1 the most cost-effective option for this workload.

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Free tier F0

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Standard S0 tier

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Standard S2 tier

Question 4

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. Azure AI Custom Vision with object detection allows you to train a model to identify defects in images, and its retraining capability enables the model to adapt to new defect types without starting from scratch, meeting the near real-time requirement. This approach is specifically designed for custom visual inspection tasks like defect detection on a conveyor belt.

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Use Azure AI Face to detect anomalies

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Use Azure AI Video Indexer to analyze video feeds

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

Question 5

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. Azure AI Translator is correct because invoices arrive in various languages, and translating extracted text to a common language (e.g., English) is necessary for downstream processing like entity extraction and validation. Without translation, multilingual invoice data would be inconsistent or unprocessable by language-specific models.

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Azure AI Custom Vision

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Azure AI Content Safety

Question 6

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 Video Indexer can extract insights from video feeds, including detecting and counting people in a retail store by analyzing recorded or live video streams. Azure AI Vision Spatial Analysis provides real-time computer vision capabilities specifically designed for counting people and measuring foot traffic in physical spaces, such as retail environments. Together, these services enable both historical analysis (Video Indexer) and real-time monitoring (Spatial Analysis) of people flow.

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Azure AI Content Safety

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Azure AI Document Intelligence

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Azure AI Face

Question 7

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

Use Azure AI Vision Spatial Analysis to detect defects in real-time by analyzing video feeds. Azure AI Vision Spatial Analysis is designed for analyzing human activities and movements, not for detecting product defects. It typically runs as a container on edge devices, contradicting the requirement for fully managed services. Using Azure AI Custom Vision as a managed service (without exporting to a container) would meet the requirements of real-time defect detection, continuous learning, and low latency, but none of the options provide this. Therefore, no option is correct.

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

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Use Azure AI Document Intelligence to analyze images of products

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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

Question 8

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. The Read API is the correct choice because it is specifically designed to extract text from images containing both printed and handwritten text, using advanced OCR capabilities that support mixed content. Unlike the OCR API, which is optimized for printed text only, the Read API leverages deep learning models to handle varied handwriting styles and complex layouts, making it ideal for analyzing customer review images.

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Custom Vision

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OCR API (optical character recognition)

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Azure AI Document Intelligence

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.. Training a custom object detection model with annotated images that include small objects directly improves the model's ability to detect them. Option A is wrong because preprocessing to remove background noise does not specifically target small object detection; the model may still miss small items. Option C is wrong because the Background Removal API is used for isolating items from the background, not for improving detection accuracy. Option D is wrong although higher resolution can help, it is not as effective as training a custom model with properly annotated small objects, and it may increase cost and latency.

Answer

Preprocess images to remove background noise.

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Use the Background Removal API to isolate items.

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Increase the image resolution before sending to the API.

Question 10

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. The Analyze Image API in Azure AI Vision includes built-in moderation categories for detecting adult, racy, and gory content in images. This feature is specifically designed for content moderation without requiring custom training, making it the correct choice for moderating user-generated images for adult content.

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Custom Vision with a custom adult classifier

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Face API

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OCR

Question 11

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.. Data augmentation artificially expands the small dataset (200 images per class) by applying transformations like rotation, scaling, and flipping, which helps the model generalize better. Transfer learning with a pre-trained model (e.g., ResNet or EfficientNet) leverages features learned from large datasets like ImageNet, allowing the Custom Vision model to achieve higher accuracy with limited data.

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Reduce the image dimensions to speed up training.

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Add more negative samples to the dataset.

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Increase the number of training iterations significantly.

Question 12

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.. Under GDPR, biometric data (such as facial recognition templates) is classified as special category data requiring explicit consent for processing. The Face API itself does not manage consent; the responsibility lies with the application layer. Option C is correct because obtaining explicit consent from each employee before enrollment is a fundamental GDPR requirement for lawful processing of biometric data.

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

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Anonymize the face data by blurring key features.

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Use encryption for stored face templates.

Question 13

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.. The Dense Captioning feature in the Computer Vision Image Analysis API generates one-sentence descriptions for each of up to 10 regions detected in an image, providing more specific and detailed captions than the single generic caption. This directly addresses the problem of captions being too generic by breaking the image into meaningful areas and describing each one individually.

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Use the Object Detection API instead.

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Train a custom model with Custom Vision.

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Increase the confidence threshold for captions.

Question 14

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. Azure AI Document Intelligence (formerly Form Recognizer) is the correct choice because it is specifically designed to extract key-value pairs, tables, and structured data from scanned documents like invoices. Unlike the Read API or OCR API, which only return raw text or OCR output, Document Intelligence uses prebuilt models (e.g., 'prebuilt-invoice') that understand the semantic layout of invoices, enabling direct extraction of fields such as invoice number, date, and total amount.

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Read API

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Custom Vision

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OCR API

Question 15

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).. B is correct because the Face API's similarity scoring is heavily influenced by lighting conditions. Variations in illumination can alter facial features, shadows, and contrast, which reduces the accuracy of face matching algorithms. The API relies on consistent lighting to extract reliable facial landmarks and embeddings, so differing lighting conditions directly lower similarity scores.

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The images are compressed with different quality levels.

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The backgrounds of the images are different.

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