AI-102 Implement computer vision solutions Practice Test 3 — 15 Questions

Question 1

An application uses Azure AI Vision to analyze images and extract text. The application crashes when processing images with embedded barcodes. You suspect the issue is related to the image pre-processing. Which step should you add to the pipeline to resolve the issue?

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

Accepted Answer

Detect and remove barcodes from the image before OCR. Azure AI Vision's OCR engine is designed to extract text from natural images and documents, but embedded barcodes can introduce noise or unexpected patterns that interfere with the text detection algorithm. By detecting and removing barcodes from the image before OCR, you eliminate this interference, allowing the OCR engine to focus on textual content without crashing or producing erroneous results.

Answer

Increase the image contrast before sending to the OCR engine

Answer

Add more training data with barcodes to the OCR model

Answer

Resize the image to a smaller resolution to reduce barcode impact

Question 2

You need to analyze a live video stream from a security camera to detect people entering a restricted area. Which Azure AI service should you use?

Accepted Answer

Azure Video Indexer. Azure Video Indexer supports live video stream analysis for detecting people in restricted areas using pre-built AI models. It can process live streams and generate alerts in real time. Azure Video Analyzer for Media is a deprecated predecessor and is not the optimal choice. Custom Vision requires custom model training, and Face API focuses on facial recognition rather than general person detection.

Answer

Azure AI Custom Vision

Answer

Azure Video Analyzer for Media (deprecated)

Answer

Azure AI Face API

Question 3

You are designing a solution that extracts information from scanned invoices. The solution must automatically classify invoices by vendor and extract key fields (total amount, date, invoice number). Which combination of Azure AI services should you use?

Accepted Answer

Azure AI Document Intelligence custom classification and extraction models. Azure AI Document Intelligence (formerly Form Recognizer) allows you to build custom classification models to identify the vendor from the invoice layout, and custom extraction models to pull key fields like total amount, date, and invoice number. This combination handles both the classification and extraction requirements in a single service, using labeled training data for high accuracy.

Answer

Azure AI Vision OCR and Azure AI Language

Answer

Azure AI Form Recognizer (deprecated) with custom models

Answer

Azure AI Document Intelligence pre-built invoice model

Question 4

An application uses Azure AI Face API to perform face detection and verification. The application must ensure that only users with verified identities can access sensitive data. Which additional Azure service should you integrate to comply with Microsoft's Responsible AI standards for facial recognition?

Accepted Answer

Microsoft Entra ID and Face API Limited Access. D is correct because Microsoft's Responsible AI standards for facial recognition require that applications using the Face API for identity verification must be backed by a verified identity system like Microsoft Entra ID (formerly Azure AD) and must apply for the Face API Limited Access policy. This ensures that only authorized users with verified identities can access sensitive data, aligning with Microsoft's ethical guidelines for facial recognition technology.

Answer

Azure Video Indexer

Answer

Azure AI Content Safety

Answer

Azure AI Vision Image Analysis

Question 5

You need to build a solution that reads text from images in multiple languages, including Arabic and English, and translates the text into English. The solution must preserve the original layout as much as possible. Which combination of Azure AI services should you use?

Accepted Answer

Azure AI Document Intelligence Read and Azure AI Translator. Azure AI Document Intelligence Read (formerly Form Recognizer Read) is optimized for extracting text from images and documents while preserving the original layout, including bounding box coordinates for each text element. Azure AI Translator then translates the extracted text into English. This combination meets the requirement for multi-language OCR (including Arabic and English) and layout preservation.

Answer

Azure AI Document Intelligence Read and Azure AI Language

Answer

Azure AI Vision OCR and Azure AI Translator

Answer

Azure AI Speech and Azure AI Translator

Question 6

You are developing a mobile app that allows users to take a photo of a product and get information about it. The app must identify the product from the image. Which Azure AI service should you use?

Accepted Answer

Azure AI Custom Vision with image classification. Azure AI Custom Vision with image classification is specifically designed to identify and categorize products or objects within an image based on trained labels. This service allows you to upload images of products, train a model to recognize them, and then use the model to classify new product photos, making it ideal for a product identification app.

Answer

Azure AI Vision OCR

Answer

Azure AI Face API

Answer

Azure AI Custom Vision with object detection

Question 7

You are deploying a computer vision model using Azure AI Custom Vision with a small dataset of 200 images per class. The model shows high accuracy on training data but low accuracy on test data. Which action should you take to reduce overfitting?

Accepted Answer

Increase the training dataset size with more varied images. Increase the training dataset size with more varied images. Overfitting occurs when the model learns noise from a small dataset. Adding more varied images helps the model generalize. Option A (increase learning rate) is wrong because it may cause divergence or unstable training, not directly reduce overfitting. Option B (reduce image size) is wrong because it can lose important features and may even increase overfitting. Option C (increase training epochs) is wrong because it can actually increase overfitting by allowing the model to memorize more noise.

Answer

Increase the learning rate

Answer

Reduce the image size to lower resolution

Answer

Increase the number of training epochs

Question 8

Which TWO Azure AI services can be used to perform optical character recognition (OCR) on images? (Choose two.)

Accepted Answer

Azure AI Document Intelligence Read model. Azure AI Document Intelligence Read model (option A) is correct because it is specifically designed to extract printed and handwritten text from images and documents using OCR. It leverages deep learning models to analyze text layout, including lines and words, and is optimized for document-centric OCR tasks.

Answer

Azure Video Indexer

Answer

Azure AI Custom Vision

Answer

Azure AI Face API

Question 9

Which THREE factors should you consider when choosing between Azure AI Custom Vision and Azure AI Vision pre-built models for an image classification task? (Choose three.)

Accepted Answer

Availability of labeled training data specific to the domain. Azure AI Custom Vision is specifically designed for scenarios where you have domain-specific labeled training data that is not covered by pre-built models. Custom Vision allows you to upload your own labeled images and train a custom model tailored to your unique classification needs, which is essential when off-the-shelf models fail to recognize your target classes.

Answer

Image format support (JPEG, PNG)

Answer

Need for real-time inference latency

Question 10

Which TWO Azure AI services provide capabilities to detect and analyze faces in images? (Choose two.)

Accepted Answer

Azure AI Face API. Azure AI Face API (B) is correct because it is specifically designed for face detection, recognition, and analysis, including attributes like age, emotion, and landmarks. Azure AI Vision Image Analysis (D) is correct because it includes the 'Detect Faces' capability within its broader image analysis feature set, allowing extraction of face bounding boxes and attributes without requiring a dedicated Face API resource.

Answer

Azure AI Custom Vision

Answer

Azure AI Document Intelligence

Answer

Azure Video Indexer

Question 11

You are calling the Azure AI Vision Image Analysis API with the above JSON request. The API returns a response that includes tags, objects, and a caption. However, the caption does not describe the image accurately. Which change should you make to improve the caption quality?

Accepted Answer

Set gender-neutral-caption to false. Setting `gender-neutral-caption` to false forces the Azure AI Vision Image Analysis API to generate captions that include gender-specific terms (e.g., 'man' or 'woman') instead of neutral terms like 'person'. This improves caption accuracy when the image clearly depicts a person whose gender is identifiable, because the default (true) may produce a vague description that fails to capture the subject's characteristics.

Answer

Change model-version to a newer version

Answer

Add 'read' feature to the request

Answer

Set language to 'en-US'

Question 12

You have an Azure AI Vision resource named MyVisionService. You run the above Azure CLI command and get the keys. Your application uses key1 for authentication. You need to rotate the keys without downtime. What should you do?

Accepted Answer

Update the application to use key2, then regenerate key1. It enables key rotation without downtime. By first updating the application to use key2 (the secondary key), you ensure that authentication continues to work while key1 is being regenerated. After key1 is regenerated, you can optionally update the application back to key1 at a later time. This pattern is standard for Azure Cognitive Services to maintain continuous access.

Answer

Delete and recreate the Cognitive Services resource

Answer

Regenerate key1 immediately and update the application to use the new key1

Answer

Regenerate both keys at the same time

Question 13

You deploy the above ARM template to create an Azure AI Vision resource. After deployment, you try to call the Vision API from your application but receive a 403 Forbidden error. What is the most likely cause?

Accepted Answer

The network ACLs block all traffic by default. When you deploy an Azure AI Vision resource using an ARM template, the default network configuration sets the 'defaultAction' to 'Deny' for IP firewall rules, meaning all traffic is blocked unless explicitly allowed. Since you did not configure any network ACLs to permit your application's IP address or virtual network, the 403 Forbidden error occurs because the API endpoint rejects the request at the network layer before any authentication or authorization checks.

Answer

The apiVersion in the template is incorrect

Answer

The custom subdomain name is not configured correctly

Answer

The SKU 'S1' does not support API calls

Question 14

You are developing an application that processes images of handwritten forms. The forms contain checkboxes that may be checked or unchecked. Which Azure AI service should you use to detect the state of the checkboxes?

Accepted Answer

Azure AI Document Intelligence. Azure AI Document Intelligence (formerly Form Recognizer) is the correct service because it is specifically designed to extract structured data from documents, including detecting the state of checkboxes (checked or unchecked) in forms. It uses prebuilt models like the 'prebuilt-document' or custom extraction models to analyze form fields and checkbox selections, making it the optimal choice for this task.

Answer

Azure AI Custom Vision

Answer

Azure AI Language

Answer

Azure AI Computer Vision

Question 15

A manufacturing company uses Azure AI Custom Vision to detect defects on a production line. The model was trained with 500 images per class and achieves 95% accuracy. After deployment, the model's accuracy drops to 80% due to changes in lighting conditions. What is the most effective first step to improve the model's robustness?

Accepted Answer

Capture additional images under the new lighting and retrain the model.. The drop in accuracy is caused by a domain shift—specifically, new lighting conditions that were not represented in the original training set. The most effective first step is to capture additional images under the new lighting and retrain the model, as Custom Vision relies on diverse, representative training data to generalize to real-world variations. This directly addresses the root cause by expanding the training distribution to include the new lighting scenario, which is a fundamental principle of supervised learning in computer vision.

Answer

Reduce the probability threshold to increase recall.

Answer

Use Azure AutoML to automatically find the best algorithm.

Answer

Add more images from the original lighting conditions to the training set.