AI-102 Implement computer vision solutions Practice Test 6 — 15 Questions

Question 1

You are a data scientist at a healthcare startup. You have deployed a custom object detection model using Azure Custom Vision to detect tumors in MRI scans. The model was trained on 10,000 labeled scans from a single hospital. After deployment, the model performs well on scans from that hospital but poorly on scans from a different hospital with a different MRI machine. The new hospital's scans have slightly different contrast and resolution. The model's precision drops from 0.92 to 0.65, and recall drops from 0.88 to 0.50. You have access to 500 labeled scans from the new hospital. You need to improve the model's performance on the new hospital's data as quickly as possible with minimal effort. What should you do?

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

Accepted Answer

Use the existing model as a starting point and retrain it with the 500 labeled scans from the new hospital.. Azure Custom Vision supports transfer learning, allowing you to take an existing trained model and retrain it with new labeled data. By using the 500 labeled scans from the new hospital as a training set, you can fine-tune the model to adapt to the different contrast and resolution characteristics without starting from scratch. This approach is the fastest and requires minimal effort, leveraging the previously learned features while incorporating domain-specific adjustments.

Answer

Collect more labeled scans from the new hospital and train a new model from scratch.

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Create a new Custom Vision project and train only on the 500 new scans.

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Apply image preprocessing to normalize the new hospital's scans to match the old hospital's style, then use the existing model.

Question 2

A retail company uses Azure Computer Vision to analyze customer traffic in stores. They process images from security cameras using the OCR API to detect product labels. Recently, the OCR accuracy has decreased for images with poor lighting. Which pre-processing step should the company implement to improve OCR accuracy?

Accepted Answer

Adjust brightness and contrast of images using image processing.. Poor lighting directly reduces the contrast between text and background, which is critical for OCR accuracy. Adjusting brightness and contrast improves the signal-to-noise ratio of the text regions, making character edges more distinct for the Azure Computer Vision OCR engine. This pre-processing step compensates for the lighting deficiency without altering the fundamental image content that the API relies on.

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Convert images to grayscale before sending to OCR API.

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Increase the image resolution before calling OCR API.

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Reduce image size to decrease noise.

Question 3

Which THREE actions can be performed using the Azure Custom Vision service?

Accepted Answer

Export a trained model to ONNX format for offline inference.. Option B is correct because Azure Custom Vision supports exporting trained models in several formats, including ONNX, TensorFlow, CoreML, and Docker, enabling offline or edge inference. Option C is correct because Custom Vision is designed for image classification, allowing you to train a model that assigns images to labeled classes such as product types. Option D is correct because Custom Vision also supports object detection, which returns bounding boxes and labels for multiple objects within an image. Option A is not correct because extracting text from scanned receipts is an OCR task handled by Azure AI Vision (Computer Vision Read API) or Document Intelligence, not Custom Vision. Option E is not correct because identifying specific individuals via facial recognition is provided by Azure AI Face, not Custom Vision.

Answer

Extract text from scanned receipts.

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Identify specific individuals in a crowd using facial recognition.

Question 4

Refer to the exhibit. An Azure Cognitive Services Computer Vision API call for image captioning is returning only one caption. The developer wants to get three possible captions ranked by confidence. Which parameter should be modified in the request?

Accepted Answer

Set the maxCandidates value to 3.. The `maxCandidates` parameter in the Computer Vision Image Analysis API controls the maximum number of captions returned in the response. By default, this value is 1, so only the top-ranked caption is returned. Setting `maxCandidates=3` instructs the API to return up to three captions, each with its own confidence score, ranked from highest to lowest confidence.

Answer

Use a different API version, such as 2023-04-01.

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Modify the URL to point to a different image.

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Change the language parameter to 'multi'.

Question 5

Which TWO Azure AI services can be used to extract text from images and PDFs? (Select two.)

Accepted Answer

Azure AI Vision OCR. Azure AI Vision OCR (option C) is correct because its Read/OCR capability extracts printed and handwritten text directly from images and scanned documents. Azure AI Document Intelligence (option D) is correct because it uses prebuilt and custom models to extract text, key-value pairs, and tables from PDFs, images, and forms. Azure AI Translator (A) performs text translation, not optical character recognition, so it cannot extract text from images or PDFs. Azure AI Search (B) is a search indexing service that can consume extracted text but does not itself perform OCR on images or PDFs. Azure AI Language (E) provides NLP features such as sentiment analysis and entity recognition on existing text, not text extraction from images or PDFs.

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Azure AI Translator

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Azure AI Search

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Azure AI Language

Question 6

Which TWO Azure services can be used to perform optical character recognition (OCR) on documents? (Select two.)

Accepted Answer

Azure AI Document Intelligence. Azure AI Document Intelligence (option C) is correct because it is the Azure service purpose-built for document processing, and its prebuilt Read and Layout models extract printed and handwritten text from documents and images using OCR. Azure AI Vision (option E) is also correct because its Image Analysis and Read capabilities include an OCR engine that extracts text from images and documents via the Read API. Azure AI Metrics Advisor (option A) is an anomaly-detection service for time-series data and performs no OCR. Azure AI Language (option B) provides natural language processing such as sentiment analysis, key phrase extraction, and entity recognition, not optical character recognition. Azure AI Personalizer (option D) is a reinforcement-learning-based recommendation service and has no OCR functionality.

Answer

Azure AI Metrics Advisor

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Azure AI Language

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Azure AI Personalizer

Question 7

You are developing a solution that uses Azure AI Video Indexer to analyze surveillance videos for suspicious activity. The solution must generate alerts when a person is detected in a restricted area. Which feature should you use?

Accepted Answer

Azure AI Video Indexer people detection and tracking. Azure AI Video Indexer's people detection and tracking feature is specifically designed to detect and track individuals across video frames, making it ideal for generating alerts when a person enters a restricted area. This feature provides bounding boxes, timestamps, and tracking IDs that enable real-time monitoring and alerting based on spatial rules.

Answer

Azure AI Video Indexer sentiment analysis

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Azure AI Face identify API

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Object detection in Azure AI Vision

Question 8

Which THREE are valid uses of Azure AI Vision Image Analysis 4.0? (Select three.)

Accepted Answer

Extract printed text from an image using OCR. Option A is correct because Image Analysis 4.0 includes a Read OCR feature that extracts printed and handwritten text from images and documents, returning lines and words. Option C is correct because the object detection capability identifies objects in an image and returns their coordinates as bounding boxes along with confidence scores. Option D is correct because Image Analysis 4.0 can generate captions (and dense captions) describing the content of an image in natural language. Option B is not valid because audio transcription is a speech service capability (e.g., Azure AI Speech), not Image Analysis. Option E is not valid because translating text is performed by Azure AI Translator; Image Analysis only extracts the text via OCR, it does not translate it.

Answer

Transcribe spoken audio from a video file

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Translate text found in an image to another language

Question 9

You need to build a solution that detects whether a person is wearing a hard hat in a construction site image. Which Azure AI service should you use?

Accepted Answer

Azure AI Custom Vision. Azure AI Custom Vision is the correct service because it allows you to train a custom object detection model to identify specific objects—such as a hard hat—in images. Unlike pre-built services, Custom Vision lets you upload labeled images of workers with and without hard hats, train a model, and then use it to detect hard hat presence in construction site photos. This tailored approach is necessary because hard hat detection is a specialized use case not covered by generic vision APIs.

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Azure AI Video Indexer

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Azure AI Face

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Azure AI Document Intelligence

Question 10

You are developing a solution to detect defects on a manufacturing assembly line using computer vision. The solution must classify images as 'defective' or 'non-defective'. You have a limited set of labeled images (500 per class). Which approach should you recommend?

Accepted Answer

Use Azure AI Custom Vision with image classification. Azure AI Custom Vision with image classification is the best choice because it allows you to fine-tune a pre-trained deep learning model on your limited dataset (500 images per class) to classify images as 'defective' or 'non-defective'. This approach requires minimal data and expertise compared to training from scratch, and it is specifically designed for custom classification tasks with small datasets.

Answer

Use Azure AI Vision Image Analysis with a pre-built model

Answer

Use Azure AI Custom Vision with object detection

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Train a deep learning model from scratch using Azure Machine Learning

Question 11

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

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Azure AI Vision OCR and Azure AI Translator

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Azure AI Speech and Azure AI Translator

Question 12

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

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Azure AI Face API

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Azure AI Custom Vision with object detection

Question 13

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

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Increase the number of training epochs

Question 14

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 its Read model is specifically designed to extract printed and handwritten text from documents and images, returning lines, words, and page layout via the Analyze Document operation. Azure AI Vision OCR (Read API) (option E) is also correct because the Read API in Azure AI Vision performs OCR on images and PDFs, returning extracted text and bounding boxes asynchronously. Azure Video Indexer (option B) focuses on video/audio insights such as transcription and face tracking, not general image OCR. Azure AI Custom Vision (option C) is an image classification and object detection service, not an OCR text extractor. Azure AI Face API (option D) detects and analyzes faces (landmarks, attributes), and does not perform optical character recognition.

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Azure Video Indexer

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Azure AI Custom Vision

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Azure AI Face API

Question 15

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)

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Need for real-time inference latency