AI-102 Implement computer vision solutions Practice Test 1 — 15 Questions

Question 1

You are designing a computer vision solution that must detect and redact personally identifiable information (PII) from images of ID cards. The solution must comply with GDPR. Which THREE components should you include? (Select three.)

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

Accepted Answer

Azure AI Document Intelligence redaction. Correct options: A, C, D. Azure AI Vision OCR (C) extracts text from ID card images. Azure AI Language PII detection (D) identifies PII entities in the extracted text. Azure AI Document Intelligence redaction (A) can redact the identified PII from the document. Option B (Azure AI Custom Vision) is used for custom image classification, not for text-based PII detection. Option E (Azure AI Video Indexer) is for analyzing video content, not static ID cards.

Answer

Azure AI Custom Vision

Answer

Azure AI Video Indexer

Question 2

You need to analyze a video stream from a security camera to count the number of people entering a building. Which Azure AI service is most suitable?

Accepted Answer

Azure AI Spatial Analysis. Azure AI Spatial Analysis is the most suitable service because it is purpose-built for real-time spatial analysis of video streams, including counting people entering a building. It can process live camera feeds and detect when people cross a line or enter a zone, providing real-time counts. In contrast, Azure AI Video Indexer is designed for analyzing recorded video files, not live streams, making it less suitable for real-time security camera analysis. Other options like Custom Vision and Computer Vision are for image classification and image analysis, respectively, not optimized for video streams.

Answer

Azure AI Custom Vision

Answer

Azure AI Computer Vision

Answer

Azure AI Video Indexer

Question 3

Your retail company uses Azure AI Face API to identify VIP customers when they enter a store. Due to privacy regulations, you must obtain explicit consent and store minimal biometric data. Which approach meets these requirements?

Accepted Answer

Create a PersonGroup with each VIP customer, and use the Identify operation.. The Face API's PersonGroup and Identify operation allow you to create a group of VIP customers with minimal biometric data: each person is represented by a unique person ID derived from their face features, not the actual image. This complies with privacy regulations requiring minimal data storage. Option A is wrong because training a Custom Vision model requires storing and processing images, which retains excessive biometric data. Option B is wrong because Face ID is a temporary identifier that expires after 24 hours, making it unsuitable for long-term VIP identification. Option C is wrong because storing face images in Blob Storage retains the actual image, violating the requirement to minimize biometric data.

Answer

Train a Custom Vision model with images of VIP customers.

Answer

Use the Face API's Face ID returned upon detection and store it in a database.

Answer

Store the customer's face image in Azure Blob Storage and compare using the Face API.

Question 4

Refer to the exhibit. You are configuring an Azure AI Video Indexer job. The exhibit shows a JSON snippet of the job configuration. What will Video Indexer extract from the video?

Accepted Answer

OCR text, detected faces, and visual labels. The JSON snippet configures an Azure Video Indexer job with the 'features' array set to include 'ocr', 'faces', and 'labels'. This instructs Video Indexer to extract OCR text (from on-screen text), detected faces (via facial detection and recognition), and visual labels (objects, scenes, and actions). These are the only features specified, so the output will contain exactly those three types of insights.

Answer

Sentiment analysis of spoken content

Answer

Audio transcript and speaker diarization

Answer

Keyframe extraction only

Question 5

Refer to the exhibit. You are configuring the Azure AI Vision Analyze Image API. You need to ensure that the response includes a list of detected objects and brands. Which parameter is missing from the request?

Accepted Answer

Add 'model-version' with value '2023-10-01' to specify a stable version.. The Analyze Image API requires a stable model version to reliably return detected objects and brands. Without specifying 'model-version' with a value like '2023-10-01', the API may use a preview or default version that does not support brand detection or may return inconsistent results. Adding this parameter ensures the response includes the 'objects' and 'brands' visual features as expected.

Answer

Add 'smartcrops' to visualFeatures.

Answer

Add 'gender-neutral-caption' parameter set to true.

Answer

Remove 'Language' parameter because it is not supported.

Question 6

A retail company uses Azure AI Vision to analyze in-store video feeds. They want to detect when employees are not wearing required safety vests. The solution must send real-time alerts to store managers. Video feeds are captured from existing CCTV cameras. Which approach should you recommend?

Accepted Answer

Train a Custom Vision classification model and deploy it to the edge on CCTV cameras. To detect objects in real-time from live CCTV feeds, the model must run at the edge with low latency. Custom Vision supports object detection models that can be exported to run on edge devices such as cameras or IoT devices. This enables real-time inference without sending video to the cloud. Azure AI Video Indexer is designed for post-processing indexed videos, not live real-time detection. Azure AI Vision Image Analysis works on single images, not live video streams.

Answer

Use Azure AI Vision Image Analysis with a custom model to analyze each frame offline

Answer

Use Azure AI Video Indexer with a custom object detection model to detect vests in video frames

Answer

Use Azure Video Analyzer for Media to index videos and detect vests after recording

Question 7

A museum wants to create an interactive exhibit where visitors take a photo of a painting and the app provides information about the artist and historical period. The solution must use a pre-built AI service and be deployed on Azure. Which approach should you recommend?

Accepted Answer

Use Azure AI Vision Image Analysis to generate captions for the paintings. Azure AI Vision Image Analysis is a pre-built AI service that can analyze images and return tags, captions, and descriptions. For famous paintings, it may recognize the artist and period from pre-trained models. Option B requires custom training, which is not pre-built. Option C is for video, and D for documents. Therefore, A is the recommended approach.

Answer

Train a Custom Vision classifier with images of paintings labeled by artist and period

Answer

Use Azure AI Video Indexer to analyze the painting image

Answer

Use Azure AI Document Intelligence to extract text from the painting

Question 8

A logistics company uses Azure AI Vision to analyze images of packages on conveyor belts. They need to detect damaged packages and read tracking numbers. The solution must process high throughput (1000 images per minute) with low latency (<500ms per image). The images are captured by fixed cameras. Which approach should you recommend?

Accepted Answer

Use Azure AI Vision OCR Read API for tracking numbers and a separate Custom Vision model for damage detection. It combines Azure AI Vision OCR Read API (for text extraction) with a separate Custom Vision model (for damage detection). This leverages the specialized strengths of each service: the OCR Read API is optimized for text reading with high accuracy, while Custom Vision excels at object detection. The latency requirement (<500ms per image) can be met through parallel processing or edge deployment, and throughput can be achieved by scaling API calls. Option B is incorrect because Custom Vision does not include built-in OCR capability; it cannot read tracking numbers. Options A and C are less suitable: Document Intelligence is designed for structured documents, not real-time package analysis, and Video Indexer is for video streams, not still images.

Answer

Use Azure AI Document Intelligence to process package labels

Answer

Train a single Custom Vision model that detects damage and reads tracking numbers using OCR

Answer

Use Azure AI Video Indexer to analyze the video stream from cameras

Question 9

A retail company uses Azure Computer Vision to analyze customer traffic in stores. They deploy a custom object detection model to count customers and detect occupancy. After deployment, the model consistently underestimates the number of customers during peak hours. The company has retrained the model with more data but the issue persists. What is the most likely cause?

Accepted Answer

The training data does not adequately represent peak-hour scenarios.. The model consistently underestimates customer counts during peak hours, which indicates a distribution shift between the training data and the inference environment. Even after retraining with more data, the issue persists because the additional data likely still lacks sufficient representation of peak-hour scenarios (e.g., high density, occlusion, rapid movement). In Azure Custom Vision, object detection models learn from labeled examples; if the training set does not include diverse peak-hour images with varied lighting, crowd densities, and angles, the model will fail to generalize to those conditions.

Answer

The model is not being batch-processed for inference.

Answer

The model is overfitting to the training data.

Answer

The Computer Vision API version is outdated.

Question 10

A hospital uses Azure Custom Vision to classify X-ray images as normal or abnormal. The model achieves 98% accuracy on the test set. However, during deployment, the model misclassifies many abnormal cases as normal, causing missed diagnoses. The hospital has a class imbalance where abnormal cases are only 5% of the data. What should the data scientist do first to address this?

Accepted Answer

Use oversampling or class-weight techniques to balance the training.. The primary issue is class imbalance, where abnormal cases constitute only 5% of the data. Oversampling (e.g., SMOTE) or class-weight techniques adjust the training process to give more importance to the minority class, directly addressing the model's bias toward the majority class and reducing false negatives. This is a standard preprocessing step in Custom Vision and other ML frameworks before tuning hyperparameters or changing algorithms.

Answer

Increase the number of training epochs.

Answer

Add more normal X-ray images to the dataset.

Answer

Switch to a different object detection algorithm.

Question 11

A company uses Azure Face API to verify employee identities for building access. They need to ensure that only live faces are used, not photos or videos. Which feature should they enable?

Accepted Answer

Enable liveness detection using session-based verification.. Azure Face API's liveness detection with session-based verification is specifically designed to prevent spoofing attacks using photos, videos, or masks. It analyzes subtle cues such as micro-movements, texture, and depth to confirm the presence of a live person, ensuring that only live faces are accepted for identity verification.

Answer

Set a high confidence threshold for face matching.

Answer

Face identification with a large person group.

Answer

Face detection with attributes such as age and emotion.

Question 12

A developer is building an application to extract text from scanned invoices using Azure Computer Vision's Read API. The invoices contain a mix of printed and handwritten text. The developer needs to ensure the highest accuracy for both types. Which parameter should they set in the API call?

Accepted Answer

No special parameter; the Read API automatically handles both.. The Read API in Azure Computer Vision is designed to extract text from images and documents, and it automatically handles both printed and handwritten text without requiring any special parameter. Setting the 'language' parameter to 'en' is optional and only improves accuracy for language-specific text, but it does not enable or disable handwriting recognition. Therefore, no additional parameter is needed to achieve the highest accuracy for both types.

Answer

Set the 'language' parameter to 'en' for English handwriting.

Answer

Specify the 'model-version' as '2022-04-30'

Answer

Use the 'mode' parameter set to 'Handwriting'

Question 13

A company uses Azure Custom Vision to build a classifier for defect detection on a manufacturing line. They have labeled images of products with and without defects. Which TWO actions should they take to improve model performance?

Accepted Answer

Use images with balanced numbers of defect and non-defect samples.. Balanced datasets prevent the model from becoming biased toward the majority class (e.g., non-defect images), which is critical for defect detection where defects are rare. Azure Custom Vision uses a weighted loss function during training, and class imbalance can cause the model to predict the majority class for most inputs, reducing recall for defects. Balanced samples ensure the model learns discriminative features for both classes equally.

Answer

Train for more iterations without validation.

Answer

Set the learning rate manually using the Custom Vision API.

Answer

Reduce the number of images per tag to avoid overfitting.

Question 14

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?

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.

Answer

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

Answer

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

Question 15

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.

Answer

Convert images to grayscale before sending to OCR API.

Answer

Increase the image resolution before calling OCR API.

Answer

Reduce image size to decrease noise.