AI-102 Implement image and video processing solutions Practice Test 1 — 15 Questions

Question 1

A retail company uses Azure Computer Vision to analyze in-store camera feeds. They recently added a new product line and updated the object detection model. However, the model fails to detect the new products. What should the company do first?

Accepted Answer

Retrain the custom object detection model with images of the new products.. The model fails to detect new products because it was never trained on them. Retraining the custom object detection model with labeled images of the new products is the correct first step, as it updates the model's knowledge to recognize the new product line. Pre-built models or threshold adjustments cannot add new object classes.

Answer

Use the pre-built 'products' model from Computer Vision.

Answer

Increase the confidence threshold in the API call.

Answer

Recreate the Computer Vision resource in a different region.

Question 2

A healthcare provider uses Azure Video Analyzer for Media to extract insights from surgical videos. They need to ensure that no patient health information (PHI) is stored in the transcriptions. What is the best approach?

Accepted Answer

Enable content moderation in Video Analyzer for Media settings.. Option C is correct because enabling content moderation in Azure Video Analyzer for Media automatically redacts or flags sensitive content, including PHI, from transcriptions during the indexing process. This built-in feature ensures that PHI is not stored in the output without requiring additional post-processing or manual deletion.

Answer

Use Azure AI Content Safety to post-process transcriptions.

Answer

Disable indexing for videos containing PHI.

Answer

Use the 'delete' API to remove all transcripts after processing.

Question 3

A company uses Azure Custom Vision to classify images of defective parts. After deploying the model, the accuracy is low. The team only has 10 images per class. What is the most effective way to improve accuracy?

Accepted Answer

Add at least 50 more images per class with variations.. Option B is correct because Azure Custom Vision relies on deep learning models that require a sufficient number of diverse training images to generalize well. With only 10 images per class, the model is severely underfit and prone to overfitting; adding at least 50 more images per class with variations in lighting, angle, and background provides the necessary data diversity to improve accuracy significantly.

Answer

Use a different classification algorithm.

Answer

Reduce the image resolution to speed up training.

Answer

Increase the number of training iterations (epochs).

Question 4

A news organization uses Azure Video Indexer to generate transcripts of live broadcasts. They notice that the speaker names are not appearing in the transcript. What is the most likely cause?

Accepted Answer

The speaker identification model has not been trained with voice samples.. Speaker names are missing because Azure Video Indexer's speaker identification feature requires pre-trained voice samples to match speakers to their identities. Without a custom voice model trained on known speakers' audio, the service can only label speakers as 'Speaker #1', 'Speaker #2', etc., but cannot assign actual names. This is a supervised learning process where the model must be trained with labeled voice samples before it can recognize and name speakers.

Answer

The video resolution is too low for OCR.

Answer

The video format is not supported.

Answer

The language is not set correctly.

Question 5

A company uses Azure Face API to detect faces in a crowd. They need to comply with GDPR and delete face data after 30 days. What should they implement?

Accepted Answer

Use the Face API Delete operation to remove stored face IDs.. Option C is correct because the Azure Face API stores face data (e.g., face IDs) in a PersonGroup or FaceList, and GDPR compliance requires the ability to delete personal data on demand. The Face API provides a dedicated Delete operation (e.g., FaceList - Delete Face or PersonGroup Person - Delete) to remove stored face IDs and associated face data, enabling the 30-day deletion requirement without recreating the resource.

Answer

Enable data encryption at rest.

Answer

Set a retention policy on the Face API resource.

Answer

Recreate the Face API resource every 30 days.

Question 6

A developer is building a mobile app that uses Azure Computer Vision to analyze images. The app needs to handle many requests with low latency. Which pricing tier should they choose?

Accepted Answer

Computer Vision S1 tier.. The Computer Vision S1 tier is designed for production workloads requiring high throughput and low latency, making it suitable for a mobile app that handles many image analysis requests. Unlike the Free F0 tier, which has strict rate limits (e.g., 20 transactions per minute), the S1 tier offers higher transactions per second (e.g., up to 10 TPS) and guaranteed performance for real-time scenarios.

Answer

Video Analyzer S1 tier.

Answer

Free F0 tier.

Answer

Custom Vision S0 tier.

Question 7

A company uses Azure Form Recognizer to extract data from invoices. The extracted data contains many errors for a specific vendor's invoices. What should they do?

Accepted Answer

Custom train a model with labeled examples of that vendor's invoices.. Option D is correct because Azure Form Recognizer's prebuilt invoice model may not generalize well to vendor-specific layouts or data formats. By custom training a model with labeled examples of that vendor's invoices, you adapt the extraction to the unique fields, tables, and formatting, significantly reducing errors. This leverages the service's supervised learning capability to improve accuracy for domain-specific documents.

Answer

Use a different prebuilt model.

Answer

Disable the OCR step.

Answer

Increase the confidence threshold.

Question 8

A security company uses Azure Video Analyzer on IoT Edge to detect intrusions. The edge device has limited compute and network. They need to reduce latency. What should they configure?

Accepted Answer

Set a high minimum confidence threshold for detection.. Option B is correct because setting a high minimum confidence threshold for detection reduces the number of false positives and the volume of events that need to be processed and transmitted. This directly lowers the computational load on the edge device and reduces network bandwidth usage, thereby decreasing latency for actionable intrusion alerts.

Answer

Increase the video resolution sent to the cloud.

Answer

Enable cloud-based processing for all frames.

Answer

Use multiple AI models simultaneously.

Question 9

Which TWO actions can reduce the cost of using Azure Custom Vision for image classification? (Choose two.)

Accepted Answer

Use the compact domain for faster training.. Option B is correct because using the compact domain in Azure Custom Vision reduces model complexity and training time, which directly lowers compute costs. Compact domains are optimized for edge deployment and require fewer resources, making them more cost-effective for image classification tasks.

Answer

Include negative samples in the dataset.

Answer

Use a larger image size for higher accuracy.

Answer

Increase the number of training iterations.

Question 10

Which THREE are required to build a custom video indexing solution using Azure Video Analyzer for Media? (Choose three.)

Accepted Answer

An Azure Video Analyzer for Media account.. An Azure Video Analyzer for Media account is required because it is the core service that provides the video indexing capabilities, including AI-powered insights like speech transcription, face detection, and sentiment analysis. Without this account, you cannot access the indexing APIs or manage indexed videos.

Answer

A custom indexing pipeline.

Answer

A custom language model.

Question 11

Which TWO Azure services can be used to detect and redact PII from images containing text? (Choose two.)

Accepted Answer

Azure AI Content Safety.. Azure AI Content Safety can detect and redact PII from images containing text by analyzing the extracted text for sensitive information such as personal identifiers and applying redaction. Azure Computer Vision (OCR) provides the underlying text extraction capability, and when combined with Content Safety's PII detection, enables end-to-end redaction of PII from images.

Answer

Azure Form Recognizer.

Answer

Azure Video Analyzer for Media.

Answer

Azure Cognitive Search.

Question 12

Which THREE factors should be considered when choosing between Azure Computer Vision and Azure Custom Vision? (Choose three.)

Accepted Answer

The need for custom model retraining over time.. Option A is correct because Azure Custom Vision is specifically designed for scenarios where you need to retrain a model over time with new labeled data, such as when the visual characteristics of objects change (e.g., new product packaging). Azure Computer Vision is a pre-trained API that cannot be retrained; it only supports fixed, generic models. Custom Vision allows iterative training with your own images, making it essential when model drift or evolving requirements demand periodic retraining.

Answer

Whether the solution runs on edge devices.

Answer

The geographic region of the Azure subscription.

Question 13

You are the Azure AI engineer for a large e-commerce company. The company uses Azure Computer Vision to automatically tag product images uploaded by sellers. The system has been running smoothly for months. However, after a recent update to the Computer Vision API, you notice that certain images of clothing items are being tagged with incorrect labels, such as 'shoe' for a shirt. The images are clear and well-lit. You have confirmed that the image format (JPEG) is supported and the size is within limits. The issue occurs consistently for clothing items with similar colors. Other product categories work fine. You suspect the issue is related to the API version. What should you do first?

Accepted Answer

Check the API version used in the application code and compare with the latest version.. Option B is correct because the issue began after a Computer Vision API update, and the problem is specific to certain clothing images with similar colors, indicating a potential regression or behavioral change in the API version. Checking the API version used in the application code against the latest version is the first logical troubleshooting step to identify if a breaking change or bug was introduced. This aligns with Azure AI best practices: always verify API version compatibility before modifying thresholds or retraining models.

Answer

Increase the image size limit.

Answer

Switch to a custom model trained on clothing items.

Answer

Reduce the confidence threshold to 50% to see if more tags appear.

Question 14

Drag and drop the steps to configure an Azure AI Search index with a custom skill into the correct order.

Question 15

Drag and drop the steps to configure an Azure AI Vision custom image classification model into the correct order.