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

Question 1

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The Computer Vision API version is outdated.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 2

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.. Option D is correct because 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 3

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.. Option C is correct because 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 4

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 5

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.. Option B is correct because 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 6

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.. Option D is correct because 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 7

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.. Option C is correct because 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.

Question 8

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 allows you to export trained models to ONNX format for offline inference. This enables running the model on edge devices or in environments without continuous internet connectivity, leveraging the ONNX runtime for efficient deployment.

Answer

Extract text from scanned receipts.

Answer

Identify specific individuals in a crowd using facial recognition.

Question 9

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.

Answer

Modify the URL to point to a different image.

Answer

Change the language parameter to 'multi'.

Question 10

You are building an application that processes scanned invoices to extract key fields such as total amount, invoice date, and vendor name. The application uses Azure AI Document Intelligence. You need to ensure high accuracy for field extraction without manual labeling. Which feature should you use?

Accepted Answer

Use a custom neural model. Option C is correct because Custom Neural Models are designed to extract fields from structured or semi-structured documents with high accuracy and do not require manual labeling if you use prebuilt models or start with a sample set. Option A is wrong because the Read API only extracts text, not key-value pairs. Option B is wrong because the Layout API extracts text and structure but not custom fields. Option D is wrong because the General Document model extracts key-value pairs but may not be optimized for invoices without customization.

Answer

Use the General Document model

Answer

Use the Read API

Answer

Use the Layout API

Question 11

A retail company uses Azure AI Vision to analyze store shelf images for product availability. The solution uses an object detection model trained on custom products. Recently, the model's performance dropped significantly due to new packaging designs. You need to improve the model's accuracy with minimal manual effort. What should you do?

Accepted Answer

Use Azure AI Vision model customization with active learning. Option D is correct because using Azure AI Vision's model customization with active learning allows the model to improve by automatically selecting the most informative images for labeling. Option A is wrong because retraining from scratch requires full labeling effort. Option B is wrong because adjusting confidence thresholds does not improve the model's recognition of new packaging. Option C is wrong because Image Analysis 4.0 with dense captioning provides descriptions but does not improve object detection for custom products.

Answer

Adjust the confidence score threshold to reduce false negatives

Answer

Use the Image Analysis 4.0 dense captioning feature

Answer

Collect a new set of images and retrain the model from scratch

Question 12

A healthcare organization uses Azure AI Health Insights to extract medical insights from unstructured clinical notes. The solution must comply with HIPAA. Which configuration is required?

Accepted Answer

Configure the Azure AI services resource to use a private endpoint and disable public network access. Option A is correct because cognitive services must be configured with a private endpoint and disable public network access to ensure data stays within a virtual network, meeting HIPAA requirements. Option B is wrong because enabling public network access is not compliant. Option C is wrong because storing data in Azure Storage with only Azure AD authentication does not ensure network isolation. Option D is wrong because using a managed identity alone does not restrict network access.

Answer

Store the clinical notes in Azure Storage with Azure AD authentication only

Answer

Enable public network access but use a firewall rule to restrict IP addresses

Answer

Use a system-assigned managed identity for authentication

Question 13

You deploy a custom vision model for defect detection on a manufacturing line. The model runs on an Azure IoT Edge device. You notice that inference latency is too high for real-time detection. Which action should you take to reduce latency?

Accepted Answer

Convert the model to TensorFlow and use the Azure IoT Edge Deep Learning module with hardware acceleration. Option C is correct because converting the model to TensorFlow and using the Deep Learning module on IoT Edge with hardware acceleration (e.g., Intel OpenVINO) can significantly reduce latency. Option A is wrong because moving to the cloud increases network latency. Option B is wrong because increasing image resolution increases processing time. Option D is wrong because retraining with more images does not reduce inference latency.

Answer

Move inference to Azure Functions in the cloud

Answer

Retrain the model with more defect images

Answer

Increase the resolution of input images

Question 14

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

Accepted Answer

Azure AI Vision OCR. Options A and D are correct. Azure AI Vision OCR (Read API) extracts printed and handwritten text from images and PDFs. Azure AI Document Intelligence (formerly Form Recognizer) extracts text from documents and also performs layout analysis. Option B is wrong because Azure AI Language focuses on text analysis, not image processing. Option C is wrong because Azure AI Search is for indexing and search, not text extraction from images. Option E is wrong because Azure AI Translator is for translation, not text extraction from images.

Answer

Azure AI Translator

Answer

Azure AI Search

Answer

Azure AI Language

Question 15

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.)

Accepted Answer

Azure AI Document Intelligence redaction. Options B, D, and E are correct. Azure AI Vision OCR extracts text from ID cards. Azure AI Language's PII detection identifies PII entities in the extracted text. Azure AI Document Intelligence's redaction feature (or a custom redaction step) removes the PII. Option A is wrong because Azure AI Video Indexer is for video analysis. Option C is wrong because Azure AI Custom Vision is for image classification/object detection, not text-based PII detection.

Answer

Azure AI Custom Vision

Answer

Azure AI Video Indexer