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CCNA Implement computer vision solutions Questions

75 of 117 questions · Page 1/2 · Implement computer vision solutions · Answers revealed

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1
Multi-Selecthard

Your organization uses Azure AI Vision to analyze surveillance images for security threats. The solution must comply with GDPR. Which TWO actions should you take?

Select 2 answers
A.Enable geo-redundant storage for the Azure AI Vision resource.
B.Enable Customer-Managed Keys (CMK) for encryption.
C.Use private endpoints to access the service.
D.Disable logging of request and response data.
E.Configure the service to use a specific data residency region within the EU.
AnswersD, E

Prevents personal data in logs.

Why this answer

Options B and D are correct because disabling logging ensures personal data is not stored in logs, and using data residency ensures data stays within the EU. Option A is wrong because CMK does not affect logging compliance. Option C is wrong because private endpoints improve security but do not directly address GDPR logging requirements.

Option E is wrong because geo-redundant storage may cause data to leave the EU.

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2
MCQhard

You are deploying a custom image classification model using Azure AI Custom Vision. The model must achieve high accuracy on a dataset with subtle differences between classes. However, the training set is small (200 images per class). Which strategy should you use to improve model performance?

A.Enable hyperparameter tuning and increase the number of iterations
B.Apply aggressive data augmentation and train from scratch
C.Use a larger batch size and train for more epochs
D.Use transfer learning with a pre-trained model and fine-tune
AnswerD

Transfer learning leverages pre-trained weights and is effective with small datasets.

Why this answer

Transfer learning with a pre-trained model is the standard approach for small datasets. Fine-tuning allows the model to leverage learned features.

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3
Multi-Selecthard

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.)

Select 3 answers
A.Azure AI Document Intelligence redaction
B.Azure AI Custom Vision
C.Azure AI Vision OCR
D.Azure AI Language PII detection
E.Azure AI Video Indexer
AnswersA, C, D

Can redact detected PII from documents.

Why this answer

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.

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4
MCQhard

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?

A.Collect more labeled scans from the new hospital and train a new model from scratch.
B.Create a new Custom Vision project and train only on the 500 new scans.
C.Apply image preprocessing to normalize the new hospital's scans to match the old hospital's style, then use the existing model.
D.Use the existing model as a starting point and retrain it with the 500 labeled scans from the new hospital.
AnswerD

Transfer learning with new data quickly adapts the model to the new domain with minimal effort.

Why this answer

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.

Exam trap

The trap here is that candidates may overestimate the need for large datasets or manual preprocessing, failing to recognize that Azure Custom Vision's built-in transfer learning is designed to efficiently adapt models with minimal new data.

How to eliminate wrong answers

Option A is wrong because collecting more labeled scans and training a new model from scratch is time-consuming and resource-intensive, not the quickest or minimal-effort solution. Option B is wrong because creating a new Custom Vision project and training only on 500 scans ignores the valuable knowledge from the original 10,000 scans, leading to a model with insufficient data and likely poor generalization. Option C is wrong because applying image preprocessing to normalize the new hospital's scans to match the old hospital's style is a manual, error-prone process that may not fully address the underlying domain shift and does not leverage the labeled data for supervised adaptation.

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5
MCQhard

You are designing a solution that uses Azure AI Vision to extract text from scanned invoices. The invoices vary in layout and include both printed and handwritten fields. The solution must achieve high accuracy with minimal manual labeling. Which approach should you recommend?

A.Use the Read API to extract all text and then use a custom regex to parse fields.
B.Use the Azure AI Document Intelligence prebuilt invoice model.
C.Train a Custom Vision object detection model to locate fields.
D.Label hundreds of invoices and train a custom Azure AI Document Intelligence model.
AnswerB

Prebuilt invoice model handles varying layouts and mixed text.

Why this answer

Option D is correct because Azure AI Document Intelligence (formerly Form Recognizer) is designed for extracting fields from documents with varying layouts and supports both printed and handwritten text. Option A is wrong because the Read API extracts all text but does not extract specific fields. Option B is wrong because Custom Vision is for object detection, not document analysis.

Option C is wrong because it requires significant manual labeling.

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6
Multi-Selecthard

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.)

Select 3 answers
A.Availability of labeled training data specific to the domain
B.Image format support (JPEG, PNG)
C.Whether the required labels are covered by the pre-built model
D.Need for real-time inference latency
E.Need to iterate and retrain the model over time
AnswersA, C, E

Custom Vision requires labeled data; pre-built models do not.

Why this answer

Custom Vision is suitable when you have custom labels not covered by pre-built models, and when you have sufficient training data. Pre-built models are better when you need immediate deployment without data. Option B is wrong because real-time requirements can be met by both services.

Option D is wrong because both services support common image formats.

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7
MCQhard

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?

A.Azure AI Video Indexer sentiment analysis
B.Azure AI Video Indexer people detection and tracking
C.Azure AI Face identify API
D.Object detection in Azure AI Vision
AnswerB

Tracks people movements and can trigger alerts.

Why this answer

Option B is correct because Azure AI Video Indexer's detection of people and their movements can be used to trigger alerts when a person is in a restricted area. Option A is wrong because object detection alone does not track location over time. Option C is wrong because face identification is for identifying specific individuals, not for location-based alerts.

Option D is wrong because sentiment analysis is for emotions, not location.

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8
MCQmedium

You are developing a solution for a hospital that uses the Face API to identify patients from photos taken at check-in. The system must be HIPAA compliant. You need to ensure that face data is protected and not retained longer than necessary. The hospital wants to use the Face API for identification only during the patient's visit. After discharge, the face data should be deleted. What is the recommended approach?

A.Use the Face API with a subscription key and export face data to local storage, then delete from the cloud.
B.Store the face IDs in a database and delete them manually after discharge.
C.Create a Person Group for each day, add patients, and delete the Person Group after the day ends.
D.Use the Face API Identify operation and then delete the face ID from the Person Group.
AnswerC

Person Groups can be deleted to remove all face data at once.

Why this answer

Option A is correct because the Face API supports person group management; you can create a temporary person group for each visit, then delete it after discharge. Option B is wrong because storing face IDs indefinitely is not compliant. Option C is wrong because exporting to local storage and deleting may lose traceability.

Option D is wrong because the Face API does not allow face ID deletion without deleting the person group.

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9
MCQhard

A retail company uses Azure Computer Vision to analyze in-store camera feeds to count customers. The solution uses the Detect API on individual frames. Recently, the counts have been inaccurate due to overlapping people. Which action should you take to improve accuracy?

A.Train a Custom Vision object detection model with images of crowded scenes.
B.Use the Analyze API instead of Detect API.
C.Increase the confidence threshold in the Detect API call.
D.Switch to Azure Video Indexer for people counting.
AnswerA

Custom model can learn to detect partially occluded people.

Why this answer

Option B is correct because Azure Custom Vision can be trained with images of crowded scenes to better detect overlapping people. Option A is wrong because increasing the confidence threshold may reduce false positives but doesn't handle overlap. Option C is wrong because Video Indexer is for video insights, not real-time counting.

Option D is wrong because the Detect API is already being used; the issue is with the pre-built model's limitations.

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10
MCQmedium

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?

A.Convert images to grayscale before sending to OCR API.
B.Increase the image resolution before calling OCR API.
C.Adjust brightness and contrast of images using image processing.
D.Reduce image size to decrease noise.
AnswerC

Improves visibility of text in low-light conditions, enhancing OCR accuracy.

Why this answer

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.

Exam trap

The trap here is that candidates confuse image quality improvements (like resolution or noise reduction) with the specific need to correct lighting-induced contrast loss, which is a distinct pre-processing requirement for OCR in poor illumination.

How to eliminate wrong answers

Option A is wrong because converting to grayscale removes color information that can help distinguish text from similarly-toned backgrounds, and it does not address the root cause of low contrast due to poor lighting. Option B is wrong because increasing resolution does not fix the underlying contrast problem; it may even amplify noise and increase API processing time without improving text legibility. Option D is wrong because reducing image size discards pixel detail, which can make small or thin text characters unreadable for OCR, and it does not mitigate the effects of poor lighting.

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11
MCQmedium

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?

A.Train a Custom Vision model with images of VIP customers.
B.Use the Face API's Face ID returned upon detection and store it in a database.
C.Store the customer's face image in Azure Blob Storage and compare using the Face API.
D.Create a PersonGroup with each VIP customer, and use the Identify operation.
AnswerD

PersonGroup stores only a template, not the image, aligning with privacy.

Why this answer

Option C is correct because Face API's PersonGroup stores only a numeric ID per face, not the actual image, which minimizes biometric data. Storing images (A) or using Face ID (B) retains more data. Custom Vision (D) is not designed for face identification.

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12
MCQeasy

You are building a solution to detect if a person is wearing a hard hat in construction site images. You have a small dataset of labeled images. Which Azure service should you use?

A.Azure AI Vision Image Analysis
B.Azure Video Indexer
C.Azure AI Document Intelligence
D.Azure Custom Vision
AnswerD

Custom Vision trains on your labeled images.

Why this answer

Option B is correct because Custom Vision allows you to train a custom object detection model with your own images. Option A is wrong because Azure AI Vision prebuilt models do not include hard hat detection. Option C is wrong because Video Indexer is for video analysis, not static images.

Option D is wrong because Azure AI Document Intelligence is for document processing.

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13
MCQmedium

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?

A.Use the General Document model
B.Use the Read API
C.Use a custom neural model
D.Use the Layout API
AnswerC

Custom neural models can be trained on a small set of sample documents to extract specific fields without manual labeling.

Why this answer

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.

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14
MCQhard

You work for a manufacturing company that uses Azure AI services to automate quality inspection on a production line. You have a Custom Vision object detection model that identifies defects on metal parts. The model was trained on images captured under ideal lighting conditions. However, when deployed in the factory, the model's accuracy drops significantly due to inconsistent lighting and glare. You need to improve the model's robustness without collecting new images from the factory floor. What should you do?

A.Increase the number of training iterations to force the model to learn more features.
B.Apply data augmentation techniques such as brightness, contrast, and blur adjustments to the existing training images.
C.Use higher resolution images for training.
D.Change the model type from object detection to classification.
AnswerB

Data augmentation simulates real-world variability and improves generalization.

Why this answer

Option B is correct because using data augmentation techniques like brightness and contrast adjustments, rotation, and noise injection can simulate various lighting conditions and improve robustness. Option A is wrong because increasing training iterations may overfit to the existing data. Option C is wrong because higher resolution does not address lighting variation.

Option D is wrong because changing the model type does not address the data issue.

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15
MCQmedium

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?

A.Azure AI Custom Vision
B.Azure AI Language
C.Azure AI Document Intelligence
D.Azure AI Computer Vision
AnswerC

Document Intelligence includes trained models for extracting marks from forms.

Why this answer

Option B is correct because Azure AI Document Intelligence (formerly Form Recognizer) is designed to extract information from forms, including checkboxes. The other options are not specialized for checkbox detection: Computer Vision for OCR, Custom Vision for image classification, and Language Service for text analysis.

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16
MCQeasy

A university is developing an app for students to take photos of handwritten notes and convert them to digital text. The app must support multiple languages including English and Spanish. The solution should use a pre-built AI service. Which Azure service should you use?

A.Azure AI Document Intelligence with a custom model
B.Azure AI Vision Read API (OCR)
C.Azure AI Language with custom entity recognition
D.Custom Vision with a custom handwriting recognition model
AnswerB

Supports handwritten text and multiple languages.

Why this answer

Azure AI Vision OCR API supports multiple languages and handwritten text. Custom Vision does not support OCR. Azure AI Language requires text input.

Azure AI Document Intelligence is for structured documents.

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17
Multi-Selectmedium

Which TWO actions should you take to reduce the latency of an Azure AI Computer Vision OCR call on a large image?

Select 2 answers
A.Use a CPU-bound compute instance.
B.Resize the image to a smaller resolution before calling the API.
C.Increase the API timeout value.
D.Use the Read API asynchronously.
E.Deploy the Cognitive Services container on-premises.
AnswersB, D

Smaller images process faster.

Why this answer

Options B and D are correct. B: Resizing reduces processing time. D: Using the Read API asynchronously allows the client to poll, avoiding timeout.

A: Increasing timeout doesn't reduce latency. C: Using CPU doesn't help. E: Cognitive Services container on-premises might add network latency.

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18
Multi-Selectmedium

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

Select 3 answers
A.Extract printed text from an image using OCR
B.Transcribe spoken audio from a video file
C.Detect objects in an image and return bounding boxes
D.Generate a human-readable caption for an image
E.Translate text found in an image to another language
AnswersA, C, D

OCR is included in Image Analysis 4.0.

Why this answer

Options A, B, and D are correct. Image Analysis 4.0 can generate captions, detect objects, and read text. Option C is wrong because translating text is done by Azure AI Translator.

Option E is wrong because transcribing audio is done by Azure AI Speech.

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19
MCQeasy

You are building an application that needs to extract printed text from scanned invoices. Which Azure AI service should you use?

A.Azure AI Vision OCR
B.Azure AI Document Intelligence
C.Azure AI Search
D.Azure AI Language
AnswerA

OCR in Azure AI Vision is designed to extract printed text from images.

Why this answer

Azure AI Vision's OCR reads printed text from images. Azure AI Document Intelligence is for forms and structured documents, but OCR is the core capability for printed text extraction.

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20
MCQmedium

You are designing a solution that reads handwritten notes from patient intake forms. The solution must handle various handwriting styles. Which Azure AI capability should you use?

A.Azure AI Document Intelligence Read model
B.Azure AI Custom Vision
C.Azure AI Vision OCR
D.Azure AI Language
AnswerA

The Read model handles handwriting and printed text.

Why this answer

Azure AI Document Intelligence's Read model supports handwriting recognition. The prebuilt-read model is designed to extract both printed and handwritten text.

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21
Multi-Selecteasy

Which TWO Azure AI services can be used to extract text from images?

Select 2 answers
A.Azure AI Face
B.Azure AI Document Intelligence
C.Azure AI Video Indexer
D.Azure AI Computer Vision
E.Azure AI Custom Vision
AnswersB, D

Document Intelligence extracts text from documents.

Why this answer

Azure AI Document Intelligence (formerly Form Recognizer) includes the Read OCR engine that extracts printed and handwritten text from images and documents. Azure AI Computer Vision provides the OCR API (optical character recognition) which can extract text from images, including both printed and handwritten text, and supports multiple languages. Both services are designed specifically for text extraction from visual content.

Exam trap

The trap here is that candidates may confuse Azure AI Video Indexer's ability to extract text from video frames as a primary image text extraction service, but it is designed for video analysis and indexing, not standalone image text extraction.

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22
MCQhard

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?

A.Increase the learning rate
B.Reduce the image size to lower resolution
C.Increase the number of training epochs
D.Increase the training dataset size with more varied images
AnswerD

More data helps the model generalize better and reduces overfitting.

Why this answer

The correct answer is to increase the training dataset size by adding more varied images. Overfitting occurs when the model learns noise from a small dataset. Option A is wrong because increasing the number of training epochs can actually increase overfitting.

Option B is wrong because increasing the learning rate may cause divergence. Option D is wrong because reducing image size can lose important features.

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23
MCQeasy

Your application needs to determine whether two photos of the same person are of the same individual, even if they are from different angles. Which Azure AI service should you use?

A.Azure AI Video Indexer
B.Azure AI Custom Vision
C.Azure AI Vision OCR
D.Azure AI Face
AnswerD

Face verification is designed for this purpose.

Why this answer

Azure AI Face's 'verify' operation compares two faces and returns a confidence score for whether they belong to the same person.

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24
Multi-Selectmedium

Which TWO Azure AI services can be used to detect objects in images?

Select 2 answers
A.Video Indexer
B.Face API
C.Custom Vision
D.Azure AI Document Intelligence
E.Computer Vision Object Detection API
AnswersC, E

Custom Vision can be trained for object detection.

Why this answer

Option A and B are correct because Custom Vision and Computer Vision Object Detection both support object detection. Option C is wrong because Face API detects faces, not generic objects. Option D is wrong because Document Intelligence extracts text and structure.

Option E is wrong because Video Indexer analyzes video, not images.

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25
MCQmedium

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?

A.Change model-version to a newer version
B.Add 'read' feature to the request
C.Set gender-neutral-caption to false
D.Set language to 'en-US'
AnswerC

Gender-neutral captions may omit descriptive terms, reducing accuracy.

Why this answer

The correct answer is to remove 'gender-neutral-caption' or set it to false. Gender-neutral captions may be less descriptive. Option A is wrong because the model-version is already latest.

Option B is wrong because language is already English. Option D is wrong because adding 'read' feature is for OCR, not caption quality.

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26
MCQhard

You are implementing a solution that uses Azure AI Vision to analyze images in a batch processing pipeline. The pipeline processes thousands of images daily. You need to minimize cost while ensuring that all images are processed within 24 hours. Which service tier should you choose?

A.Free tier F0
B.Standard S1 tier
C.Standard S0 tier
D.Standard S2 tier
AnswerB

S1 provides higher throughput and pay-per-transaction cost, ideal for batch processing.

Why this answer

The Standard S1 tier provides pay-per-transaction pricing, which is cost-effective for high-volume batch processing. Free tier has limits, and S0 is too slow.

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27
MCQmedium

A company uses the Computer Vision Image Analysis API to generate captions for images. The captions are often too generic. How can they improve the descriptiveness of captions?

A.Use the Object Detection API instead.
B.Train a custom model with Custom Vision.
C.Increase the confidence threshold for captions.
D.Use the Dense Captioning feature.
AnswerD

Dense captioning generates more detailed descriptions for regions of the image.

Why this answer

Option C is correct because using the 'dense' captioning feature provides more detailed descriptions. Option A is wrong because increasing confidence threshold reduces false positives but does not improve descriptiveness. Option B is wrong because object detection provides tags, not captions.

Option D is wrong because Custom Vision can be trained for image classification, not captioning.

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28
MCQeasy

You are building an application that analyzes images of handwritten notes. The application must extract the handwritten text and preserve the original layout, including line breaks and indentation. Which Azure AI service should you use?

A.Azure AI Document Intelligence Read model
B.Azure AI Vision OCR
C.Azure AI Document Intelligence Layout model
D.Azure AI Translator
AnswerA

The Read model in Azure AI Document Intelligence extracts printed and handwritten text while preserving the spatial layout, including line breaks and indentation.

Why this answer

The correct answer is Azure AI Document Intelligence (formerly Form Recognizer) with the Read OCR model, which extracts text including layout information. Option A is wrong because Azure AI Vision OCR does not preserve layout to the same extent; it returns text regions but not the full layout structure. Option B is wrong because the Layout model specifically focuses on layout and is part of Document Intelligence.

Option D is wrong because Translator is for translation, not OCR.

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29
MCQeasy

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?

A.Azure AI Video Indexer
B.Azure AI Face
C.Azure AI Custom Vision
D.Azure AI Document Intelligence
AnswerC

Train a custom object detection model for hard hats.

Why this answer

Option B is correct because Azure AI Custom Vision allows you to train a custom object detection model to detect hard hats. Option A is wrong because Azure AI Face is for face detection/recognition, not hard hat detection. Option C is wrong because Azure AI Video Indexer is for video analysis.

Option D is wrong because Azure AI Document Intelligence is for document analysis.

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30
MCQhard

You are a senior AI engineer at a manufacturing company. The company has a production line that uses cameras to capture images of assembled products. The current system uses a set of rule-based heuristics to detect defects, but it has high false-positive rates. You have been tasked to design a new computer vision solution using Microsoft Azure AI services. The solution must: - Detect defects such as scratches, dents, and misalignments in real-time as products move on the conveyor belt (frame rate of 30 fps). - Support continuous learning: when a new defect type is discovered, the model should be updated without retraining the entire model from scratch. - Operate with low latency (<100 ms per inference) to keep up with the production speed. - Use only fully managed services (no custom containers or edge devices). - The factory network has limited internet bandwidth, so the solution must minimize data transfer. Which approach should you recommend?

A.Use Azure AI Vision Spatial Analysis to detect defects in real-time by analyzing video feeds
B.Use Azure AI Video Indexer to index and search for defects in recorded videos
C.Use Azure AI Document Intelligence to analyze images of products
D.Train an object detection model using Azure AI Custom Vision, export it as a Docker container, and deploy it on an on-premises server with GPU
AnswerD

Custom Vision supports export to container for low latency, and can be retrained incrementally.

Why this answer

Azure AI Custom Vision with object detection can be exported and deployed to a Docker container on-premises for low latency. Continuous learning is supported by retraining with new images. However, Option C suggests using Azure AI Video Indexer, which is for video analysis and not real-time defect detection.

Option A uses Azure AI Vision Spatial Analysis, which is for people tracking. Option B uses Azure AI Document Intelligence, which is for documents. Option D is the only one that meets all requirements: Custom Vision allows retraining, container deployment for low latency, and minimal bandwidth (only model updates).

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31
MCQhard

You are deploying a Custom Vision model to an Azure Machine Learning managed endpoint using the above ARM template snippet. The deployment fails with a timeout error. Which parameter should you adjust?

A.Change model version to 2
B.Change compute to 'GPU'
C.Change authMode to 'AAD'
D.Increase scoringTimeout to 'PT10M'
AnswerD

Extends timeout to accommodate slow inference.

Why this answer

Option C is correct because the scoringTimeout of 5 minutes (PT5M) may be too short for the model to return predictions. Increasing it allows more time. Option A is wrong because authMode does not affect timeout.

Option B is wrong because compute type (CPU) is not the direct cause of timeout; changing to GPU might speed up but not necessarily. Option D is wrong because the model version is not related to timeout.

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32
MCQhard

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?

A.Use Azure AI Document Intelligence to process package labels
B.Train a single Custom Vision model that detects damage and reads tracking numbers using OCR
C.Use Azure AI Video Indexer to analyze the video stream from cameras
D.Use Azure AI Vision OCR Read API for tracking numbers and a separate Custom Vision model for damage detection
AnswerB

Custom Vision supports object detection and can integrate OCR for text.

Why this answer

Azure AI Vision OCR Read API is optimized for text detection and has high throughput. Object detection can be added via Custom Vision, but combining both in a single Custom Vision model is efficient. Separate API calls would increase latency.

Using separate services for OCR and detection would double processing time.

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33
MCQhard

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?

A.Delete and recreate the Cognitive Services resource
B.Regenerate key1 immediately and update the application to use the new key1
C.Regenerate both keys at the same time
D.Update the application to use key2, then regenerate key1
AnswerD

By switching to key2 first, the app remains active while key1 is regenerated.

Why this answer

The correct answer is to update the application to use key2, then regenerate key1. This ensures no downtime because the application continues to work with key2 while key1 is being regenerated. Option A is wrong because regenerating key1 first would cause downtime if the app is using it.

Option B is wrong because regenerating both simultaneously causes downtime. Option D is wrong because there is no need to delete the resource.

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34
Multi-Selectmedium

A company is building a computer vision solution using Azure AI Vision to analyze images of retail shelves. The solution must detect product presence and read expiration dates. Which TWO Azure AI Vision features should be used?

Select 2 answers
A.Face detection
B.Brand detection
C.Object detection
D.Optical Character Recognition (OCR)
E.Image captioning
AnswersC, D

Detects products on shelves.

Why this answer

Object detection identifies products on shelves. OCR reads text from images, such as expiration dates. Image captioning describes scenes, not suitable for this task.

Face detection and brand detection are not relevant.

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35
Multi-Selectmedium

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

Select 2 answers
A.Azure AI Custom Vision
B.Azure AI Face API
C.Azure AI Document Intelligence
D.Azure AI Vision Image Analysis
E.Azure Video Indexer
AnswersB, D

Face API is dedicated to face detection, recognition, and analysis.

Why this answer

Azure AI Face API is specifically for face detection and analysis. Azure AI Vision Image Analysis can also detect faces as part of its object detection. Option C is wrong because Video Indexer analyzes video, not images.

Option D is wrong because Custom Vision is not pre-built for faces. Option E is wrong because Document Intelligence is for document analysis.

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36
MCQmedium

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?

A.Use Azure AI Vision Image Analysis with a pre-built model
B.Use Azure AI Custom Vision with image classification
C.Use Azure AI Custom Vision with object detection
D.Train a deep learning model from scratch using Azure Machine Learning
AnswerB

Custom Vision with transfer learning is ideal for small datasets and binary classification tasks like defect detection.

Why this answer

The correct answer is to use a custom model with transfer learning via Azure AI Custom Vision. This is designed for small datasets and allows classification with limited labeled data. Option A is wrong because pre-built models are not trained for specific defects.

Option B is wrong because object detection is overkill and not focused on classification. Option D is wrong because training a model from scratch requires much more data.

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37
MCQeasy

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?

A.Store the clinical notes in Azure Storage with Azure AD authentication only
B.Enable public network access but use a firewall rule to restrict IP addresses
C.Configure the Azure AI services resource to use a private endpoint and disable public network access
D.Use a system-assigned managed identity for authentication
AnswerC

Ensures data is not exposed over the public internet.

Why this answer

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.

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38
MCQhard

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?

A.Add 'smartcrops' to visualFeatures.
B.Add 'gender-neutral-caption' parameter set to true.
C.Add 'model-version' with value '2023-10-01' to specify a stable version.
D.Remove 'Language' parameter because it is not supported.
AnswerC

The exhibit uses 'latest' which may cause inconsistencies; specifying a version ensures stable results.

Why this answer

Option A is correct because the 'visualFeatures' parameter includes 'Objects' and 'Brands', but the API requires that these features be explicitly listed as shown. However, the exhibit already includes them, so perhaps the question is trick: The exhibit shows the configuration is correct. But the stem says 'missing', maybe the API also requires 'details' to include something? Actually, the exhibit includes 'Objects' and 'Brands' in visualFeatures, and 'details' includes nothing relevant.

The correct answer is that nothing is missing; but since we must pick, Option A is the closest because the configuration is correct. However, to make a diagnostic question, maybe the issue is that 'modelVersion' should be a specific version. But given the options, Option A is correct.

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39
MCQmedium

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?

A.Use Azure AI Vision Image Analysis to generate captions for the paintings
B.Train a Custom Vision classifier with images of paintings labeled by artist and period
C.Use Azure AI Video Indexer to analyze the painting image
D.Use Azure AI Document Intelligence to extract text from the painting
AnswerB

Custom model can be trained for specific art classification.

Why this answer

Custom Vision with a domain-specific model can classify paintings by artist/period. Azure AI Vision Image Analysis provides generic descriptions, not specific art knowledge. Azure AI Video Indexer is for video.

Azure AI Document Intelligence is for documents.

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40
MCQmedium

You are building a solution to automatically tag images uploaded to an Azure Storage blob container using Azure AI Vision. The solution must process images as soon as they are uploaded. Which service should you use to trigger the image analysis?

A.Azure Functions with a timer trigger
B.Azure Event Grid with an Azure Function trigger
C.Azure Batch with a job schedule
D.Azure Logic Apps with a recurrence trigger
AnswerB

Event Grid provides real-time event-driven trigger.

Why this answer

Option A is correct because Azure Event Grid can trigger an Azure Function when a blob is created, which then calls Azure AI Vision. Option B is wrong because Logic Apps can also be triggered but Event Grid is more lightweight and recommended for event-driven scenarios. Option C is wrong because Azure Functions can be triggered directly by Event Grid, not by a timer.

Option D is wrong because Azure Batch is for batch processing, not real-time triggers.

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41
MCQmedium

Your team is building a mobile app that uses Azure AI Computer Vision to extract text from business cards. The app must handle cards in multiple languages (English, French, German). Which feature of the Computer Vision API should you use?

A.OCR API
B.Recognize Text
C.Read API
D.Describe Image
AnswerC

The Read API supports multiple languages and is the recommended option.

Why this answer

Option D is correct because the Read API supports multiple languages and is designed for OCR. The OCR API (A) is older and less accurate. Describe Image (B) generates captions, not text extraction.

Recognize Text (C) is deprecated.

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42
MCQhard

A company uses Azure AI Custom Vision to classify images of products. The model is deployed to a mobile app. The app sends images to the Custom Vision prediction endpoint. Users report that the app is slow when the network is poor. You need to enable offline inference on the mobile device. What should you do?

A.Use the Custom Vision compact model with smaller image size
B.Export the model as TensorFlow Lite and integrate it into the mobile app
C.Increase the prediction API timeout
D.Deploy the model to an Azure IoT Edge device
AnswerB

Enables on-device inference without network.

Why this answer

Option D is correct because exporting the model to TensorFlow Lite and running inference on-device allows offline operation. Option A is wrong because it only reduces latency when online. Option B is wrong because it does not enable offline inference.

Option C is wrong because Azure IoT Edge is for edge devices, not mobile apps directly.

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43
MCQeasy

You need to analyze videos stored in Azure Blob Storage to detect objects and generate timestamps. Which Azure service should you use?

A.Azure Custom Vision
B.Azure Form Recognizer
C.Azure Computer Vision
D.Azure Video Indexer
AnswerD

Video analysis with object detection and timestamps.

Why this answer

Azure Video Indexer (D) is the correct choice because it is specifically designed to analyze videos, extracting insights such as object detection, scene segmentation, and timestamps. It uses AI models to process video content stored in Azure Blob Storage and generates a timeline of detected objects, making it ideal for this scenario.

Exam trap

The trap here is that candidates often confuse Azure Computer Vision (image analysis) with video analysis, overlooking that Computer Vision lacks native video processing and timestamp generation, while Video Indexer is the dedicated service for end-to-end video insights.

How to eliminate wrong answers

Option A is wrong because Azure Custom Vision is a service for training custom image classification and object detection models on images, not for analyzing pre-recorded videos with timestamp generation. Option B is wrong because Azure Form Recognizer is designed to extract text and structure from documents (e.g., invoices, forms), not for video analysis or object detection. Option C is wrong because Azure Computer Vision provides image analysis APIs (e.g., object detection in static images) but lacks native video processing capabilities and timestamp generation; it would require additional custom logic to handle video frames sequentially.

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44
MCQmedium

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?

A.Azure AI Document Intelligence custom classification and extraction models
B.Azure AI Vision OCR and Azure AI Language
C.Azure AI Form Recognizer (deprecated) with custom models
D.Azure AI Document Intelligence pre-built invoice model
AnswerA

Custom models allow classification by vendor and extraction of specific fields tailored to each vendor's invoice layout.

Why this answer

The correct answer is to use Azure AI Document Intelligence with a custom classification model for vendor classification and a custom extraction model for field extraction. Option A is wrong because pre-built invoice model does not support custom vendor classification. Option B is wrong because Azure AI Vision OCR alone cannot extract structured fields.

Option D is wrong because Form Recognizer is the older name and the service is now Document Intelligence; the combination is correct but the naming is outdated, and it does not include classification.

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45
MCQmedium

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?

A.Use Azure AI Vision Image Analysis with a custom model to analyze each frame offline
B.Train a Custom Vision classification model and deploy it to the edge on CCTV cameras
C.Use Azure AI Video Indexer with a custom object detection model to detect vests in video frames
D.Use Azure Video Analyzer for Media to index videos and detect vests after recording
AnswerC

Supports real-time video and custom model deployment.

Why this answer

Azure AI Video Indexer supports real-time video analysis with custom models. Azure AI Vision Image Analysis works on still images, not real-time video. Custom Vision is for image classification.

Azure Video Analyzer for Media (now part of Video Indexer) is correct.

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46
MCQeasy

You need to extract printed text from scanned invoices in multiple languages. Which Azure service should you use?

A.Azure Form Recognizer
B.Azure Computer Vision Read API
C.Azure Custom Vision
D.Azure Video Indexer
AnswerB

OCR service supporting multiple languages.

Why this answer

Option A is correct because Azure Computer Vision's Read API is designed for OCR with multilingual support. Option B is wrong because Custom Vision is for custom image classification, not OCR. Option C is wrong because Form Recognizer is for form extraction, but the Read API is the primary OCR service.

Option D is wrong because Video Indexer is for video.

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47
MCQhard

You are analyzing an image using the Azure AI Vision REST API with the JSON request above. The response includes a description: 'a person holding a smartphone'. However, the response does not include any brand information even though the smartphone is clearly visible. What is the most likely reason?

A.The smartphone brand is not in the Microsoft brand catalog.
B.The 'brands' feature was not included in the request.
C.The 'model-version' is set to 'latest' which does not support brand detection.
D.The API only detects one brand per image, and a different brand was detected.
AnswerA

Brand detection only recognizes brands in Microsoft's predefined catalog.

Why this answer

Option D is correct. The brands feature is included, but brand detection only works for well-known brands; if the smartphone brand is not in Microsoft's brand catalog, it won't be detected. Option A is wrong because 'brands' is included.

Option B is wrong because model-version 'latest' supports brand detection. Option C is wrong because the API can detect multiple brands.

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48
Multi-Selecthard

Which THREE factors should be considered when choosing between Azure AI Vision prebuilt models and Custom Vision?

Select 3 answers
A.Custom Vision cannot process images from real-time video feeds.
B.Prebuilt models require a large training dataset.
C.Prebuilt models may not have high accuracy for industry-specific objects.
D.Prebuilt models are suitable for common object categories like cars and animals.
E.Custom Vision allows training on custom object categories.
AnswersC, D, E

Prebuilt models are trained on general data, so accuracy may be lower for niche objects.

Why this answer

Options A, C, and E are correct. Prebuilt models have limited accuracy for niche domains (A). Custom Vision allows training on specific objects (C).

Prebuilt models cover common objects, but Custom Vision is needed for custom categories (E). Option B is wrong because Custom Vision also supports images from various sources. Option D is wrong because prebuilt models do not require training data.

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49
MCQhard

You are designing a solution to detect brand logos in social media images. The logos vary in size and orientation. You need to achieve high accuracy with minimal false positives. Which approach should you recommend?

A.Use Azure Computer Vision Describe API to generate captions and filter by logo mentions.
B.Train an Azure Custom Vision object detection model with labeled logo images.
C.Use Azure Computer Vision Analyze API with domain-specific models.
D.Use Azure Form Recognizer to extract logo positions from images.
AnswerB

Custom object detection can learn to detect logos in various conditions.

Why this answer

Option B is correct because Custom Vision with object detection can be trained on diverse logo images to handle variations. Option A is wrong because the Analyze API does not detect specific objects. Option C is wrong because Form Recognizer is for documents.

Option D is wrong because the Describe API generates captions, not logo detection.

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50
MCQhard

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?

A.Increase the image contrast before sending to the OCR engine
B.Add more training data with barcodes to the OCR model
C.Detect and remove barcodes from the image before OCR
D.Resize the image to a smaller resolution to reduce barcode impact
AnswerC

Removing barcodes eliminates patterns that cause OCR errors, improving accuracy.

Why this answer

The correct answer is to add a barcode detection and removal step before OCR. Barcodes can confuse OCR services because they contain patterns that may be interpreted as text, causing errors. Option A is wrong because adjusting contrast does not address barcode interference.

Option B is wrong because adding more training data is for custom models, not pre-built OCR. Option D is wrong because compressing the image may reduce quality and exacerbate the issue.

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51
MCQhard

A company uses the Face API for identity verification. During testing, they find that the similarity scores between two images of the same person are lower than expected. Which factor is most likely causing this?

A.The images are compressed with different quality levels.
B.The images have different lighting conditions (e.g., one is brightly lit, the other is dark).
C.The backgrounds of the images are different.
D.The images have different dimensions (e.g., 500x500 vs 1000x1000).
AnswerB

Lighting changes facial appearance and reduces similarity scores.

Why this answer

Option B is correct because large differences in lighting can significantly affect similarity scores. Option A is wrong because moderate difference in image size does not affect facial features. Option C is wrong because background mismatch does not affect the face comparison.

Option D is wrong because compression artifacts may have a minor effect but not as much as lighting.

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52
MCQeasy

Your team is building a mobile app that uses Azure Custom Vision to classify plant species. The app must work offline and sync labeled images when connectivity is restored. Which SDK feature should you use?

A.Azure IoT Edge runtime on the phone
B.Azure API Management with caching
C.Export the model as a TensorFlow or CoreML model for on-device inference
D.Continuous deployment integration
AnswerC

Exported model runs offline.

Why this answer

Option B is correct because Custom Vision provides an offline prediction capability by exporting the model to run locally on device. Option A is wrong because continuous deployment is for cloud deployments. Option C is wrong because API Management is for managing APIs, not offline operation.

Option D is wrong because IoT Edge is for edge devices, not mobile apps.

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53
MCQmedium

Refer to the exhibit. You have trained an object detection model in Azure Custom Vision. The model is published as 'defect-model'. You need to deploy this model to a Docker container for on-premises inference using the Azure IoT Edge runtime. What should you do first?

A.Create an Azure Container Registry and push the Custom Vision base image.
B.Export the model as a Docker container (e.g., TensorFlow) using the Custom Vision portal.
C.Use the Custom Vision prediction API to call the published endpoint from the edge device.
D.Retrain the model with more images to improve mAP.
AnswerB

Exporting creates a container image for offline inference.

Why this answer

Option B is correct because to deploy to a container, you must export the model as a Docker image (e.g., TensorFlow, ONNX) and then create an IoT Edge module. Option A is wrong because the model is already published; you need to export it. Option C is wrong because you don't need to retrain.

Option D is wrong because the container registry is used to store the exported container image.

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54
MCQeasy

You need to extract handwritten text from scanned forms. Which Azure Computer Vision feature should you use?

A.OCR API (optical character recognition)
B.Tag API
C.Read API
D.Describe API
AnswerC

Supports both printed and handwritten text.

Why this answer

Option D is correct because the Read API supports handwritten text. Option A is wrong because the OCR API (legacy) is for printed text. Option B is wrong because the Describe API generates captions.

Option C is wrong because the Tag API returns tags.

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55
MCQmedium

A company uses Azure AI Vision to analyze product images in an e-commerce application. The solution uses the Analyze Image API with OCR. Recently, the OCR accuracy has decreased for images with handwritten text on product labels. What should the team do to improve accuracy?

A.Enable OCR in the Analyze Image API configuration.
B.Use Azure AI Document Intelligence prebuilt receipt model.
C.Switch to the Read API for OCR.
D.Retrain the OCR model with additional handwritten samples.
AnswerC

The Read API is better for handwritten text.

Why this answer

Option B is correct because the Read API is optimized for images with text, including handwritten text, and offers better accuracy than the Analyze Image API for OCR. Option A is wrong because Azure AI Document Intelligence (formerly Form Recognizer) is designed for structured documents. Option C is wrong because OCR is already enabled; the issue is using the wrong API.

Option D is wrong because adding more training data does not apply to the prebuilt OCR model.

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56
MCQmedium

A company uses Azure Computer Vision to moderate user-generated content. The solution must detect adult content and flag it. Which API should you call?

A.Read API
B.Analyze API with visualFeatures set to 'Adult'
C.Detect API
D.Describe API
AnswerB

Detects adult/racy content.

Why this answer

Option C is correct because the Analyze API with the visualFeatures parameter set to 'Adult' detects adult content. Option A is wrong because the Read API is for OCR. Option B is wrong because the Describe API generates captions.

Option D is wrong because the Detect API is for object detection.

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57
MCQhard

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?

A.Reduce the probability threshold to increase recall.
B.Capture additional images under the new lighting and retrain the model.
C.Use Azure AutoML to automatically find the best algorithm.
D.Add more images from the original lighting conditions to the training set.
AnswerB

Adding representative data from the new conditions is the best practice.

Why this answer

Option C is correct because retraining with images that represent the new lighting conditions will directly address the distribution shift. Adding more images of the original lighting (A) won't help with new conditions. Adjusting probability threshold (B) might change precision/recall but not robustness.

Using AutoML (D) is not directly applicable to Custom Vision.

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58
MCQhard

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?

A.Use Azure AI Vision model customization with active learning
B.Adjust the confidence score threshold to reduce false negatives
C.Use the Image Analysis 4.0 dense captioning feature
D.Collect a new set of images and retrain the model from scratch
AnswerA

Active learning selects the most informative images for labeling, minimizing manual effort.

Why this answer

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.

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59
Multi-Selecteasy

You are developing a solution to detect and redact personally identifiable information (PII) in images of documents. Which THREE Azure services can you use together?

Select 3 answers
A.Azure Custom Vision
B.Azure AI Language (PII detection)
C.Azure AI Document Intelligence
D.Azure AI Vision (Read API)
E.Azure Video Indexer
AnswersB, C, D

Detects PII in text.

Why this answer

Options A, B, and D are correct because Azure AI Vision or Azure AI Document Intelligence can extract text, Azure AI Language can detect PII, and then the text can be redacted. Option C is wrong because Custom Vision is for object detection, not PII detection. Option E is wrong because Video Indexer is for video, not images.

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60
Multi-Selectmedium

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?

Select 2 answers
A.Train for more iterations without validation.
B.Use images with balanced numbers of defect and non-defect samples.
C.Set the learning rate manually using the Custom Vision API.
D.Increase the number of images per tag, including variations in lighting and angle.
E.Reduce the number of images per tag to avoid overfitting.
AnswersB, D

Balanced datasets prevent bias toward majority class.

Why this answer

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.

Exam trap

The trap here is that candidates may think reducing images prevents overfitting (Option E) or that manual learning rate tuning (Option C) is possible in Custom Vision, but the service abstracts hyperparameter tuning and requires sufficient, varied data for robust defect detection.

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61
Multi-Selecthard

A manufacturing company uses Azure AI Custom Vision to classify defects on assembly line parts. The model is currently trained with 500 images per class and achieves 85% accuracy. The company needs to improve accuracy to 95% without collecting new images. Which THREE actions should they take?

Select 3 answers
A.Change the domain to 'General'
B.Use data augmentation (e.g., rotation, scaling)
C.Increase the number of training iterations
D.Adjust the probability threshold
E.Add negative images (images without defects)
AnswersB, D, E

Creates variations from existing images.

Why this answer

Adjusting probability threshold can reduce false positives. Adding negative images improves model robustness. Using data augmentation generates variations from existing images.

Changing domain is not applicable after training; increasing training time may help but not as directly as augmentation.

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62
MCQeasy

Your company wants to moderate user-uploaded images for adult content. Which Azure AI service should you use?

A.Azure AI Content Safety
B.Azure AI Face
C.Azure AI Document Intelligence
D.Azure AI Vision Image Analysis
AnswerA

Content Safety provides content moderation for images and text.

Why this answer

Azure AI Content Safety is specifically designed to detect adult and offensive content. It can analyze images for adult content.

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63
MCQhard

Refer to the exhibit. You run the Azure CLI command shown. What is the output?

A.A single key string
B.Both key1 and key2
C.The endpoint URL
D.The region of the account
AnswerA

The query extracts key1 as a string.

Why this answer

The `az cognitiveservices account keys list` command retrieves the access keys for an Azure Cognitive Services account. By default, it returns both key1 and key2, but the `--query` parameter with `keys[0]` filters the output to return only the first key (key1) as a single string. This is a standard Azure CLI query syntax using JMESPath to extract a specific element from the JSON response.

Exam trap

Microsoft often tests the distinction between retrieving keys versus other account properties (like endpoint or region), and the trap here is that candidates assume `keys list` returns all keys by default without recognizing that the `--query` parameter can filter to a single key.

How to eliminate wrong answers

Option B is wrong because the `--query keys[0]` filter explicitly selects only the first key (key1), not both key1 and key2. Option C is wrong because the command retrieves keys, not the endpoint URL; the endpoint URL is obtained via `az cognitiveservices account show` with a different query. Option D is wrong because the region is a property of the account resource, not the keys list; the region is returned by `az cognitiveservices account show` or `list`, not by the keys list command.

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64
Multi-Selecteasy

Which TWO features are available in Azure Computer Vision but not in Azure Custom Vision?

Select 2 answers
A.Object detection
B.Image classification
C.Face detection
D.Optical character recognition (OCR)
E.Image captioning
AnswersD, E

Pre-built OCR only in Computer Vision.

Why this answer

A and E are correct. Azure Computer Vision provides pre-built optical character recognition (OCR) and image captioning. Custom Vision is for custom models and does not include these pre-built capabilities.

B and C are wrong because Custom Vision can do image classification and object detection with custom training. D is wrong because face detection is available in Face API, not directly in Computer Vision.

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65
MCQmedium

A healthcare organization uses Azure AI Vision to analyze medical images for abnormalities. The solution must ensure patient data is not stored in the service's logs. What should you configure?

A.Use a private endpoint to access the Azure AI Vision resource.
B.Set the logging level to 'None' in the Azure AI Vision resource diagnostic settings.
C.Enable Customer-Managed Keys (CMK) for the resource.
D.Use Azure Policy to restrict data residency.
AnswerB

Disabling logging prevents data from being stored.

Why this answer

Option A is correct because Azure AI services allow you to disable logging of request and response data to meet data privacy requirements. Option B is wrong because network isolation does not prevent logging. Option C is wrong because data residency controls where data is stored, not logging.

Option D is wrong because encryption protects data at rest, not logging.

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66
MCQeasy

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?

A.Set a high confidence threshold for face matching.
B.Face identification with a large person group.
C.Enable liveness detection using session-based verification.
D.Face detection with attributes such as age and emotion.
AnswerC

Liveness detection checks for spoofing attacks.

Why this answer

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.

Exam trap

The trap here is that candidates may confuse confidence thresholds or face attributes with liveness detection, not realizing that only session-based verification actively checks for spoofing through motion and depth analysis.

How to eliminate wrong answers

Option A is wrong because setting a high confidence threshold only increases the strictness of face matching scores, but does not differentiate between a live face and a spoofed image or video. Option B is wrong because face identification with a large person group is used to match a detected face against a database of enrolled persons, but it does not verify liveness or detect presentation attacks. Option D is wrong because face detection with attributes like age and emotion extracts demographic and emotional information from a face, but it cannot determine whether the face is live or a reproduction.

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67
MCQeasy

You are a developer for a healthcare startup. They are building a mobile app that allows users to take photos of prescription labels and extract medication names, dosages, and frequencies. The app must run on iOS and Android devices. The solution should use a pre-built AI service with minimal custom code. What should you recommend?

A.Use Azure AI Document Intelligence (Form Recognizer) to analyze labels
B.Train a Custom Vision object detection model to locate and read text
C.Use Azure AI Language to extract entities from the label text
D.Use Azure AI Vision OCR API to extract text from the label images
AnswerD

Pre-built OCR works for text extraction from natural scenes.

Why this answer

Azure AI Vision OCR provides pre-built text extraction from images. Custom Vision requires custom training. Form Recognizer is for forms.

Azure AI Language is for text analytics, not image text extraction.

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68
MCQmedium

A healthcare organization uses Custom Vision to classify X-ray images. They have a small dataset of 200 images per class. Which strategy will most likely improve model accuracy?

A.Reduce the image dimensions to speed up training.
B.Add more negative samples to the dataset.
C.Use data augmentation and transfer learning with a pre-trained model.
D.Increase the number of training iterations significantly.
AnswerC

Data augmentation increases effective dataset size, and transfer learning leverages pre-trained features.

Why this answer

Option A is correct because data augmentation and transfer learning are standard techniques to improve accuracy with small datasets. Option B is wrong because increasing iterations without more data can lead to overfitting. Option C is wrong because reducing image size may lose important details.

Option D is wrong because negative tagging is useful but does not replace the need for more training data.

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69
MCQmedium

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?

A.The network ACLs block all traffic by default
B.The apiVersion in the template is incorrect
C.The custom subdomain name is not configured correctly
D.The SKU 'S1' does not support API calls
AnswerA

With defaultAction set to 'Deny', no traffic is allowed unless IP rules are added.

Why this answer

The correct answer is that network ACLs are set to Deny by default, blocking all traffic. The template sets networkAcls.defaultAction to 'Deny', meaning no IP addresses are allowed unless explicitly added. Option A is wrong because the SKU S1 is valid.

Option B is wrong because the custom subdomain is correctly configured. Option D is wrong because the apiVersion is valid.

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70
MCQmedium

A company is building a solution to analyze customer reviews images using Azure AI Vision. They need to extract text from images that may contain both printed and handwritten text. Which feature should they use?

A.Custom Vision
B.OCR API (optical character recognition)
C.Read API
D.Azure AI Document Intelligence
AnswerC

The Read API is designed to extract both printed and handwritten text from images.

Why this answer

Option B is correct because the Read API is designed to extract printed and handwritten text from images. Option A is wrong because OCR API is older and less capable for handwritten text. Option C is wrong because Form Recognizer is for forms and documents.

Option D is wrong because Custom Vision is for image classification and object detection.

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71
MCQhard

You have a real-time video processing pipeline using Azure AI Video Indexer. You need to detect when a specific person appears in archived video footage. Which approach minimizes latency and cost?

A.Use Video Indexer's face detection and indexing, then search
B.Extract keyframes and use Custom Vision to detect the person
C.Run face detection on every frame using Azure AI Face and store results
D.Use Azure AI Vision to detect faces in video frames and compare against a database
AnswerA

Video Indexer indexes faces efficiently and allows search without reprocessing.

Why this answer

Using face detection and indexing in Video Indexer allows efficient search. Re-indexing only when needed avoids unnecessary processing.

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72
MCQmedium

You are building a mobile app that allows users to take a photo of a product and get detailed information. The app uses Azure AI Custom Vision to classify products. You need to ensure low latency for inference. What should you do?

A.Increase the number of training iterations
B.Use the Azure AI Vision API directly
C.Use Azure Front Door to cache results
D.Export the Custom Vision model as a TensorFlow model and run on-device
AnswerD

On-device inference is fastest; TensorFlow Lite can run on mobile.

Why this answer

Deploying the model to a container on the edge (mobile device) reduces latency since inference happens locally without network calls.

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73
MCQmedium

You have a computer vision solution that analyzes security camera feeds to detect people and vehicles. The solution uses Azure AI Vision Spatial Analysis. You need to ensure compliance with privacy regulations by blurring detected faces. Which feature should you enable?

A.Use Azure AI Content Safety to filter faces
B.Post-process frames with Azure AI Face client SDK
C.Enable face detection and redact faces using Azure AI Video Indexer
D.Enable face blurring in the Spatial Analysis configuration
AnswerD

Spatial Analysis supports face blurring to obscure identities.

Why this answer

Azure AI Vision includes face blurring (or anonymization) as a built-in capability for privacy compliance in spatial analysis scenarios.

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74
MCQhard

A company uses the Face API to detect and identify employees for building access. They need to ensure that the system complies with GDPR requirements for biometric data. Which action should they take?

A.Store faces in a secure database and delete after 30 days.
B.Anonymize the face data by blurring key features.
C.Obtain explicit consent from each employee before enrollment.
D.Use encryption for stored face templates.
AnswerC

GDPR requires explicit consent for processing biometric data.

Why this answer

Option C is correct because obtaining explicit consent is a key GDPR requirement for biometric data processing. Option A is wrong because deleting data after a period may not be sufficient without consent. Option B is wrong because encrypting data is a security measure but does not address consent.

Option D is wrong because anonymizing data may circumvent the need for consent but is not always feasible for facial recognition.

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75
MCQhard

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?

A.Increase the number of training epochs.
B.Add more normal X-ray images to the dataset.
C.Switch to a different object detection algorithm.
D.Use oversampling or class-weight techniques to balance the training.
AnswerD

Balancing the dataset or adjusting loss weights improves minority class recall.

Why this answer

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.

Exam trap

The trap here is that candidates often confuse accuracy with model effectiveness, assuming high test accuracy (98%) means the model is robust, but they overlook that accuracy is misleading with severe class imbalance—a model predicting 'normal' for every image would achieve 95% accuracy while missing all abnormal cases.

How to eliminate wrong answers

Option A is wrong because increasing training epochs does not fix class imbalance; it may lead to overfitting on the majority class without improving minority class recall. Option B is wrong because adding more normal X-ray images exacerbates the imbalance, making the model even more biased toward the majority class. Option C is wrong because switching to a different object detection algorithm (e.g., YOLO vs.

Faster R-CNN) does not inherently address data imbalance; the core problem is the skewed training distribution, not the algorithm choice.

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