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CCNA Implement computer vision solutions Questions

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76
Multi-Selectmedium

Which TWO Azure services can be used to perform optical character recognition (OCR) on images?

Select 2 answers
A.Azure Computer Vision Read API
B.Azure Face API
C.Azure Video Indexer
D.Azure Custom Vision
E.Azure Form Recognizer
AnswersA, E

The Computer Vision Read API is purpose-built for OCR, extracting printed and handwritten text from images and documents via synchronous or asynchronous read operations. This directly satisfies the stem's requirement for an Azure service performing optical character recognition on images.

Why this answer

Azure Computer Vision Read API is correct because it provides a dedicated OCR capability that extracts printed and handwritten text from images and documents. It uses deep learning models to detect text regions, recognize characters, and return structured output with bounding boxes and confidence scores.

Exam trap

Candidates may mistakenly think that only the Computer Vision Read API can perform OCR. However, Azure Form Recognizer also uses OCR technology to extract text from documents, though it is optimized for structured forms and tables. The correct answers are both A and E.

A common mistake is to choose the Face API or Custom Vision, which do not provide OCR capabilities.

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77
MCQeasy

You are developing a mobile app that allows users to take a photo of a product and get information about it. The app must identify the product from the image. Which Azure AI service should you use?

A.Azure AI Vision OCR
B.Azure AI Face API
C.Azure AI Custom Vision with image classification
D.Azure AI Custom Vision with object detection
AnswerC

Custom Vision image classification trains a model on your own labelled product images, returning the predicted product class for a photo. This satisfies identifying a specific product, which a pre-trained general service cannot do without custom training.

Why this answer

Azure AI Custom Vision with image classification is specifically designed to identify and categorize products or objects within an image based on trained labels. This service allows you to upload images of products, train a model to recognize them, and then use the model to classify new product photos, making it ideal for a product identification app.

Exam trap

The trap here is that candidates often confuse image classification with object detection, thinking that identifying a product requires bounding boxes, when in fact classification alone suffices for determining the product type without needing its location in the image.

How to eliminate wrong answers

Option A is wrong because Azure AI Vision OCR (Optical Character Recognition) extracts text from images, not product identification; it cannot recognize or classify objects like a specific product. Option B is wrong because Azure AI Face API is specialized for detecting, analyzing, and recognizing human faces, not general products or objects. Option D is wrong because Azure AI Custom Vision with object detection identifies and locates multiple objects within an image by drawing bounding boxes around them, which is overkill for simply identifying a single product; image classification is more appropriate for determining what the product is without needing spatial coordinates.

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78
MCQeasy

A company wants to moderate user-generated images for adult content. Which Azure AI Vision feature should they use?

A.Custom Vision with a custom adult classifier
B.Face API
C.Analyze Image API with moderation categories
D.OCR
AnswerC

The Analyze Image API returns adult and racy classification flags directly from its moderation categories, satisfying the requirement to detect adult content in user-generated images. Unlike standalone classifiers, it performs this assessment within a single vision call, so no separate moderation service or custom model is needed.

Why this answer

The Analyze Image API in Azure AI Vision includes built-in moderation categories for detecting adult, racy, and gory content in images. This feature is specifically designed for content moderation without requiring custom training, making it the correct choice for moderating user-generated images for adult content.

Exam trap

The trap here is that candidates may assume Custom Vision is needed for any custom moderation task, but Azure AI Vision's Analyze Image API already includes built-in adult content detection, making custom training unnecessary for this specific use case.

How to eliminate wrong answers

Option A is wrong because Custom Vision requires training a custom classifier with labeled data, which is unnecessary when Azure AI Vision already provides pre-built adult content moderation categories. Option B is wrong because Face API is designed for face detection, recognition, and analysis, not for general adult content moderation. Option D is wrong because OCR (Optical Character Recognition) extracts text from images and does not analyze visual content for adult themes.

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79
Multi-Selectmedium

Which TWO Azure AI services can be used to perform optical character recognition (OCR) on images? (Choose two.)

Select 2 answers
A.Azure AI Document Intelligence Read model
B.Azure Video Indexer
C.Azure AI Custom Vision
D.Azure AI Face API
E.Azure AI Vision OCR (Read API)
AnswersA, E

Azure AI Document Intelligence's Read model extracts printed and handwritten text from images and documents via OCR, returning lines, words and page layout. It satisfies the stem's requirement to perform optical character recognition on images, operating on image files directly rather than requiring search-index ingestion or translation pipelines.

Why this answer

Azure AI Document Intelligence Read model (option A) is correct because its Read model is specifically designed to extract printed and handwritten text from documents and images, returning lines, words, and page layout via the Analyze Document operation. Azure AI Vision OCR (Read API) (option E) is also correct because the Read API in Azure AI Vision performs OCR on images and PDFs, returning extracted text and bounding boxes asynchronously. Azure Video Indexer (option B) focuses on video/audio insights such as transcription and face tracking, not general image OCR.

Azure AI Custom Vision (option C) is an image classification and object detection service, not an OCR text extractor. Azure AI Face API (option D) detects and analyzes faces (landmarks, attributes), and does not perform optical character recognition.

Exam trap

The trap here is that candidates may confuse Azure AI Custom Vision (option C) with OCR capabilities, assuming it can read text from images, when it is actually limited to classifying and detecting objects based on custom training data.

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80
MCQhard

You are deploying a Custom Vision object detection model to an Azure Container Instance for real-time inference. The model must respond within 500 ms. The default container runs on CPU. What should you do to meet the latency requirement?

A.Increase the number of CPU cores in the container instance.
B.Export the model as a Dockerfile with GPU support and deploy to a GPU-enabled ACI.
C.Deploy the model to Azure Functions with a Premium plan.
D.Use the Cognitive Services Computer Vision container instead.
AnswerB

GPU acceleration is key for low-latency object detection.

Why this answer

The default Custom Vision container runs on CPU, which is insufficient for real-time object detection inference within 500 ms. Exporting the model as a Dockerfile with GPU support and deploying to a GPU-enabled Azure Container Instance (ACI) leverages NVIDIA CUDA-accelerated inference, dramatically reducing latency to meet the sub-500 ms requirement.

Exam trap

The trap here is that candidates assume increasing CPU cores (Option A) is a valid performance fix, but Azure explicitly documents that Custom Vision object detection models require GPU acceleration for real-time latency under 500 ms, and the default CPU container is only suitable for batch or offline processing.

How to eliminate wrong answers

Option A is wrong because increasing CPU cores does not provide the parallel processing power needed for deep learning inference; object detection models like YOLO or Faster R-CNN require GPU acceleration for sub-500 ms latency. Option C is wrong because Azure Functions, even with a Premium plan, still runs on CPU and incurs cold-start latency, making it unsuitable for real-time inference under 500 ms. Option D is wrong because the Cognitive Services Computer Vision container is a pre-built container for general image analysis, not for deploying a custom-trained object detection model; it cannot be used to host your own Custom Vision model.

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81
MCQeasy

Refer to the exhibit. You are creating an Azure Cognitive Services account using an ARM template snippet. What type of account is being created?

A.Azure AI Language
B.Azure AI Computer Vision
C.Azure AI Services multi-service account
D.Azure OpenAI Service
AnswerC

The ARM snippet specifies `kind: "CognitiveServices"` with a multi-service SKU, provisioning one key and endpoint across Vision, Language, Speech and Translator. This satisfies the stem's requirement to identify a single Azure AI Services resource spanning multiple APIs, rather than a single-service account such as FormRecognizer or TextAnalytics.

Why this answer

The ARM template snippet uses the 'CognitiveServices' resource type and sets 'kind' to 'CognitiveServices', which provisions a multi-service account that provides access to multiple Azure AI services (e.g., Language, Computer Vision, Translator) under a single endpoint and key. This is distinct from single-service accounts, which use specific 'kind' values like 'TextAnalytics' or 'ComputerVision'.

Exam trap

The trap here is that candidates often confuse the 'CognitiveServices' kind (multi-service) with a specific single-service account, especially when the ARM template lacks explicit service-specific properties, leading them to pick a single-service option like Azure AI Language or Computer Vision.

How to eliminate wrong answers

Option A is wrong because Azure AI Language (formerly Text Analytics) is a single-service account created with 'kind': 'TextAnalytics', not 'CognitiveServices'. Option B is wrong because Azure AI Computer Vision is a single-service account created with 'kind': 'ComputerVision', not 'CognitiveServices'. Option D is wrong because Azure OpenAI Service uses a different resource type 'OpenAI' and 'kind': 'OpenAI', not the 'CognitiveServices' resource type.

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82
Multi-Selecteasy

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

Select 2 answers
A.Azure AI Metrics Advisor
B.Azure AI Language
C.Azure AI Document Intelligence
D.Azure AI Personalizer
E.Azure AI Vision
AnswersC, E

Document Intelligence provides prebuilt and custom OCR models that read printed and handwritten text from documents, returning structured layout, key-value pairs and tables. This satisfies the scenario's requirement to perform optical character recognition on documents, going beyond simple text extraction.

Why this answer

Azure AI Document Intelligence (option C) is correct because it is the Azure service purpose-built for document processing, and its prebuilt Read and Layout models extract printed and handwritten text from documents and images using OCR. Azure AI Vision (option E) is also correct because its Image Analysis and Read capabilities include an OCR engine that extracts text from images and documents via the Read API. Azure AI Metrics Advisor (option A) is an anomaly-detection service for time-series data and performs no OCR.

Azure AI Language (option B) provides natural language processing such as sentiment analysis, key phrase extraction, and entity recognition, not optical character recognition. Azure AI Personalizer (option D) is a reinforcement-learning-based recommendation service and has no OCR functionality.

Exam trap

The trap here is that candidates often assume only Azure AI Vision (the Computer Vision service) can perform OCR, forgetting that Azure AI Document Intelligence also provides OCR as part of its document analysis capabilities, and both services are valid for OCR tasks depending on the scenario.

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83
Multi-Selecthard

Which THREE factors are critical to consider when designing a custom vision solution for a manufacturing quality inspection system?

Select 3 answers
A.Imbalance between defective and non-defective product samples.
B.Variation in lighting conditions across different inspection stations.
C.Inference latency requirements for real-time decisions.
D.The need for optical character recognition (OCR) of product serial numbers.
E.Multilingual support for labeling.
AnswersA, B, C

Class imbalance leads to biased models.

Why this answer

Class imbalance is a critical factor in custom vision solutions for manufacturing quality inspection. If defective samples are rare compared to non-defective ones, the model may become biased toward predicting the majority class, leading to poor recall for defects. Azure Custom Vision allows adjusting the probability threshold and using techniques like oversampling or weighted loss to mitigate this, but the imbalance must be accounted for during dataset preparation.

Exam trap

The trap here is that candidates may confuse peripheral requirements (like OCR or multilingual labels) with core design factors that directly impact model accuracy, latency, and robustness in a production vision system.

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84
MCQhard

You are using Azure AI Custom Vision to classify images of animals. The training set has 1000 images of cats and 1000 images of dogs. After training, the model performs well on the test set. However, when deployed, it misclassifies images of wolves as dogs. What is the most likely cause?

A.The training set does not include enough negative examples that look like dogs but are not.
B.The probability threshold is set too low.
C.The model is overfitted to the training data.
D.The training set has class imbalance.
AnswerA

The model learned dog features from images lacking wolf-like negatives, so it maps wolf visual traits onto the dog class. Adding negative examples resembling dogs but labelled otherwise would sharpen the decision boundary and satisfy the requirement to classify wolves correctly.

Why this answer

The model misclassifies wolves as dogs because the training set lacks negative examples that are visually similar to dogs but belong to a different class. Custom Vision learns to distinguish classes based on the features present in the training images; without images of wolf-like canines labeled as 'not dog,' the model has no basis to reject wolves. This is a classic case of insufficient hard negative mining, where the model generalizes too broadly for the 'dog' class.

Exam trap

Microsoft often tests the misconception that class imbalance is the primary cause of misclassification, but here the dataset is balanced, and the real issue is the lack of representative negative examples—a subtle but critical distinction in Custom Vision training.

How to eliminate wrong answers

Option B is wrong because the probability threshold controls the confidence required for a prediction, not the model's ability to distinguish between visually similar classes; lowering the threshold would increase false positives, not fix the underlying feature confusion. Option C is wrong because overfitting would cause poor performance on the test set, not specifically misclassify wolves as dogs; the model generalizes well to test images but fails on out-of-distribution examples like wolves. Option D is wrong because class imbalance is not present—the training set has equal numbers of cats and dogs (1000 each)—and imbalance would typically bias predictions toward the majority class, which is not the issue here.

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85
MCQhard

A retail company uses Azure AI Vision to analyze shelf images for inventory management. They notice that the Object Detection model sometimes misses small items. What is the most effective way to improve detection of small objects?

A.Preprocess images to remove background noise.
B.Train a custom object detection model with annotated images that include small objects.
C.Use the Background Removal API to isolate items.
D.Increase the image resolution before sending to the API.
AnswerB

Custom training with annotated images containing small objects teaches the model the specific visual features and scale variation needed, directly addressing missed detections. Generic pre-trained models lack this domain tuning, so retraining on representative shelf imagery is the effective remedy.

Why this answer

Training a custom object detection model with annotated images that include small objects directly improves the model's ability to detect them. Option A is wrong because preprocessing to remove background noise does not specifically target small object detection; the model may still miss small items. Option C is wrong because the Background Removal API is used for isolating items from the background, not for improving detection accuracy.

Option D is wrong although higher resolution can help, it is not as effective as training a custom model with properly annotated small objects, and it may increase cost and latency.

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86
Multi-Selecthard

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

Select 3 answers
A.Extract text from scanned receipts.
B.Export a trained model to ONNX format for offline inference.
C.Train a model to classify images of different product types.
D.Detect and locate multiple objects in an image with bounding boxes.
E.Identify specific individuals in a crowd using facial recognition.
AnswersB, C, D

Exporting a trained model to ONNX format is supported by Custom Vision, enabling offline inference on edge devices without cloud connectivity. This satisfies the scenario's requirement for local, disconnected prediction, since ONNX provides a portable, framework-agnostic representation that runs outside Azure while preserving the trained classifier's behaviour.

Why this answer

Option B is correct because Azure Custom Vision supports exporting trained models in several formats, including ONNX, TensorFlow, CoreML, and Docker, enabling offline or edge inference. Option C is correct because Custom Vision is designed for image classification, allowing you to train a model that assigns images to labeled classes such as product types. Option D is correct because Custom Vision also supports object detection, which returns bounding boxes and labels for multiple objects within an image.

Option A is not correct because extracting text from scanned receipts is an OCR task handled by Azure AI Vision (Computer Vision Read API) or Document Intelligence, not Custom Vision. Option E is not correct because identifying specific individuals via facial recognition is provided by Azure AI Face, not Custom Vision.

Exam trap

The trap here is that candidates may confuse Azure Custom Vision's capabilities with other Azure AI services, mistakenly thinking it handles OCR (like Form Recognizer) or facial recognition (like Face API), when Custom Vision is strictly for custom image classification and object detection.

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87
MCQmedium

A company wants to use Azure AI Vision to extract text from scanned documents that contain both printed and handwritten text in multiple languages. The documents are large and can take several minutes to process. The solution must return the extracted text asynchronously. Which Azure AI Vision API should they use?

A.Read API
B.Image Analysis API with the read feature
C.OCR API
D.Document Intelligence prebuilt-read model
AnswerA

The Read API is designed for asynchronous extraction of printed and handwritten text from documents and images. It supports multiple languages, handles large documents, and returns results via an operation that you poll. It is the correct choice for large scanned documents with mixed printed and handwritten text that require asynchronous processing and multi-language support.

Why this answer

The Read API in Azure AI Vision is designed for asynchronous extraction of printed and handwritten text from large documents and supports multiple languages. It returns results via a polling operation, which suits documents that take minutes to process. The OCR API is synchronous and limited for handwriting, while the Document Intelligence prebuilt-read model, though capable, is not an Azure AI Vision API.

Exam trap

The trap here is assuming that the OCR API can handle handwritten text and large documents asynchronously, when it is actually a synchronous, printed-text-focused API.

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88
MCQhard

A financial services company is building a computer vision solution to automatically extract data from scanned checks. The solution must recognize handwritten amounts, printed account numbers, and signature presence. The company has a large dataset of labeled check images. They need high accuracy and the ability to retrain with new data. Which Azure service should they use?

A.Azure AI Vision OCR with a custom dataset using Custom Vision
B.Azure AI Language with custom entity recognition
C.Azure AI Document Intelligence (Form Recognizer) with a custom model trained on check images
D.Azure AI Vision Image Analysis with a custom model
AnswerC

A custom Document Intelligence model trains on your labelled check images, learning the specific layouts, handwriting and field positions, and supports retraining as new data arrives. This satisfies the high-accuracy and retraining constraints for handwritten amounts, printed account numbers and signature presence.

Why this answer

Azure AI Document Intelligence (Form Recognizer) with a custom model is designed for extracting structured fields from domain-specific documents like checks, and it supports training on your labeled dataset with the ability to retrain as new data arrives. It handles handwriting, printed text, and layout, and can be trained to detect signature presence as a labeled field. This matches the accuracy and retraining requirements.

Exam trap

AI-102 often tests whether candidates confuse Document Intelligence (structured document extraction with custom training) with Vision OCR (generic text extraction) or Custom Vision (image classification), so they pick a service that cannot learn custom fields.

How to eliminate wrong answers

Option A is wrong because Custom Vision does object detection/classification, not text extraction, and Vision OCR alone cannot learn custom field schemas or detect signatures. Option B is wrong because Azure AI Language operates on text, not scanned images, so it cannot read checks. Option D is wrong because Image Analysis with a custom model is for image classification/object detection tasks, not structured document field extraction with handwriting and signature detection.

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89
MCQeasy

A developer is building a mobile app that uses Azure AI Vision to generate a descriptive caption for user-uploaded photos. The app must return a human-readable sentence describing the main content of each image. Which Image Analysis feature should the developer use?

A.Tags
B.Objects
C.Read
D.Caption
AnswerD

The Caption feature in Image Analysis generates a single, human-readable sentence that describes the main content of an image, such as 'a person riding a bike on a beach'. It is specifically designed for this purpose and returns a confidence score for the caption. This directly meets the requirement of producing a descriptive sentence for each photo.

Why this answer

The Caption feature of Image Analysis is purpose-built to generate a one-sentence description of an image's main content. Tags, Objects, and Read serve different purposes: tags provide keywords, objects give bounding boxes, and Read extracts text. Only Caption produces the natural language sentence needed for the mobile app.

Exam trap

The trap here is assuming that tags or objects can be concatenated into a sentence, but only the Caption feature is designed to output a fluent description.

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90
Multi-Selectmedium

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

Select 2 answers
A.Azure AI Translator
B.Azure AI Search
C.Azure AI Vision OCR
D.Azure AI Document Intelligence
E.Azure AI Language
AnswersC, D

Azure AI Vision's Read OCR engine extracts printed and handwritten text from images and scanned documents. It returns lines and words with bounding boxes, making it suitable for pulling text out of photographs and image-only PDFs where no embedded text layer exists.

Why this answer

Azure AI Vision OCR (option C) is correct because its Read/OCR capability extracts printed and handwritten text directly from images and scanned documents. Azure AI Document Intelligence (option D) is correct because it uses prebuilt and custom models to extract text, key-value pairs, and tables from PDFs, images, and forms. Azure AI Translator (A) performs text translation, not optical character recognition, so it cannot extract text from images or PDFs.

Azure AI Search (B) is a search indexing service that can consume extracted text but does not itself perform OCR on images or PDFs. Azure AI Language (E) provides NLP features such as sentiment analysis and entity recognition on existing text, not text extraction from images or PDFs.

Exam trap

The trap here is that candidates may confuse Azure AI Language's text analysis capabilities with OCR, or assume Azure AI Search can extract text directly, when in fact it only indexes pre-extracted data.

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