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CCNA Image Video Processing Questions

17 questions · Image Video Processing topic · All types, answers revealed

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1
Multi-Selecteasy

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

Select 2 answers
A.Azure Form Recognizer.
B.Azure Video Analyzer for Media.
C.Azure AI Content Safety.
D.Azure Computer Vision (OCR).
E.Azure Cognitive Search.
AnswersC, D

Can detect and redact sensitive content.

Why this answer

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

Exam trap

The trap here is that candidates may confuse Azure Form Recognizer's document analysis with general image text processing, overlooking that Content Safety is the specific service for PII detection and redaction, while Computer Vision provides the OCR foundation.

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2
MCQmedium

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

A.Use the pre-built 'products' model from Computer Vision.
B.Increase the confidence threshold in the API call.
C.Retrain the custom object detection model with images of the new products.
D.Recreate the Computer Vision resource in a different region.
AnswerC

Custom models need retraining with new labeled data.

Why this answer

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

Exam trap

The trap here is that candidates may assume a pre-built model or a simple threshold tweak can handle new object classes, when in fact custom object detection requires retraining with labeled examples of the new items.

How to eliminate wrong answers

Option A is wrong because the pre-built 'products' model from Computer Vision is a fixed, general-purpose model that cannot be extended to recognize custom or newly introduced product lines. Option B is wrong because increasing the confidence threshold would only filter out low-confidence detections, not enable detection of entirely new object classes that the model was never trained to recognize. Option D is wrong because recreating the Computer Vision resource in a different region has no impact on the model's ability to detect new products; region selection affects data residency and latency, not model capabilities.

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3
MCQeasy

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

A.Use a different classification algorithm.
B.Add at least 50 more images per class with variations.
C.Reduce the image resolution to speed up training.
D.Increase the number of training iterations (epochs).
AnswerB

More data improves model accuracy.

Why this answer

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

Exam trap

The trap here is that candidates often assume increasing epochs or changing the algorithm will fix low accuracy, but the real bottleneck is insufficient and non-diverse training data, which is the most common cause of poor Custom Vision model performance.

How to eliminate wrong answers

Option A is wrong because Azure Custom Vision automatically selects and tunes the underlying classification algorithm (a convolutional neural network) based on the dataset; manually changing the algorithm is not supported and would not address the core issue of insufficient training data. Option C is wrong because reducing image resolution can discard important fine-grained features needed to detect defects, and Azure Custom Vision already resizes images to a fixed input size (e.g., 224x224) during training, so further reduction harms accuracy rather than improving it. Option D is wrong because increasing training iterations (epochs) with only 10 images per class will cause the model to overfit to the small dataset, memorizing the training examples rather than learning generalizable patterns, leading to poor accuracy on new images.

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4
Multi-Selecthard

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

Select 3 answers
A.An Azure Video Analyzer for Media account.
B.A custom indexing pipeline.
C.An Azure Media Services account.
D.A custom language model.
E.An Azure Storage account to store video files.
AnswersA, C, E

The service itself requires an account.

Why this answer

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

Exam trap

The trap here is that candidates often mistake optional customization features (like custom language models or custom pipelines) as mandatory requirements, when in fact the core prerequisites are the three Azure resources: Video Analyzer for Media account, Media Services account, and Storage account.

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5
Drag & Dropmedium

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

Drag steps to the numbered slots on the right, or tap a step then tap a slot.

Steps
Order

Why this order

Start by creating the search service, define the index, then create the custom skill, set up the indexer with the skillset, and finally run it.

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6
Matchingmedium

Match each Azure AI feature to the service that provides it.

Drag a concept onto its matching description — or click a concept then click the description.

Concepts
Matches

Text Analytics

Face API

Custom Speech

LUIS

Computer Vision

Why these pairings

These features are part of specific Azure Cognitive Services.

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7
MCQhard

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

A.Increase the video resolution sent to the cloud.
B.Set a high minimum confidence threshold for detection.
C.Enable cloud-based processing for all frames.
D.Use multiple AI models simultaneously.
AnswerB

Filters out low-confidence results, saving compute.

Why this answer

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

Exam trap

The trap here is that candidates often assume increasing cloud processing (Option C) or using more models (Option D) improves accuracy, but they overlook the critical constraint of limited compute and network on the edge device, which makes local filtering via confidence thresholds the correct latency-reducing strategy.

How to eliminate wrong answers

Option A is wrong because increasing video resolution sent to the cloud increases the data size per frame, which consumes more network bandwidth and processing time on the edge device, worsening latency rather than reducing it. Option C is wrong because enabling cloud-based processing for all frames would require continuous high-bandwidth uploads from the edge device, defeating the purpose of edge processing and increasing end-to-end latency due to network round trips. Option D is wrong because using multiple AI models simultaneously increases the computational load on the resource-constrained edge device, leading to higher processing latency and potential queueing delays.

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8
MCQmedium

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

A.The video resolution is too low for OCR.
B.The speaker identification model has not been trained with voice samples.
C.The video format is not supported.
D.The language is not set correctly.
AnswerB

Speaker names require custom voice identification.

Why this answer

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

Exam trap

The trap here is that candidates may confuse speaker identification with automatic diarization or assume that speaker names are automatically extracted from the video metadata, when in fact Azure Video Indexer requires explicit training of a custom Person Model with voice samples to assign names.

How to eliminate wrong answers

Option A is wrong because OCR (optical character recognition) is used for extracting text from video frames, not for identifying speakers or generating transcripts; low resolution would affect text extraction but not speaker name assignment. Option C is wrong because Azure Video Indexer supports a wide range of common video formats (e.g., MP4, MOV, AVI), and an unsupported format would cause a failure to index or generate any transcript, not just missing speaker names. Option D is wrong because setting the language incorrectly would affect the accuracy of the speech-to-text transcription (e.g., wrong words or gibberish), but it would not prevent speaker names from appearing; speaker identification is a separate model that requires training regardless of language.

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9
MCQhard

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

A.Use Azure AI Content Safety to post-process transcriptions.
B.Disable indexing for videos containing PHI.
C.Enable content moderation in Video Analyzer for Media settings.
D.Use the 'delete' API to remove all transcripts after processing.
AnswerC

Content moderation can detect and redact PHI.

Why this answer

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

Exam trap

The trap here is that candidates often confuse Azure AI Content Safety (which handles general content moderation) with Video Analyzer for Media's built-in content moderation (which specifically targets PII/PHI in video transcriptions), leading them to choose Option A instead of the more direct and integrated solution.

How to eliminate wrong answers

Option A is wrong because Azure AI Content Safety is designed to detect harmful or inappropriate content (e.g., hate speech, violence), not specifically to redact PHI from transcriptions; it would require custom logic and post-processing, which is less efficient and not the intended use. Option B is wrong because disabling indexing for videos containing PHI would prevent any insights from being extracted, which defeats the purpose of using Video Analyzer for Media for surgical videos; it does not address the requirement to extract insights while avoiding PHI storage. Option D is wrong because using the 'delete' API to remove transcripts after processing still results in PHI being temporarily stored, which violates compliance requirements; the goal is to prevent storage altogether, not to delete it after the fact.

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10
MCQhard

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

A.Enable data encryption at rest.
B.Set a retention policy on the Face API resource.
C.Use the Face API Delete operation to remove stored face IDs.
D.Recreate the Face API resource every 30 days.
AnswerC

Explicitly deleting face data meets GDPR requirements.

Why this answer

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

Exam trap

The trap here is that candidates confuse data encryption (which protects data at rest) with data lifecycle management (which deletes data), or assume that Azure services have a universal retention policy setting, when in fact Face API requires explicit API calls to delete stored face data.

How to eliminate wrong answers

Option A is wrong because enabling data encryption at rest protects data from unauthorized access but does not delete or expire data after a specific time period, so it does not satisfy the 30-day deletion requirement. Option B is wrong because Azure Face API does not support a built-in retention policy on the resource itself; retention policies are available for other Azure services like storage accounts or Log Analytics, not for Face API resources. Option D is wrong because recreating the Face API resource every 30 days would delete all resource-level configurations and potentially impact other workloads, but it is an inefficient and disruptive approach compared to using the targeted Delete operation to remove only the stored face IDs.

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11
MCQmedium

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

A.Use a different prebuilt model.
B.Disable the OCR step.
C.Increase the confidence threshold.
D.Custom train a model with labeled examples of that vendor's invoices.
AnswerD

Custom model learns vendor-specific layouts.

Why this answer

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

Exam trap

The trap here is that candidates assume increasing the confidence threshold (Option C) will fix extraction errors, but it only filters results rather than improving the underlying model's accuracy for vendor-specific formats.

How to eliminate wrong answers

Option A is wrong because using a different prebuilt model (e.g., from receipt to invoice) would not address vendor-specific variations; all prebuilt models are trained on generic datasets and lack customization for a single vendor's patterns. Option B is wrong because disabling the OCR step would prevent text extraction entirely, making data capture impossible; OCR is a foundational step in Form Recognizer's pipeline. Option C is wrong because increasing the confidence threshold only filters out low-confidence results, it does not correct extraction errors; it may reduce false positives but will not improve the model's ability to correctly parse vendor-specific fields.

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12
MCQeasy

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

A.Video Analyzer S1 tier.
B.Free F0 tier.
C.Custom Vision S0 tier.
D.Computer Vision S1 tier.
AnswerD

S1 offers up to 30 calls per second.

Why this answer

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

Exam trap

The trap here is that candidates may confuse the Computer Vision S1 tier with the Custom Vision S0 tier or the Video Analyzer S1 tier, not realizing that each Azure AI service has its own distinct pricing tiers and that the question specifically targets the Computer Vision service for image analysis.

How to eliminate wrong answers

Option A is wrong because Video Analyzer S1 is a pricing tier for Azure Video Analyzer, a service for video indexing and analysis, not for image analysis with Computer Vision. Option B is wrong because the Free F0 tier has severe rate limits (e.g., 20 calls per minute) and is intended only for evaluation and small-scale testing, not for production apps with many requests. Option C is wrong because Custom Vision S0 is a tier for the Custom Vision service, which is used to train and deploy custom image classification models, not the general-purpose Computer Vision API for analyzing images.

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13
Multi-Selectmedium

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

Select 2 answers
A.Include negative samples in the dataset.
B.Use the compact domain for faster training.
C.Reduce the number of training images.
D.Use a larger image size for higher accuracy.
E.Increase the number of training iterations.
AnswersB, C

Compact domain reduces training time and cost.

Why this answer

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

Exam trap

The trap here is that candidates often confuse 'faster training' with 'reduced cost' but may overlook that compact domains specifically lower resource consumption, while options like reducing images or iterations seem intuitive but are not explicitly cost-reduction features in the exam context.

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14
Multi-Selectmedium

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

Select 3 answers
A.The need for custom model retraining over time.
B.Whether the solution runs on edge devices.
C.The amount of labeled training data available.
D.The geographic region of the Azure subscription.
E.Whether the detection objects are generic or domain-specific.
AnswersA, C, E

Custom Vision allows retraining.

Why this answer

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

Exam trap

Microsoft often tests the misconception that edge deployment is exclusive to Custom Vision, but in reality, both services support containerized edge deployment, so the true differentiator is the need for custom retraining and domain-specific detection.

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15
Matchingmedium

Match each Azure Cognitive Services endpoint to its purpose.

Drag a concept onto its matching description — or click a concept then click the description.

Concepts
Matches

Analyze sentiment of text

Generate description of an image

Query a knowledge base

Detect faces in an image

Translate text between languages

Why these pairings

These are common REST API endpoints for Azure Cognitive Services.

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16
MCQhard

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

A.Increase the image size limit.
B.Check the API version used in the application code and compare with the latest version.
C.Switch to a custom model trained on clothing items.
D.Reduce the confidence threshold to 50% to see if more tags appear.
AnswerB

API updates may change behavior; rolling back or updating code may fix.

Why this answer

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

Exam trap

The trap here is that candidates may jump to retraining a custom model (Option C) or adjusting confidence thresholds (Option D) without first verifying the API version, which is the simplest and most cost-effective diagnostic step in Azure AI troubleshooting.

How to eliminate wrong answers

Option A is wrong because increasing the image size limit does not address incorrect labeling; the images are already within supported size limits and the issue is with tag accuracy, not file size. Option C is wrong because switching to a custom model is a significant investment and should only be considered after verifying that the pre-built API version is not the root cause; the problem may be a temporary API regression that a version rollback could fix. Option D is wrong because reducing the confidence threshold to 50% would increase the number of tags but not correct the mislabeling of a shirt as 'shoe'; it would likely introduce more false positives without fixing the core issue.

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17
Drag & Dropmedium

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

Drag steps to the numbered slots on the right, or tap a step then tap a slot.

Steps
Order

Why this order

Start with labeled images, create resource, train, evaluate, then publish.

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