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Implement image and video processing solutions practice questions

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Practise Microsoft Azure AI Engineer Associate AI-102 Implement image and video processing solutions practice questions — original exam-style scenarios with answer choices, explanations, and analysis of common mistakes.

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17 questionsDomain: Implement image and video processing solutions

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What the exam tests

What to know about Implement image and video processing solutions

Implement image and video processing solutions questions test whether you can apply the concept in context, not just recognise a definition.

How the topic appears in realistic exam-style scenarios.

Which detail in the question changes the correct answer.

How to eliminate plausible but wrong options.

How to connect the question back to the wider exam objective.

Watch out for

Common Implement image and video processing solutions exam traps

▸Answering from memory before reading the full scenario.
▸Missing a constraint such as cost, availability, security, scope or command context.
▸Choosing a broad answer when the question asks for the most specific fix.
▸Ignoring why the wrong options are tempting.

Practice set

Implement image and video processing solutions questions

17 questions · select your answer, then reveal the explanation

Question 1mediummultiple choice

Read the full Implement image and video processing solutions explanation →

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?

Trap 1: Use the pre-built 'products' model from Computer Vision.

Pre-built models may not include the new product line.

Trap 2: Increase the confidence threshold in the API call.

Adjusting threshold does not add new objects to detect.

Trap 3: Recreate the Computer Vision resource in a different region.

Region change does not affect model capabilities.

Study all Implement image and video processing solutions common traps →

A
Use the pre-built 'products' model from Computer Vision.
Why wrong: Pre-built models may not include the new product line.
B
Increase the confidence threshold in the API call.
Why wrong: Adjusting threshold does not add new objects to detect.
C
Retrain the custom object detection model with images of the new products.
Custom models need retraining with new labeled data.
D
Recreate the Computer Vision resource in a different region.
Why wrong: Region change does not affect model capabilities.

Full breakdown with real-world context →

Question 2hardmultiple choice

Read the full NAT/PAT explanation →

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?

Trap 1: Use Azure AI Content Safety to post-process transcriptions.

Not natively integrated; requires custom solution.

Trap 2: Disable indexing for videos containing PHI.

No such selective disablement feature.

Trap 3: Use the 'delete' API to remove all transcripts after processing.

Still temporarily stores PHI during processing.

Study all Implement image and video processing solutions common traps →

A
Use Azure AI Content Safety to post-process transcriptions.
Why wrong: Not natively integrated; requires custom solution.
B
Disable indexing for videos containing PHI.
Why wrong: No such selective disablement feature.
C
Enable content moderation in Video Analyzer for Media settings.
Content moderation can detect and redact PHI.
D
Use the 'delete' API to remove all transcripts after processing.
Why wrong: Still temporarily stores PHI during processing.

Full breakdown with real-world context →

Question 3easymultiple choice

Read the full Implement image and video processing solutions explanation →

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?

Trap 1: Use a different classification algorithm.

Custom Vision uses advanced algorithms; data is the issue.

Trap 2: Reduce the image resolution to speed up training.

Does not improve accuracy.

Trap 3: Increase the number of training iterations (epochs).

May overfit with small dataset.

Study all Implement image and video processing solutions common traps →

A
Use a different classification algorithm.
Why wrong: Custom Vision uses advanced algorithms; data is the issue.
B
Add at least 50 more images per class with variations.
More data improves model accuracy.
C
Reduce the image resolution to speed up training.
Why wrong: Does not improve accuracy.
D
Increase the number of training iterations (epochs).
Why wrong: May overfit with small dataset.

Full breakdown with real-world context →

Question 4mediummultiple choice

Read the full Implement image and video processing solutions explanation →

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?

Trap 1: The video resolution is too low for OCR.

OCR extracts text, not speaker names.

Trap 2: The video format is not supported.

Format support does not affect speaker identification.

Trap 3: The language is not set correctly.

Language detection affects transcription, not speaker names.

Study all Implement image and video processing solutions common traps →

A
The video resolution is too low for OCR.
Why wrong: OCR extracts text, not speaker names.
B
The speaker identification model has not been trained with voice samples.
Speaker names require custom voice identification.
C
The video format is not supported.
Why wrong: Format support does not affect speaker identification.
D
The language is not set correctly.
Why wrong: Language detection affects transcription, not speaker names.

Full breakdown with real-world context →

Question 5hardmultiple choice

Read the full Implement image and video processing solutions explanation →

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?

Trap 1: Enable data encryption at rest.

Encryption does not delete data.

Trap 2: Set a retention policy on the Face API resource.

No such policy exists for Face API.

Trap 3: Recreate the Face API resource every 30 days.

Unnecessary and causes service disruption.

Study all Implement image and video processing solutions common traps →

A
Enable data encryption at rest.
Why wrong: Encryption does not delete data.
B
Set a retention policy on the Face API resource.
Why wrong: No such policy exists for Face API.
C
Use the Face API Delete operation to remove stored face IDs.
Explicitly deleting face data meets GDPR requirements.
D
Recreate the Face API resource every 30 days.
Why wrong: Unnecessary and causes service disruption.

Full breakdown with real-world context →

Question 6easymultiple choice

Read the full Implement image and video processing solutions explanation →

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?

Trap 1: Video Analyzer S1 tier.

For video indexing, not image analysis.

Trap 2: Free F0 tier.

Free tier has 20 calls per minute limit.

Trap 3: Custom Vision S0 tier.

Custom Vision is separate from Computer Vision.

Study all Implement image and video processing solutions common traps →

A
Video Analyzer S1 tier.
Why wrong: For video indexing, not image analysis.
B
Free F0 tier.
Why wrong: Free tier has 20 calls per minute limit.
C
Custom Vision S0 tier.
Why wrong: Custom Vision is separate from Computer Vision.
D
Computer Vision S1 tier.
S1 offers up to 30 calls per second.

Full breakdown with real-world context →

Question 7mediummultiple choice

Read the full Implement image and video processing solutions explanation →

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?

Trap 1: Use a different prebuilt model.

Other prebuilt models may not cover invoices.

Trap 2: Disable the OCR step.

OCR is essential for text extraction.

Trap 3: Increase the confidence threshold.

May reduce errors but not address format.

Study all Implement image and video processing solutions common traps →

A
Use a different prebuilt model.
Why wrong: Other prebuilt models may not cover invoices.
B
Disable the OCR step.
Why wrong: OCR is essential for text extraction.
C
Increase the confidence threshold.
Why wrong: May reduce errors but not address format.
D
Custom train a model with labeled examples of that vendor's invoices.
Custom model learns vendor-specific layouts.

Full breakdown with real-world context →

Question 8hardmultiple choice

Read the full Implement image and video processing solutions explanation →

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?

Trap 1: Increase the video resolution sent to the cloud.

Higher resolution increases latency.

Trap 2: Enable cloud-based processing for all frames.

Adds network latency.

Trap 3: Use multiple AI models simultaneously.

Increases compute load.

Study all Implement image and video processing solutions common traps →

A
Increase the video resolution sent to the cloud.
Why wrong: Higher resolution increases latency.
B
Set a high minimum confidence threshold for detection.
Filters out low-confidence results, saving compute.
C
Enable cloud-based processing for all frames.
Why wrong: Adds network latency.
D
Use multiple AI models simultaneously.
Why wrong: Increases compute load.

Full breakdown with real-world context →

Question 9mediummulti select

Read the full Implement image and video processing solutions explanation →

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

Trap 1: Include negative samples in the dataset.

Negative samples improve accuracy but increase cost.

Trap 2: Use a larger image size for higher accuracy.

Larger images require more compute.

Trap 3: Increase the number of training iterations.

More iterations cost more.

Study all Implement image and video processing solutions common traps →

A
Include negative samples in the dataset.
Why wrong: Negative samples improve accuracy but increase cost.
B
Use the compact domain for faster training.
Compact domain reduces training time and cost.
C
Reduce the number of training images.
Fewer images reduce transaction costs.
D
Use a larger image size for higher accuracy.
Why wrong: Larger images require more compute.
E
Increase the number of training iterations.
Why wrong: More iterations cost more.

Full breakdown with real-world context →

Question 10hardmulti select

Read the full Implement image and video processing solutions explanation →

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

Trap 1: A custom indexing pipeline.

Indexing is built-in.

Trap 2: A custom language model.

Not required; default models work.

Study all Implement image and video processing solutions common traps →

A
An Azure Video Analyzer for Media account.
The service itself requires an account.
B
A custom indexing pipeline.
Why wrong: Indexing is built-in.
C
An Azure Media Services account.
Video Analyzer for Media integrates with Media Services.
D
A custom language model.
Why wrong: Not required; default models work.
E
An Azure Storage account to store video files.
Videos are stored in Azure Storage.

Full breakdown with real-world context →

Question 11easymulti select

Read the full Implement image and video processing solutions explanation →

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

Trap 1: Azure Form Recognizer.

Extracts data from forms, not general PII redaction.

Trap 2: Azure Video Analyzer for Media.

Primarily for video insights, not image PII redaction.

Trap 3: Azure Cognitive Search.

Search does not process images.

Study all Implement image and video processing solutions common traps →

A
Azure Form Recognizer.
Why wrong: Extracts data from forms, not general PII redaction.
B
Azure Video Analyzer for Media.
Why wrong: Primarily for video insights, not image PII redaction.
C
Azure AI Content Safety.
Can detect and redact sensitive content.
D
Azure Computer Vision (OCR).
OCR extracts text; can be used to detect PII.
E
Azure Cognitive Search.
Why wrong: Search does not process images.

Full breakdown with real-world context →

Question 12mediummulti select

Read the full Implement image and video processing solutions explanation →

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

Trap 1: Whether the solution runs on edge devices.

Both support edge deployment.

Trap 2: The geographic region of the Azure subscription.

Both services are available globally.

Study all Implement image and video processing solutions common traps →

A
The need for custom model retraining over time.
Custom Vision allows retraining.
B
Whether the solution runs on edge devices.
Why wrong: Both support edge deployment.
C
The amount of labeled training data available.
Custom Vision requires labeled data.
D
The geographic region of the Azure subscription.
Why wrong: Both services are available globally.
E
Whether the detection objects are generic or domain-specific.
Custom Vision for domain-specific objects.

Full breakdown with real-world context →

Question 13hardmultiple choice

Read the full Implement image and video processing solutions explanation →

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?

Trap 1: Increase the image size limit.

Size is within limits; not the cause.

Trap 2: Switch to a custom model trained on clothing items.

Custom model may not be necessary; issue likely version-related.

Trap 3: Reduce the confidence threshold to 50% to see if more tags appear.

May worsen accuracy.

Study all Implement image and video processing solutions common traps →

A
Increase the image size limit.
Why wrong: Size is within limits; not the cause.
B
Check the API version used in the application code and compare with the latest version.
API updates may change behavior; rolling back or updating code may fix.
C
Switch to a custom model trained on clothing items.
Why wrong: Custom model may not be necessary; issue likely version-related.
D
Reduce the confidence threshold to 50% to see if more tags appear.
Why wrong: May worsen accuracy.

Full breakdown with real-world context →

Question 14mediumdrag order

Read the full Implement image and video processing solutions explanation →

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

1Step 1

2Step 2

3Step 3

4Step 4

5Step 5

Question 15mediumdrag order

Read the full Implement image and video processing solutions explanation →

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

1Step 1

2Step 2

3Step 3

4Step 4

5Step 5

Question 16mediummatching

Read the full Implement image and video processing solutions explanation →

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

Question 17mediummatching

Read the full Implement image and video processing solutions explanation →

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

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What does the AI-102 exam test about Implement image and video processing solutions?: Implement image and video processing solutions questions test whether you can apply the concept in context, not just recognise a definition.
How should I use these practice questions?: Select your answer before revealing the explanation. Then read why each option is right or wrong — this active recall approach builds retention far faster than re-reading notes.
Can I practise just Implement image and video processing solutions questions in a focused session?: Yes — the session launcher on this page draws every question from the Implement image and video processing solutions domain. Use a 10-question session first to gauge your baseline, then move to 20 or 30 once the weak spots are clear.
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Exam traps to avoid

▸Answering from memory before reading the full scenario.
▸Missing a constraint such as cost, availability, security, scope or command context.
▸Choosing a broad answer when the question asks for the most specific fix.
▸Ignoring why the wrong options are tempting.

Implement image and video processing solutions practice questions

What to know about Implement image and video processing solutions

Common Implement image and video processing solutions exam traps

Implement image and video processing solutions questions

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 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 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 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 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 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 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 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?

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

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

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

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

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

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

Match each Azure Cognitive Services endpoint to its purpose.

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

Track your progress over time

Start a Implement image and video processing solutions only practice session

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