Pass the Microsoft Azure AI Engineer Associate AI-102 Questions and answers with CertsForce

Azure AI Content Understanding supports common document and image formats including .pdf, .docx, .jpg, and .png. .webp isn’t listed among supported types. Therefore, you can analyze File1.pdf, File2.jpg, File3.docx, and File5.png, but not File4.webp. Microsoft Learn

Microsoft References:

Content Understanding: service limits and supported file types. Microsoft Learn

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To maximize answer accuracy (reduce randomness and hallucinations) with Azure OpenAI GPT-3.5, you should lower the sampling randomness. The primary control for this is temperature:

Temperature controls how “creative” or random the model is.

Lower values → more deterministic, focused, and conservative outputs.

temperature = 0 makes the model pick the highest-probability tokens, which typically yields the most accurate and repeatable responses for transactional bots like booking/reservations.

Top P (nucleus sampling) is an alternative to temperature. Microsoft recommends using one or the other. While reducing Top P also narrows randomness, the clearest and most common approach for maximizing accuracy is setting temperature to 0. Setting Top P to 0 is generally not recommended and may be invalid depending on the SDK.

System message tuning (Option D) helps with tone, role, and policy adherence, but does not itself guarantee factual accuracy if decoding randomness remains high.

Including the travel agent’s database (Option A) would require Azure OpenAI on your data or a RAG pattern. That can improve grounding of facts, but it’s not a model parameter change and is outside the scope of simply configuring GPT-3.5; moreover, accuracy still benefits from low temperature even when using your own data.

Therefore, the best single action here is: set Temperature to 0.

Microsoft Azure AI Solution References

Azure OpenAI Service – Chat Completions parameters (Temperature and Top P): “Lower values like 0.2 will make the output more focused and deterministic… use either temperature or top_p but generally not both.”https://learn.microsoft.com/azure/ai-services/openai/reference#chat-completions

Prompt engineering with Azure OpenAI – controlling randomness and determinism:https://learn.microsoft.com/azure/ai-services/openai/concepts/prompt-engineering

Azure OpenAI on your data (grounding with enterprise data/RAG):https://learn.microsoft.com/azure/ai-services/openai/concepts/use-your-data

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According to the Microsoft documentation, Computer Vision is a cloud-based service that provides developers with access to advanced algorithms for processing images and returning information. By uploading an image or specifying an image URL, Computer Vision algorithms can analyze visual content in different ways based on inputs and user choices.

According to the Microsoft documentation, image type detection is one of the features of Computer Vision that can categorize an image as either a photograph or a drawing. You can use the image type detection feature by calling the Analyze Image API with the visualFeatures parameter set to ImageType. The API will return a JSON response with an imageType field that indicates whether the image is a photo or a clipart.

According to the Microsoft documentation, image descriptions is another feature of Computer Vision that can generate a caption for an image. You can use the image descriptions feature by calling the Analyze Image API with the visualFeatures parameter set to Description. The API will return a JSON response with a description field that contains a list of captions for the image, each with a confidence score.

Therefore, by using these two features of Computer Vision, you can achieve your app requirements with minimal development effort. You don’t need to use any other services, such as object detection, content tags, or Custom Vision, which are designed for different purposes.

We need to design a Conversational Language Understanding (CLU) model for smart home commands.

The sample utterances:

“Set all the lights to on.” → Intent = change setting, Entity = device (lights)

“Turn off the lights in the living room.” → Intent = change setting, Entities = device (lights) + location (living room)

“What is the current thermostat temperature?” → Intent = query setting, Entity = device (thermostat)

“Lower the temperature of the thermostat by five degrees.” → Intent = change setting, Entity = device (thermostat)

Intents needed:

Change setting intent (D): Covers actions like turning devices on/off, lowering temperature.

Query setting intent (E): Covers questions about current state, like “What is the thermostat temperature?”

Entities needed:

Device entity (F): Identifies devices such as lights and thermostat.

A. a location intent → Location (like “living room”) should be modeled as an entity, not an intent.

B. a change setting entity → Change setting is an action (intent), not an entity.

C. a device intent → Devices are entities, not intents.

Breakdown:Why not the others:

Correct Answer:D. a change setting intent

E. a query setting intent

F. a device entity

Intents and entities in CLU

CLU model design best practices

Microsoft References

Upload to: An Azure Storage account

Model type: Custom neural

You have 1,000 scanned hand-written survey responses with no consistent layout. The goal is to extract data while minimizing development effort.

Step 1: Upload location

For Azure AI Document Intelligence (formerly Form Recognizer), training data (images, PDFs) must be placed in a container in an Azure Storage account.

Options like Cosmos DB or Azure Files share are not valid training data sources for Document Intelligence.

Therefore, you upload the survey images to Azure Storage account.

Step 2: Model type

The surveys are handwritten and do not have a consistent layout.

Custom template models work only when the layout is fixed and consistent (e.g., invoices with the same fields in the same place).

Identity document (ID) model is a prebuilt model for passports, driver’s licenses, etc., not for surveys.

Custom neural model is designed to handle unstructured or variable-layout forms, including handwritten content. This best fits the survey scenario.

Thus, the correct configuration is:

Upload to Azure Storage account

Use Custom neural model

Correct Answer:

Upload to: An Azure Storage account

Model type: Custom neural

Azure AI Document Intelligence – Custom models

Custom neural vs custom template models

Train a custom model with Azure Storage input

Microsoft References

Text Extraction: b) Computer Vision

Computer Vision is a service that can analyze images and extract text from them using optical character recognition (OCR) or read API1. OCR can detect and extract printed or handwritten text from images in various languages and formats, such as PDF, TIFF, or JPEG2. Read API can perform asynchronous batch processing of text from multiple images, and can also handle text in tables, mixed languages, and rotated text3.

Profane Language Detection: c) Content Moderator

Content Moderator is a service that can detect and filter out potentially offensive or unwanted content in text, images, and videos. For text, Content Moderator can identify profane or abusive language, personal data, and custom terms that you define. You can also use Content Moderator to review and moderate the content manually or automatically using workflows.

You need to detect PPE compliance (masks or safety glasses) on a periodic schedule (every 15 minutes) while minimizing development effort and cost.

Why Computer Vision (Azure AI Vision)Azure AI Vision (formerly “Computer Vision”) lets you analyze still images with prebuilt Image Analysis and—when needed—train a lightweight custom object detection model directly in Vision Studio without building ML pipelines. You can simply grab a frame (snapshot) from your camera feed every 15 minutes and call the Vision API—this keeps costs low because you pay per image analyzed rather than continuously processing video. Microsoft’s current Vision stack supports image analysis and custom object detection via Vision Studio and v4.0 APIs, designed for scenarios like PPE detection. Microsoft Azure+1

Why not FaceThe Face service is intended for face detection/recognition workflows. While Face can return some accessory attributes (e.g., glasses/mask confidence), Microsoft has scaled back several face attributes under Responsible AI guidance, and Face is not positioned as a general PPE-compliance detector across a scene (multiple people, full body PPE). It’s therefore a poorer fit and can increase false decisions for PPE checks beyond the face region. Azure Docs+3

Why not Azure Video Analyzer for Media (Video Indexer)Video Indexer is optimized for extracting insights from full videos (speech, people, labels, brands). Running it just to check PPE every 15 minutes is overkill and more expensive than analyzing a single still frame with Vision. In addition, Microsoft has been adjusting video services post–Azure Media Services retirement; using snapshot + Vision avoids those complexities and keeps costs minimal.

How this meets the requirements

Identify masks/safety glasses: Use Image Analysis or a quick custom object detector in Vision Studio trained on a small set of PPE images. Microsoft Learn

Every 15 minutes: Capture a single frame from the camera and call the Vision endpoint—simple scheduling with a timer-triggered Function or Logic App, minimal code. (General Azure pattern; Vision API is stateless per image.) Microsoft Azure

Minimize development effort: Vision Studio provides no-code/low-code training and testing for custom detectors if the prebuilt model isn’t sufficient. Microsoft Learn

Minimize costs: Pay-per-call image analysis is cheaper than indexing entire video segments; no continuous streaming required. Microsoft Azure

Answer: B. Computer Vision

Microsoft References

Azure AI Vision (product overview): image and video analysis with OCR, object detection, Image Analysis. Microsoft Azure

What’s new in Azure AI Vision: custom image classification/object detection in Vision Studio and v4.0 APIs. Microsoft Learn

Face detection & attributes (accessories including glasses and mask)—not the right tool for scene-level PPE compliance. Microsoft Learn

Face Detect REST API (attributes list, including glasses/mask). Microsoft Learn

Responsible AI updates indicating changes to facial analysis attributes; facial detection/accessories remain but are limited in scope. Azure Docs

Azure AI Video Indexer release notes/updates (video-centric use cases, ongoing adjustments post–AMS retirement).

The requirement is to extract structured information (invoice items, sales amounts, customer details) from scanned invoice images.

Form Recognizer (now part of Azure AI Document Intelligence) is specifically designed for extracting structured data from documents such as invoices, receipts, and business forms.

Custom Vision → used for image classification or object detection, not for text/data extraction.

Computer Vision → can extract text with OCR, but it doesn’t provide structured field-level extraction for invoices.

Immersive Reader → for accessibility (reading content aloud, translating text), not document data extraction.

Correct Answer: D