Microsoft AI-102 Korean Exam : Designing and Implementing a Microsoft Azure AI Solution (AI-102 Korean Version)

Correct Answer: B,C  🗳️

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Correct Answer:

Explanation:
Project type: Object detection
Domain: General (compact)
The scenario requires building a Custom Vision model for a factory with intermittent internet connectivity.
The solution must:
Detect defects in packaging
Locate the defects (provide bounding box information)
Ensure each package contains four products
Support offline use due to intermittent connectivity
Step 1 - Determine Project Type
Image Classification: Assigns a label to an image, but cannot locate where objects/defects are in the image.
Object Detection: Identifies what objects are present and their location (via bounding boxes).
Since the requirement is to identify location of defects and count products, the correct choice is Object detection.
Step 2 - Determine Domain
General domain: Standard general-purpose domain, optimized for cloud inference.
General (compact) domain: A lightweight version designed for exporting models to run offline on edge devices (iOS, Android, ONNX, etc.). Perfect for scenarios with intermittent internet connectivity.
Food domain: Specialized for food image classification, not defect detection in packaging.
Logo domain: Specialized for logo recognition, irrelevant here.
Since the requirement includes offline capability, the correct domain is General (compact).
The answer:
Project type: Object detection
Domain: General (compact)
Microsoft References
Custom Vision project types: Classification vs Object detection
Domains in Custom Vision (General, General compact, Food, Logo, etc.)
Exporting compact models for offline use

Correct Answer:

Explanation:
Provide access to AI1 by using: A bearer token
Connect to the deployment by using: A deployment name
To ensure only your apps can access the Azure OpenAI resource, authenticate with Microsoft Entra ID (Azure AD) and obtain a Bearer token (ideally via managed identity ). This avoids sharing long-lived API keys and lets you scope access by app identity and Azure RBAC. Microsoft's guidance recommends token- based auth for Azure OpenAI and shows how to acquire a bearer token/managed identity token for calls When calling the REST API , Azure OpenAI requires you to specify the deployment name to select which model deployment to use. The REST route includes {deployment-id} (deployment name) in the path, e.g., ...
/openai/deployments/{deployment-id}/chat/completions. This is explicitly documented as a key difference from OpenAI's public API, which takes only a model.
Therefore:
Access method: Bearer token (via Entra ID/managed identity)
Deployment selector: Deployment name in the REST path
Microsoft References
Authenticate to Azure OpenAI with Microsoft Entra ID / Managed identity (bearer token): guidance and how- to Azure OpenAI request header recommends token-based authentication (Bearer).
Azure OpenAI requires deployment name in API calls; REST path uses

Correct Answer:

Explanation:
The percentage of false positives is [0].
The value for the number of true positives divided by the total number of true positives and false negatives is [100].
The screenshot shows the Performance tab of a Custom Vision object detection model after training.
Two key performance metrics are in focus:
False Positives (FP):
False positives occur when the model incorrectly predicts an object that isn't present.
In the provided exhibit, the model shows 0% false positives.
This means it did not incorrectly classify any non-object as an object.
Recall (True Positives ÷ (True Positives + False Negatives)):
This measures how many of the actual objects were correctly detected.
Formula: Recall = TP / (TP + FN).
In the performance report, recall is 100%, meaning the model correctly detected all the actual objects (no false negatives).
Evaluating the answer choices:
False positives: 0%
Recall (TP ÷ (TP + FN)): 100%
Correct Answers:
False positives # 0
Recall # 100
Microsoft References
Custom Vision performance metrics
Precision, recall, and accuracy in machine learning

Correct Answer:

Explanation:
a tabular form of rows and columns
Rationale
Relational data is, by definition, data that adheres to the relational model .
In the relational model, data is organized into tables , which consist of rows (records or tuples) and columns (fields or attributes). This is known as the tabular form .
The other options are incorrect:
Unstructured data is the opposite of relational data.
A hierarchical folder structure is used for file organization, not the logical structure of relational data.
Comma-separated value (CSV) files can contain relational data, but the core definition of relational data is the tabular structure , not the file format itself.