Microsoft Updated AI-102 Exam Questions and Answers by zorawar

When testing a bot with the Bot Framework Emulator, especially if the bot uses authentication (OAuth), you need the Emulator to mimic real-world authentication flows. To do that:

Use version 1.0 authentication tokens

Ensures that authentication tokens are generated in the same format as Bot Framework v1.0, which is compatible with most OAuth flows.

Use a sign-in verification code for OAuthCard

Prompts the user with a verification code during sign-in flows, ensuring the emulator behaves like a real client app (e.g., mobile or Teams).

This is required to test sign-in prompts and enforce credential entry.

Use your own user ID to communicate with the bot

Ensures that the Emulator identifies you uniquely and triggers authentication properly.

Without this, the bot may treat sessions generically, and authentication prompts might not behave as expected.

Why not the others?

Application Updates (automatically download/install) → Related to emulator updates, not authentication.

Data Collection → Only for telemetry; does not affect authentication.

Ngrok settings → Used for tunneling remote bot connections; unrelated to credential prompts.

Correct Answer Selections:

Use version 1.0 authentication tokens

Use a sign-in verification code for OAuthCard

Use your own user ID to communicate with the bot

Microsoft References

Bot Framework Emulator - Authentication

Testing bots with Bot Framework Emulator

Comprehensive Detailed Explanation

You are tasked with building a solution using Custom Vision to detect faults in factory components. Custom Vision follows a defined workflow:

Create a project

Before anything else, you must create a Custom Vision project in the Azure AI portal.

This project specifies the type of model you are building: classification (classifying entire images) or object detection (locating items within images).

Since the question is about detecting faults in specific components (a business-specific scenario), it aligns with classification unless explicitly stated otherwise.

Upload and tag images

You must provide training data.

Upload a set of images showing both faulty and non-faulty components.

Tag each image correctly so that the model can learn from labeled examples.

Train the classifier model

Once images are tagged, you run training on the dataset to generate the classifier model.

The model can then be published and used for inference via API calls.

Why not the other options?

Initialize the training dataset: This is not a direct step in Custom Vision; instead, uploading and tagging images inherently prepares the training dataset.

Train the object detection model: Object detection is used if you need bounding boxes around items within an image. Since the question is about recognizing faults in a component (faulty vs non-faulty), a classifier model is appropriate.

Correct Sequence:

Create a project

Upload and tag images

Train the classifier model

Microsoft References

Quickstart: Build an image classification project with the Custom Vision portal

Custom Vision overview

Train and test a Custom Vision model

The requirement is to identify the created Azure AI services account within a DevOps pipeline.

The az cognitiveservices account show command returns the details (name, id, location, etc.) of an existing Azure AI service account.

az resource link creates or manages links between resources, not what we need.

az account list lists Azure subscriptions, not resources.

az cognitiveservices account network-rule manages network access rules but does not return account identity.

Microsoft References:

az cognitiveservices account show

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