Explanation:

The correct answers are Yes, Yes, and Yes.
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn content in the section "Describe features of conversational AI workloads on Azure", bots created using Azure Bot Service can interact with users across multiple channels. The AI-900 syllabus explains that Azure Bot Service integrates with various communication platforms, allowing developers to build a single bot that can be deployed in many contexts without rewriting the logic.
* "You can communicate with a bot by using Cortana." - Yes.The AI-900 learning materials explain that Cortana, Microsoft's intelligent personal assistant, can serve as a channel for bots built with the Azure Bot Service. Through the Bot Framework, bots can be connected to Cortana to allow users to interact via voice or text. Although Cortana is less prominent now, it remains conceptually included in the AI-
900 coverage as an example of a voice-based conversational AI channel.
* "You can communicate with a bot by using Microsoft Teams." - Yes.This statement is true and directly referenced in the AI-900 syllabus. Microsoft Teams is a fully supported communication channel for Azure Bot Service. Bots in Teams can handle chat messages, commands, and interactions in team or personal contexts. The Microsoft Learn materials specify Teams as one of the native connectors where enterprise users can interact with organizational bots.
* "You can communicate with a bot by using a webchat interface." - Yes.This is also true. The Web Chat channel is one of the most common ways to deploy bots publicly. Azure Bot Service provides a Web Chat control that can be embedded directly into a webpage or web application. This allows users to interact with the bot using a chat window, just like on customer service websites.
Therefore, all three interfaces-Cortana (voice-based), Microsoft Teams (enterprise chat), and Web Chat (browser-based)-are valid and officially supported communication channels for Azure bots.

To prevent hateful or harmful responses from being returned by a generative AI solution, the correct approach is to implement content filtering. According to the Microsoft Learn documentation for Azure OpenAI Service and the Responsible AI principles, content filtering is a built-in safety mechanism that automatically screens both user prompts (inputs) and model outputs (responses) for inappropriate, harmful, or policy-violating material.
Content filters are designed to detect and block content such as:
* Hate speech or harassment
* Sexual or explicit material
* Self-harm or violent content
* Personally identifiable information (PII) misuse
In Azure OpenAI, the content filtering system is part of Microsoft's Responsible AI standard and cannot be disabled. It ensures that generative AI models such as GPT-3.5 or GPT-4 operate safely and ethically, reducing the risk of producing offensive or discriminatory text. The filter evaluates model responses in real time and can modify, block, or flag inappropriate outputs before they reach the user.
Let's review the other options:
* A. Abuse monitoring tracks misuse after deployment but does not actively prevent hateful responses.
* C. Fine-tuning customizes a model's style or domain knowledge but does not guarantee filtering of offensive content.
* D. Prompt engineering helps steer model behavior but cannot fully prevent harmful outputs.
Therefore, to proactively prevent hateful, unsafe, or offensive responses in a generative AI system built on Azure OpenAI, the correct and Microsoft-verified approach is B. Content filtering.

Explanation:

Box 1: Select Columns in Dataset
For Columns to be cleaned, choose the columns that contain the missing values you want to change. You can choose multiple columns, but you must use the same replacement method in all selected columns.
Example:

The task is to build a machine learning model in Azure Machine Learning designer to predict automobile prices, which is a regression problem since the output (price) is a continuous numeric value. The pipeline must follow the logical data preparation, training, and evaluation flow as outlined in the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn module "Create a machine learning model with Azure Machine Learning designer." Here's the correct sequence and reasoning:
* Select Columns in Dataset:The first step after loading the raw automobile dataset is to choose the relevant columns that will be used as features (inputs) and the label (output). This module ensures that only necessary fields (for example, horsepower, engine size, mileage, etc.) are used to train the model while excluding irrelevant columns like vehicle ID or serial number.
* Split Data:Next, the cleaned and filtered dataset must be split into two subsets: training data and testing data (often 70/30 or 80/20). This allows the model to be trained on one portion and evaluated on the other to measure predictive accuracy.
* Linear Regression:Since automobile price prediction is a numeric prediction task, the appropriate learning algorithm is Linear Regression. This supervised algorithm learns relationships between numeric features and the target (price).
Finally, the workflow connects the training data and Linear Regression module to the Train Model module, which outputs a trained regression model. The trained model is then linked to the Score Model module to compare predicted vs. actual prices.
This pipeline fully aligns with Microsoft's recommended process for regression in Azure ML Designer.

The correct answer is A. Personally Identifiable Information (PII) detection.
In the Azure AI Language service, PII detection is a built-in feature designed to automatically identify and redact sensitive or confidential information from text data. According to the Microsoft Learn module
"Identify capabilities of Azure AI Language" and the AI-900 study guide, this capability can detect personal data such as names, phone numbers, email addresses, credit card numbers, and other identifiers.
When applied, the service scans input text and either masks or removes these PII elements based on configurable parameters, ensuring compliance with data privacy regulations like GDPR or HIPAA.
For example, if a document contains "John Doe's phone number is 555-123-4567," PII detection can return
"******'s phone number is ***********," thereby preventing exposure of sensitive personal details.
Option analysis:
* A. Personally Identifiable Information (PII) detection: # Correct. It identifies and masks sensitive data in text.
* B. Entity linking: Connects recognized entities to known data sources like Wikipedia; not used for redaction.
* C. Custom text classification: Classifies text into predefined categories; not designed for masking personal data.
* D. Custom named entity recognition (NER): Detects domain-specific entities you define but doesn't automatically mask them.
Therefore, to automate masking of names and phone numbers, the appropriate Azure AI Language feature is PII detection.

Explanation:

According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and the Microsoft Learn module: "Describe features of common AI workloads", conversational AI solutions like chatbots can be created using various methods-not only through custom code. Azure provides both no-code/low-code and developer-focused approaches. For instance, users can design chatbots using Power Virtual Agents, which requires no programming knowledge, or they can use Azure Bot Service with the Bot Framework SDK for fully customized scenarios. Hence, the statement "Chatbots can only be built by using custom code" is False (No) because Azure supports multiple levels of technical involvement for building bots.
The second statement is True (Yes) because the Azure Bot Service is designed specifically to host, manage, and connect conversational bots to users across different channels. Microsoft Learn explicitly explains that the service provides integrated hosting, connection management, and telemetry for bots built using the Bot Framework or Power Virtual Agents. It acts as the foundation for deploying, scaling, and managing chatbot workloads in Azure.
The third statement is also True (Yes) because Azure Bot Service supports integration with Microsoft Teams, among many other channels such as Skype, Facebook Messenger, Slack, and web chat. Microsoft documentation states that Azure-hosted bots can communicate directly with Teams users through the Teams channel, enabling intelligent virtual assistants within the Teams environment.