Pass the Microsoft Microsoft Certified: Azure AI Fundamentals AI-900 Questions and answers with CertsForce

Object detection is similar to tagging, but the API returns the bounding box coordinates (in pixels) for each object found. For example, if an image contains a dog, cat and person, the Detect operation will list those objects together with their coordinates in the image. You can use this functionality to process the relationships between the objects in an image. It also lets you determine whether there are multiple instances of the same tag in an image.

The Detect API applies tags based on the objects or living things identified in the image. There is currently no formal relationship between the tagging taxonomy and the object detection taxonomy. At a conceptual level, the Detect API only finds objects and living things, while the Tag API can also include contextual terms like " indoor " , which can ' t be localized with bounding boxes.

Yes, No, Yes.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module “Identify capabilities of Azure Cognitive Services for Language”, the Azure Translator service is a cloud-based machine translation service used to translate text or entire documents between languages in real time. It uses REST APIs or client libraries to translate text input, detect languages, and support multiple target languages in a single request.

“The following service call will accept English text as an input and output Italian and French text: /translate?from=en & to=it,fr – Yes.This URL format is correct because the Translator service API allows multiple target languages to be specified in a single to parameter separated by commas. In this case, from=en defines the source language (English), and to=it,fr requests translations into Italian (it) and French (fr). The API would return results in both target languages simultaneously. This syntax is officially documented in Microsoft Learn as the valid format for multi-language translation.

“The following service call will accept English text as an input and output Italian and French text: /translate?from=en & to=fr & to=it – No.This format is incorrect, as the Translator API does not support repeating the to parameter multiple times. Only one to parameter is valid, and multiple target languages must be provided as a comma-separated list within the same to parameter.

“The Translator service can be used to translate documents from English to French.” – Yes.This statement is true. The Translator service supports both text translation and document translation. The document translation capability allows the translation of whole files such as Word, PowerPoint, or PDF documents while preserving formatting and structure. This feature is included in the official Translator API under “Document Translation.”

In summary, the AI-900 study content clarifies that:

✅ /translate?from=en & to=it,fr → Valid syntax

❌ /translate?from=en & to=fr & to=it → Invalid syntax

✅ Translator can translate full documents between languages

Microsoft’s Responsible AI Principles, as outlined in the AI-900 certification materials and official Microsoft documentation, emphasize six guiding principles: fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability. The principle of transparency means that AI systems should be designed so their decisions and processes are understandable and explainable to users and stakeholders.

In this scenario, the AI system is being developed to decide whether a loan should be approved. Such a decision directly affects people’s lives and finances, so it is essential that the system can explain which factors influenced its decision—for example, credit score, income, or payment history. Microsoft’s Responsible AI framework stresses that transparency helps ensure trust between humans and AI systems. When decisions are explainable, users can understand and contest the reasoning if necessary.

The other options do not align precisely with this scenario:

B. Inclusiveness focuses on making AI accessible to all people, regardless of ability or background.

C. Fairness ensures that AI systems treat all individuals equally and do not discriminate. While fairness is important for loan assessment, the question specifically highlights the need for explainability, not equality.

D. Privacy and Security deals with safeguarding user data, which is separate from explaining decisions.

Therefore, the principle demonstrated here is transparency, as it ensures decision-making processes are clear, explainable, and traceable—directly aligning with Microsoft’s responsible AI guidance.

According to the Microsoft Azure AI Fundamentals (AI-900) learning path and Azure Text Analytics service documentation, key phrase extraction is a natural language processing (NLP) technique used to automatically identify the main topics or talking points within a text document or a collection of documents. This feature is designed to summarize textual data by detecting the most relevant words or short phrases that capture the essence of the content.

For example, if a document discusses “renewable energy sources such as solar and wind power,” the key phrases extracted might include “renewable energy,” “solar power,” and “wind power.” This helps users quickly understand the primary focus areas of large volumes of text without manual review.

In Azure, the Text Analytics service provides several core NLP capabilities, including:

Key phrase extraction – identifies main concepts or topics.

Entity recognition – detects and categorizes proper names like people, locations, or organizations.

Sentiment analysis – determines the emotional tone (positive, neutral, or negative).

Language detection – identifies the language used in the text.

Since the question specifies identifying main talking points, the correct feature is key phrase extraction, as it focuses on summarizing themes rather than identifying entities or emotions.

Therefore, the verified answer is B. key phrase extraction.

B: Azure ' s Computer Vision service provides developers with access to advanced algorithms that process images and return information based on the visual features you ' re interested in. For example, Computer Vision can determine whether an image contains adult content, find specific brands or objects, or find human faces.

C: Computer Vision includes Optical Character Recognition (OCR) capabilities. You can use the new Read API to extract printed and handwritten text from images and documents.

According to the Microsoft Learn module “Build a QnA Maker knowledge base”, QnA Maker allows developers to create bots that answer user queries based on documents like FAQs or manuals. To make a bot more natural and conversational, Microsoft provides a “chit-chat” feature — a prebuilt, professionally written set of responses to common conversational phrases such as greetings (“Hello”), small talk (“How are you?”), and polite phrases (“Thank you”).

Adding chit-chat improves user experience by making the bot sound friendlier and more human-like. It doesn’t alter the main Q & A logic but enhances the bot’s tone and responsiveness.

The other options are not correct:

A. Increase the confidence threshold makes the bot more selective in responses but doesn’t add new conversational features.

B. Enable active learning improves knowledge base accuracy over time through user feedback.

C. Create multi-turn questions adds conversational flow for related topics but doesn’t add greetings or casual dialogue.

Thus, to make the bot more personable, the correct action is to Add chit-chat.

Box1: Sentiment analysis

Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.

Box 2: Broad entity extraction

Broad entity extraction: Identify important concepts in text, including key

Key phrase extraction/ Broad entity extraction: Identify important concepts in text, including key phrases and named entities such as people, places, and organizations.

Box 3: Entity Recognition

Named Entity Recognition: Identify and categorize entities in your text as people, places, organizations, date/time, quantities, percentages, currencies, and more. Well-known entities are also recognized and linked to more information on the web.

In Microsoft Azure OpenAI Service and the AI-900/AI-102 study materials, grounding data is the correct term used to describe the process of providing contextual or external information to improve the accuracy, relevance, and quality of responses generated by a generative AI model such as GPT-3.5 or GPT-4.

Grounding is a prompt engineering technique where the AI model is supplemented with relevant background data, such as company documents, knowledge bases, or user context, that helps the model generate factually correct and context-aware responses. Microsoft Learn defines grounding as a way to connect the model’s general knowledge to specific, real-world information. For example, if you ask a GPT-3.5 model about your organization’s HR policies, the base model will not know them unless that policy information is provided (grounded) in the prompt. By embedding this contextual data, the AI becomes “grounded” in the facts it needs to respond reliably.

This technique differs from other prompt engineering concepts:

A. Providing examples (few-shot prompting) shows the model sample inputs and outputs to guide formatting or style, not factual context.

B. Fine-tuning involves retraining the model with labeled data to permanently adjust its behavior — it’s not a prompt-based technique.

D. System messages define the model’s role, tone, or style (for example, “You are a helpful assistant”) but do not add factual context.

Therefore, when you provide contextual information (like product details, policy documents, or reference text) within a prompt to enhance the quality and factual reliability of the model’s responses, you are applying the grounding data technique.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module “Identify Azure services for conversational AI,” the Azure AI Bot Service is specifically designed to create intelligent conversational agents (chatbots) that can interact with users across multiple communication channels, such as web chat, social media, phone calls, Microsoft Teams, and digital assistants.

In this scenario, customers need the ability to query the status of their orders through various interfaces — including voice and text platforms. Azure AI Bot Service enables this by integrating with Azure AI Language (for understanding natural language), Azure Speech (for speech-to-text and text-to-speech capabilities), and Azure Communication Services (for telephony or chat integration).

The bot can interpret user input like “Where is my order?” or “Check my delivery status,” call backend systems (such as an order database or API), and then respond appropriately to the user through the same communication channel.

Let’s analyze the incorrect options:

B. Azure AI Translator Service: Used for real-time text translation between languages; it doesn’t handle conversation logic or database queries.

C. Azure AI Document Intelligence model: Extracts data from structured and semi-structured documents (e.g., invoices, receipts), not user queries.

D. Azure Machine Learning model: Builds and deploys predictive models, but doesn’t provide conversational or multi-channel interaction capabilities.

Thus, for enabling multi-channel conversational experiences where customers can inquire about order statuses using voice, chat, or digital assistants, the most appropriate solution is Azure AI Bot Service, as outlined in Azure’s AI conversational workload documentation.