According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module “Identify features of common AI workloads”, OCR (Optical Character Recognition) is a Computer Vision technology that detects and extracts printed or handwritten text from images and scanned documents. OCR allows organizations and individuals to convert physical or image-based text into machine-readable, editable, and searchable digital text.

In the context of this question, a historian working with old newspaper articles or archival documents would use OCR to digitize printed content. For instance, the historian can scan or photograph old newspaper pages, and then use an OCR tool—such as Azure Computer Vision’s OCR API—to automatically recognize and extract the textual content from those images. This process enables the historian to store, edit, and analyze the content digitally without manually typing everything.

OCR works by using deep learning algorithms trained on thousands of text samples. The system analyzes patterns, shapes, and spatial relationships of characters to identify text accurately, even from low-quality or aged paper documents. Once extracted, the digital text can be indexed, translated, or processed further using Natural Language Processing (NLP) tools for content analysis.

Now, addressing the other options:

Facial analysis is used to detect emotions, age, or gender from human faces—irrelevant to text digitization.

Image classification identifies entire images by categories (e.g., cat, car, flower).

Object detection identifies and locates multiple objects within an image but doesn’t extract text.

Therefore, per the AI-900 learning objectives under the Computer Vision workload, the correct and verified completion is:

To improve the quality and relevance of responses generated by a generative AI solution using GPT-3.5, the following three actions are emphasized in the Microsoft Learn: Azure OpenAI Service best practices and AI-900/AI-102 training materials:

(A) Add grounding data to prompts: Grounding ensures that the model’s output is based on factual, domain-specific information. By adding context or external knowledge sources, responses become more accurate and aligned with the organization’s data rather than relying on the model’s general training corpus.

(B) Provide additional examples to prompts: Also known as few-shot prompting, this method demonstrates desired response patterns by including examples in the prompt. This significantly improves output quality, consistency, and adherence to desired formats.

(E) Modify system messages: In Azure OpenAI chat completions, system messages define the model’s behavior, style, and tone. Adjusting system messages allows fine-tuning of the model’s response quality, ensuring it follows context or persona guidelines.

The remaining options are incorrect:

(C) Modify tokenization is a low-level text-processing technique not used to improve model response quality directly.

(D) Add training data to prompts is not possible at runtime since GPT-3.5 models are pre-trained; only prompt engineering can influence output behavior.

Therefore, based on Azure OpenAI and AI-900 guidance, the three best ways to enhance generative

QnA Maker is an Azure Cognitive Service used to build question-and-answer knowledge bases from structured and unstructured documents, such as FAQs, product manuals, or webpages. According to the AI-900 study guide and Microsoft Learn module “Build a knowledge base with QnA Maker”, this service allows you to extract question-answer pairs from existing data sources like FAQ pages, PDF files, or Word documents.

In this scenario, you have:

A product troubleshooting guide (Word document)

A FAQ webpage

QnA Maker can automatically read both sources, extract relevant Q & A pairs, and create a knowledge base that your chatbot can use to respond to user queries intelligently.

To clarify the other options:

A. Azure Bot Service provides the chatbot interface and conversation logic but doesn’t extract knowledge from documents.

B. Language Understanding (LUIS) identifies intents and entities in natural language input, but it’s not used to read document content.

C. Text Analytics is used for key phrase extraction and sentiment analysis, not Q & A creation.

Therefore, the correct service for processing FAQ-style and document-based content into a question-answering bot is QnA Maker.

The correct matching aligns directly with the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn modules under “Identify features of Azure Machine Learning”. Azure Machine Learning provides a suite of tools that serve different functions within the model development lifecycle — from creating workspaces, to training models, to automating experimentation.

The Azure portal → Create a Machine Learning workspace.The Azure portal is a web-based graphical interface for managing all Azure resources. According to Microsoft Learn, you use the portal to create and configure the Azure Machine Learning workspace, which acts as the central environment where datasets, experiments, models, and compute resources are organized. Creating a workspace through the portal involves specifying a subscription, resource group, and region — tasks that are part of the setup stage rather than model development.

Machine Learning designer → Use a drag-and-drop interface used to train and deploy models.The Machine Learning designer (formerly “Azure ML Studio (classic)”) provides a visual, no-code/low-code interface for building, training, and deploying machine learning pipelines. The designer uses a drag-and-drop workflow where users connect modules representing data transformations, model training, and evaluation. This tool is ideal for beginners and those who want to quickly experiment with machine learning concepts without writing code.

Automated machine learning (Automated ML) → Use a wizard to select configurations for a machine learning run.Automated ML simplifies model creation by automatically selecting algorithms, hyperparameters, and data preprocessing options. Users interact through a guided wizard (within the Azure Machine Learning studio) that walks them through configuration steps such as selecting datasets, target columns, and performance metrics. The system then iteratively trains and evaluates multiple models to recommend the best-performing one.

Together, these tools streamline the machine learning workflow:

Azure portal for setup and resource management,

Machine Learning designer for visual model creation, and

Automated ML for guided, automated model selection and tuning.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module “Identify features of common machine learning types”, the three main machine learning model types differ by their purpose and the kind of data they use — whether supervised (using labeled data) or unsupervised (using unlabeled data).

Regression → A supervised machine learning model used to predict numeric values.Regression is a type of supervised learning that predicts continuous numerical outcomes. It learns the relationship between input features (independent variables) and a continuous target variable (dependent variable). Examples include predicting house prices, sales revenue, or temperature. The AI-900 curriculum emphasizes regression for “predicting numeric values based on known data,” using algorithms such as linear regression or decision tree regression.

Classification → A supervised machine learning model used to predict categories.Classification is also a supervised learning technique, but it predicts discrete outcomes (classes) instead of continuous values. It assigns input data to one or more categories based on learned patterns. Typical examples include spam detection (spam vs. not spam), sentiment analysis (positive, neutral, negative), or predicting loan approval (approved/denied). The AI-900 study materials describe classification as “predicting a category or label for new observations.”

Clustering → An unsupervised machine learning model used to group similar entities based on features.Clustering is an unsupervised learning approach, meaning it works on unlabeled data. It automatically identifies patterns and groups similar data points into clusters based on shared characteristics. Examples include customer segmentation (grouping customers by behavior) and grouping similar documents. The AI-900 learning module explains clustering as “discovering natural groupings in data without predefined labels.”

Thus, per Microsoft’s official AI-900 learning objectives:

Regression → Predicts numeric/continuous values.

Classification → Predicts categories/labels.

Clustering → Groups similar entities (unsupervised).

✅ Final Verified Configuration:

Regression → Predict numeric values

Classification → Predict categories

Clustering → Group similar entities based on features

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.

The correct mappings are based on the Microsoft Azure AI Fundamentals (AI-900) curriculum topic: “Describe features of computer vision workloads.” Microsoft divides computer vision tasks into key workload types — image classification, object detection, facial recognition, and optical character recognition (OCR) — each designed for specific visual analysis objectives.

Identify celebrities in images → Facial recognitionFacial recognition goes beyond simple face detection; it can identify or verify specific individuals by comparing facial features with known profiles. According to Microsoft Learn, the Face service in Azure Cognitive Services can detect, recognize, and identify people in photos or videos. Recognizing celebrities or known individuals is a prime example of facial recognition.

Extract movie title names from movie poster images → Optical Character Recognition (OCR)OCR is used to detect and extract text content from images, such as printed or handwritten words. Azure’s Computer Vision API uses OCR technology to read text in various languages from photos, scanned documents, or posters. Therefore, extracting movie titles or actor names from a poster image is a perfect use case for OCR.

Locate vehicles in images → Object detectionObject detection identifies and locates specific objects within an image, returning bounding boxes that indicate their positions. In Azure, the Custom Vision service or Computer Vision object detection models are used to detect multiple objects like vehicles, pedestrians, or animals in a single image.

Summary:

Facial recognition → Identifies specific people (celebrities)

OCR → Extracts text (movie titles)

Object detection → Finds and locates physical items (vehicles)

Thus, the verified and official answer is:

Features

The correct completion of the sentence is:

“In a machine learning model, the data that is used as inputs are called features.”

According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and Microsoft Learn module “Identify features of common machine learning types,” the term feature refers to an individual measurable property or characteristic of the data that is used by a machine learning model to make predictions or decisions.

In supervised and unsupervised learning, features serve as the inputs to the model. They are the variables that represent the information the algorithm learns from. For example, if a model predicts the price of a house, the features might include square footage, number of bedrooms, location, and age of the property. These features feed into the model so that it can learn the relationship between inputs and outputs.

Microsoft Learn further defines these key concepts:

Features: Input variables (independent variables) used by the model to learn patterns.

Labels: The desired output or target variable that the model is trained to predict (e.g., price, category).

Instances: Individual rows or data records within the dataset (each instance has multiple features).

Functions: Algorithms or mathematical operations used during training and prediction — not data inputs.

Therefore, among the provided options — features, functions, labels, instances — only features accurately describe the data elements used as inputs for training or inference in a machine learning model.

In summary, within the AI-900 learning context:

Features = inputs to the model.

Labels = outputs for supervised learning.

Instances = examples (rows) of data.

According to the Microsoft Azure OpenAI Service documentation and AI-900 official study materials, the DALL-E model is specifically designed to generate and edit images from natural language prompts. When a user provides a descriptive text input such as “a futuristic city skyline at sunset”, DALL-E interprets the textual prompt and produces an image that visually represents the content described. This functionality is known as text-to-image generation and is one of the creative AI capabilities supported by Azure OpenAI.

DALL-E belongs to the family of generative models that can create new visual content, expand existing images, or apply transformations to images based on textual instructions. Within Azure OpenAI, the DALL-E API enables developers to integrate image creation directly into applications—useful for design assistance, marketing content generation, or visualization tools. The model learns from vast datasets of text–image pairs and is optimized to ensure alignment, diversity, and accuracy in the produced visuals.

By contrast, the other options serve different purposes:

A. GPT-4 is a large language model for text-based generation, reasoning, and conversation, not for creating images.

C. GPT-3 is an earlier text generation model, primarily used for language tasks like summarization, classification, and question answering.

D. Whisper is an automatic speech recognition (ASR) model used to convert spoken language into written text; it has no image-generation capability.

Therefore, when the requirement is to generate images based on user prompts, the only Azure OpenAI model that fulfills this purpose is DALL-E. This aligns directly with the AI-900 learning objective covering Azure OpenAI generative capabilities for text, code, and image creation.

According to the Microsoft Azure AI Fundamentals (AI-900) learning content, Language Understanding Intelligent Service (LUIS) is part of Azure Cognitive Services used to interpret the meaning or intent behind a user’s input in natural language. When a user says, “Call me back later,” the system must recognize that the user intends for a call to be scheduled or delayed—this is not just about translating or analyzing text but understanding intent and relevant entities.

LUIS allows developers to train models to identify intents (such as ScheduleCall, CancelMeeting, etc.) and extract key entities (like names, times, or actions) from text inputs. It is typically integrated with conversational agents such as Azure Bot Service, enabling more natural, human-like interactions.

Other options do not fit the scenario:

Translator Text (A) translates text between languages but does not interpret meaning.

Text Analytics (B) performs sentiment analysis, key phrase extraction, and named entity recognition, but it doesn’t identify intent.

Speech (C) converts spoken language to text or text to speech but doesn’t interpret what the words mean.

Therefore, for understanding user intent such as “Call me back later,” the correct AI service is D. Language Understanding (LUIS).