Microsoft Azure AI Fundamentals - AI-900 Exam Practice Test
Match the Azure OpenAI large language model (LLM) process to the appropriate task.
To answer, drag the appropriate process from the column on the left to its task on the right. Each process may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.
To answer, drag the appropriate process from the column on the left to its task on the right. Each process may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.
Correct Answer:
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) study material and Azure OpenAI Service documentation, large language models (LLMs) such as GPT are capable of performing multiple natural language processing (NLP) tasks depending on the intent of the prompt. These tasks generally fall into categories like classification, generation, summarization, and translation, each with a distinct purpose and output type.
* Classifying - This process involves analyzing text and assigning it to a predefined category or label based on its content. The scenario "Detect the genre of a work of fiction" clearly fits this category. The model must evaluate the text and determine whether it belongs to genres like mystery, romance, or science fiction. This is a classic text classification problem, as the output is a discrete category derived from textual features.
* Summarizing - This process means condensing lengthy text into a shorter version that preserves the key information. In the scenario "Create a list of bullet points based on text input," the model extracts essential information and reformats it as concise bullet points, which is an abstraction form of summarization. Summarization models help users quickly understand the main ideas from long documents, meeting efficiency and readability goals.
* Generating - This refers to the LLM's ability to produce new, creative content based on input instructions. The task "Create advertising slogans from a product description" represents generation because it requires the model to construct original text that didn't previously exist. Generation tasks showcase the creativity and contextual fluency of models like GPT in marketing and content creation.
Thus, these mappings align directly with the Azure OpenAI LLM capabilities taught in AI-900, linking each NLP process with its most suitable real-world task.
For each of The following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
The Azure AI Language service (part of Azure Cognitive Services) provides a set of natural language processing (NLP) capabilities designed to analyze and interpret text data. Its core features include language detection, key phrase extraction, sentiment analysis, and named entity recognition (NER).
* Language Identification - YESAccording to the Microsoft Learn module "Analyze text with Azure AI Language," one of the service's built-in capabilities is language detection, which determines the language of a given text string (e.g., English, Spanish, or French). This allows applications to automatically adapt to multilingual input.
* Handwritten Signature Detection - NOThe Azure AI Language service only processes text-based data; it does not analyze images or handwriting. Detecting handwritten signatures requires computer vision capabilities, specifically Azure AI Vision or Azure AI Document Intelligence, which can extract and interpret visual content from scanned documents or images.
* Identifying Companies and Organizations - YESThe Named Entity Recognition (NER) feature within Azure AI Language can identify entities such as people, locations, dates, organizations, and companies mentioned in text. It tags these entities with categories, enabling structured analysis of unstructured data.
# Summary:
* Language detection # Yes (supported by AI Language).
* Handwritten signatures # No (requires Computer Vision).
* Entity recognition for companies/organizations # Yes (supported by AI Language NER).
You have a frequently asked questions (FAQ) PDF file.
You need to create a conversational support system based on the FAQ.
Which service should you use?
You need to create a conversational support system based on the FAQ.
Which service should you use?
Correct Answer: C
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You are building a knowledge base by using QnA Maker. Which file format can you use to populate the knowledge base?
Correct Answer: A
Explanation: Only visible for TrainingDump members. You can sign-up / login (it's free).
What is an example of a Microsoft responsible Al principle?
Correct Answer: C
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Select the answer that correctly completes the sentence.
Correct Answer:
Explanation:
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 classification technique is a type of supervised machine learning used to predict which category or class a new observation belongs to, based on patterns learned from labeled training data.
In this scenario, a banking system that predicts whether a loan will be repaid is dealing with a binary outcome-either the loan will be repaid or will not be repaid. These two possible results represent distinct classes, making this problem a classic example of binary classification. During training, the model learns from historical data containing features such as customer income, credit score, loan amount, and repayment history, along with labeled outcomes (repaid or defaulted). After training, it can classify new applications into one of these two categories.
The AI-900 curriculum distinguishes between three key supervised and unsupervised learning approaches:
* Classification: Predicts discrete categories (e.g., spam/not spam, fraud/not fraud, will repay/won't repay).
* Regression: Predicts continuous numerical values (e.g., house prices, sales forecast, temperature).
* Clustering: Groups data based on similarity without predefined labels (e.g., customer segmentation).
Since the banking problem focuses on predicting a categorical outcome rather than a continuous numeric value, it fits squarely into the classification domain. In Azure Machine Learning, such tasks can be performed using algorithms like Logistic Regression, Decision Trees, or Support Vector Machines (SVMs), all configured for categorical prediction.
Therefore, per Microsoft's official AI-900 learning objectives, a banking system predicting whether a loan will be repaid represents a classification type of machine learning problem.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
This question is derived from the Microsoft Azure AI Fundamentals (AI-900) learning module, particularly under "Describe features of conversational AI workloads on Azure." It tests understanding of chatbot capabilities and design principles within the context of Azure Bot Service and Conversational AI.
* Chatbots can support voice input - YesAccording to the AI-900 official materials, conversational AI systems such as chatbots can interact with users through text or voice. Using speech recognition services like Azure Cognitive Services Speech-to-Text, bots can interpret spoken input, and with Text- to-Speech, they can respond verbally. This enables voice-based chatbots used in virtual assistants, call centers, and customer support. Hence, voice input is fully supported by conversational AI solutions in Azure.
* A separate chatbot is required for each communication channel - NoThe Azure Bot Service is designed to provide multi-channel communication from a single bot instance. A single chatbot can communicate across several channels such as Microsoft Teams, Web Chat, Slack, Facebook Messenger, and email without needing separate bots for each platform. This centralized design allows developers to create, deploy, and manage one bot while configuring multiple channel connections through the Azure portal.
Therefore, the statement is false.
* Chatbots manage conversation flows by using a combination of natural language and constrained option responses - YesIn Microsoft's AI-900 training, chatbots are described as using Natural Language Processing (NLP) to understand free-form user input while also guiding interactions with predefined options such as buttons or quick replies. This hybrid approach ensures both flexibility and control, improving user experience and accuracy. Bots can interpret natural language via services like Language Understanding (LUIS) and also present structured options to guide conversations efficiently.
Which two scenarios are examples of a natural language processing workload? Each correct answer presents a complete solution.
NOTE; Each correct selection is worth one point.
NOTE; Each correct selection is worth one point.
Correct Answer: B,C
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You have the following apps:
* App1: Uses a set of images of tumors to identify whether the tumors are benign or malignant and suggest a treatment
* App2: Uses images from cameras to track individual livestock as they move around a farm
* App3: Identifies brands in photographs of billboards
What does each app use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
* App1: Uses a set of images of tumors to identify whether the tumors are benign or malignant and suggest a treatment
* App2: Uses images from cameras to track individual livestock as they move around a farm
* App3: Identifies brands in photographs of billboards
What does each app use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Let's analyze each application in the context of Microsoft Azure AI Fundamentals (AI-900) and computer vision model types.
* App1 - Uses a set of images of tumors to identify whether the tumors are benign or malignant and suggest a treatment # Image classificationThis application is performing image classification, where each image (of a tumor) is assigned to a single predefined category - benign or malignant. Image classification models learn patterns from labeled training images and predict the correct class for new ones. In this case, the model identifies the type of tumor, a classic binary classification scenario.
* App2 - Uses images from cameras to track individual livestock as they move around a farm # Object detectionThis scenario describes object detection, which not only identifies what objects (in this case, animals) are in an image but also locates them by drawing bounding boxes. Tracking movement requires detecting the position of each animal frame by frame. Object detection models are well-suited for use cases involving counting, tracking, or monitoring objects in a visual scene.
* App3 - Identifies brands in photographs of billboards # Optical character recognition (OCR)This app involves reading and interpreting text (brand names, slogans, or logos) from images of billboards.
Optical Character Recognition (OCR), part of Azure AI Vision, extracts textual information from images or scanned documents. Once extracted, that text can be analyzed to identify brand names or keywords.
Summary:
* App1 # Image classification
* App2 # Object detection
* App3 # Optical character recognition (OCR)
What are three stages in a transformer model? Each correct answer presents a complete solution.
NOTE: Each correct answer is worth one point.
NOTE: Each correct answer is worth one point.
Correct Answer: A,B,E
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You build a machine learning model by using the automated machine learning user interface (UI).
You need to ensure that the model meets the Microsoft transparency principle for responsible AI.
What should you do?
You need to ensure that the model meets the Microsoft transparency principle for responsible AI.
What should you do?
Correct Answer: A
Explanation: Only visible for TrainingDump members. You can sign-up / login (it's free).
Select the answer that correctly completes the sentence.
Correct Answer:
Explanation:
"When evaluating the performance of a model, the confusion matrix displays the predicted and actual positives and negatives by using a grid of 0 and 1 values." According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and Microsoft Learn module "Identify features of common machine learning types", a confusion matrix is a tool used to evaluate the performance of classification models. It visually summarizes how many predictions were correctly or incorrectly classified by comparing the predicted labels to the actual (true) labels.
A confusion matrix is a table, typically 2×2 for binary classification, with the following components:
* True Positives (TP): The model correctly predicted the positive class.
* True Negatives (TN): The model correctly predicted the negative class.
* False Positives (FP): The model incorrectly predicted the positive class.
* False Negatives (FN): The model incorrectly predicted the negative class.
The confusion matrix allows data scientists and analysts to derive important performance metrics such as accuracy, precision, recall, and F1-score, which together provide a more complete understanding of how well a model performs beyond a single number.
In Microsoft Learn's AI-900 curriculum, the confusion matrix is highlighted as a key visualization tool that
"compares actual values to predicted values to evaluate classification performance." The grid format (using 0s and 1s for predicted classes) helps identify where misclassifications occur.
By contrast:
* AUC metric (Area Under Curve) and ROC curve evaluate model discrimination ability.
* Threshold defines decision cutoffs but doesn't display classifications.
Therefore, based on the official Microsoft AI-900 study guide and Microsoft Learn resources, the correct answer is Confusion Matrix, as it provides a grid view comparing actual versus predicted values in classification models.
Select the answer that correctly completes the sentence.
Correct Answer:
Explanation:
The correct answer is Azure AI Language, which includes the Question Answering capability (previously known as QnA Maker). According to the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn documentation, the Azure AI Language service can be used to create a knowledge base from frequently asked questions (FAQ) and other structured or semi-structured text sources.
This service allows developers to build intelligent applications that can understand and respond to user questions in natural language by referencing prebuilt or custom knowledge bases. The Question Answering feature extracts pairs of questions and answers from documents, websites, or manually entered data and uses them to construct a searchable knowledge base. This knowledge base can then be integrated with Azure Bot Service or other conversational platforms to create interactive, self-service chatbots.
Here's how it works:
* Developers upload FAQ documents, URLs, or structured content.
* Azure AI Language processes the content and identifies logical question-answer pairs.
* The model stores these pairs in a knowledge base that can be queried by user input.
* When users ask questions, the model finds the best matching answer using natural language understanding techniques.
In contrast:
* Azure AI Document Intelligence (Form Recognizer) is used to extract structured data from forms and documents, not to create FAQ knowledge bases.
* Azure AI Bot Service is for managing and deploying conversational bots but does not generate knowledge bases.
* Microsoft Bot Framework SDK provides tools for building conversational logic but still requires a knowledge source like Question Answering from Azure AI Language.
Therefore, the service that can create a knowledge base from FAQ content is Azure AI Language.