Microsoft AI-900 Actual Free Exam Questions & Community Discussion
Extracting relationships between data from large volumes of unstructured data is an example of which type of Al workload?
Correct Answer: C
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You need to track multiple versions of a model that was trained by using Azure Machine Learning. What should you do?
Correct Answer: A
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You have a dataset that contains experimental data for fuel samples.
You need to predict the amount of energy that can be obtained from a sample based on its density.
Which type of Al workload should you use?
You need to predict the amount of energy that can be obtained from a sample based on its density.
Which type of Al workload should you use?
Correct Answer: C
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Your company is exploring the use of voice recognition technologies in its smart home devices. The company wants to identify any barriers that might unintentionally leave out specific user groups.
This an example of which Microsoft guiding principle for responsible AI?
This an example of which Microsoft guiding principle for responsible AI?
Correct Answer: D
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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 "Explore fundamental principles of machine learning," a regression model is used when the goal is to predict a continuous numerical value based on historical data.
In this question, the task is to predict the sale price of auctioned items, which is a numeric output that can take on a wide range of values (for example, $50.25, $199.99, etc.). This makes it a regression problem because the output is continuous rather than categorical.
Regression models analyze the relationship between input features (such as item type, condition, age, bidding history, or demand) and a numerical target variable (the sale price). Common regression algorithms include linear regression, decision tree regression, and neural network regression. In Azure Machine Learning, these models are trained using labeled datasets containing known outcomes to learn patterns and make future predictions.
Let's review the incorrect options:
* Classification: Used to predict discrete categories or labels, such as "sold" vs. "unsold" or "low,"
"medium," "high." It cannot output continuous numeric predictions.
* Clustering: An unsupervised technique used to group similar data points based on shared characteristics, not to predict specific numeric outcomes.
Therefore, because predicting a sale price involves forecasting a continuous numerical value, the correct model type is Regression.
This aligns with Microsoft's AI-900 teaching that regression is used for tasks such as:
* Predicting house prices
* Forecasting sales revenue
* Estimating car values or auction prices
Match the types of machine learning to the appropriate scenarios.
To answer, drag the appropriate machine learning type from the column on the left to its scenario on the right.
Each machine learning type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
To answer, drag the appropriate machine learning type from the column on the left to its scenario on the right.
Each machine learning type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Describe features of common AI workloads", there are three primary supervised and unsupervised machine learning types: Regression, Classification, and Clustering. Each type of learning addresses a different kind of problem depending on the data and desired prediction output.
* Regression - Regression models are used to predict numeric, continuous values. The study guide specifies that "regression predicts a number." In the scenario "Predict how many minutes late a flight will arrive based on the amount of snowfall," the output (minutes late) is a continuous numeric value.
Therefore, this is a regression problem. Regression algorithms like linear regression or decision tree regression estimate relationships between variables and predict measurable quantities.
* Clustering - Clustering falls under unsupervised learning, where the model identifies natural groupings or patterns in unlabeled data. The official AI-900 training material states that "clustering is used to find groups or segments of data that share similar characteristics." The scenario "Segment customers into different groups to support a marketing department" fits this description because the goal is to group customers based on behavior or demographics without predefined labels. Thus, it is a clustering problem.
* Classification - Classification is a supervised learning method used to predict discrete categories or labels. The AI-900 content defines classification as "predicting which category an item belongs to." The scenario "Predict whether a student will complete a university course" requires a yes/no (binary) outcome, which is a classic classification problem. Examples include logistic regression, decision trees, or neural networks trained for categorical prediction.
In summary:
* Regression # Predicts continuous numeric outcomes.
* Clustering # Groups data by similarities without predefined labels.
* Classification # Predicts discrete or categorical outcomes.
Hence, the correct and verified mappings based on the official AI-900 study material are:
* Regression # Flight delay prediction
* Clustering # Customer segmentation
* Classification # Course completion prediction
Match the Azure Al service to the appropriate generative Al capability.
To answer, drag the appropriate service from the column on the left to its capability on the right. Each service may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.
To answer, drag the appropriate service from the column on the left to its capability on the right. Each service may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.
Correct Answer:
Explanation:
This question maps each Azure AI service to its correct capability based on the Microsoft Azure AI Fundamentals (AI-900) syllabus and Microsoft Learn documentation on Azure Cognitive Services.
* Classify and label images # Azure AI VisionAzure AI Vision (formerly Computer Vision) provides capabilities to analyze visual content, detect objects, classify images, and extract information from pictures. It includes object detection, image classification, and tagging, which are core vision tasks.
This service enables businesses to build solutions that understand visual input, such as identifying products, reading signs, or detecting faces in images.
* Generate conversational responses # Azure OpenAI ServiceAzure OpenAI Service integrates powerful large language models such as GPT-3.5 and GPT-4, capable of generating human-like text responses, summarizations, translations, and dialogues. These models are designed for natural language generation (NLG) and conversational AI, making them ideal for chatbots, virtual agents, and intelligent assistants that produce dynamic, context-aware replies.
* Convert speech to text in real time # Azure AI SpeechAzure AI Speech provides speech-to-text capabilities (speech recognition) that convert spoken language into written text instantly. It is commonly used in transcription services, voice command systems, and live captioning applications.
Additionally, the Speech service supports text-to-speech (speech synthesis) and speech translation, making it versatile for voice-based AI applications.
By understanding each service's specialization-Vision for visual data, OpenAI for generative text, and Speech for audio processing-you can correctly match the capabilities.
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:
These answers align with the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Explore conversational AI in Microsoft Azure."
1. A webchat bot can interact with users visiting a website # Yes
This statement is true. The Azure Bot Service allows developers to create intelligent chatbots that can be integrated into a webchat interface. This enables visitors to interact with the bot directly from a website, asking questions and receiving automated responses. This is a typical use case of conversational AI, where natural language processing (NLP) is used to interpret and respond to user input conversationally.
2. Automatically generating captions for pre-recorded videos is an example of conversational AI # No This statement is false. Automatically generating captions from video content is an example of speech-to-text (speech recognition) technology, not conversational AI. While it uses AI to convert spoken words into text, it lacks the two-way interactive communication characteristic of conversational AI. This task is typically handled by the Azure AI Speech service, which transcribes spoken content.
3. A smart device in the home that responds to questions such as "What will the weather be like today?" is an example of conversational AI # Yes This statement is true. Smart home assistants that engage in dialogue with users are powered by conversational AI. These devices use speech recognition to understand spoken input, natural language understanding (NLU) to determine intent, and speech synthesis (text-to-speech) to respond audibly. This represents the full conversational AI loop, where machines communicate naturally with humans.
You have an Azure Machine Learning pipeline that contains a Split Data module. The Split Data module outputs to a Train Model module and a Score Model module. What is the function of the Split Data module?
Correct Answer: C
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You need to convert handwritten notes into digital text.
Which type of computer vision should you use?
Which type of computer vision should you use?
Correct Answer: C
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