Below are 30 interview questions and answers focused on Microsoft AI Builder. They cover topics like models, integration with Power Automate, training models, custom models, and practical scenarios. These are designed to show skill in AI Builder and its application within the Microsoft Power Platform.

General AI Builder Questions

  1. What is Microsoft AI Builder, and how does it fit into the Power Platform?
    Answer: Microsoft AI Builder is a low-code AI capability within the Power Platform that allows users to create and customize AI models to optimize business processes. It integrates seamlessly with Power Apps and Power Automate, enabling users to automate tasks and gain insights from data without requiring extensive coding or data science expertise. It leverages Azure AI technologies to bring intelligence to apps and workflows.
  2. What are the two main types of AI models in AI Builder?
    Answer: AI Builder offers two main types of models: Prebuilt models, which are ready-to-use for common business scenarios (e.g., receipt processing, text recognition), and Custom models, which users can build, train, and tailor to specific business needs (e.g., custom object detection, prediction models).
  3. What are some common use cases for AI Builder?
    Answer: Common use cases include automating document processing (e.g., extracting data from invoices), detecting objects in images (e.g., identifying products on shelves), predicting outcomes (e.g., customer churn), and classifying text (e.g., sentiment analysis of feedback). These help streamline workflows and improve decision-making.
  4. What is the difference between prebuilt and custom models in AI Builder?
    Answer: Prebuilt models are pre-trained by Microsoft and ready for immediate use in common scenarios, requiring no training (e.g., business card reader). Custom models require users to provide their own data, train the model, and publish it for use, making them suitable for unique business requirements (e.g., custom form processing).
  5. How does AI Builder leverage Azure technologies?
    Answer: AI Builder is built on Azure AI capabilities, such as Azure Cognitive Services for text and image processing, and Azure Machine Learning for training custom models. This integration provides robust AI functionality within a low-code environment.

Model Types and Training

  1. What are the steps to create a custom AI model in AI Builder?
    Answer: The steps are:
    1. Sign into Power Apps or Power Automate and navigate to the AI Builder section.
    2. Choose a model type (e.g., object detection, prediction).
    3. Connect to your data source (e.g., Dataverse, SharePoint).
    4. Configure the model by selecting relevant fields or labeling data.
    5. Train the model using the provided data.
    6. Evaluate the model’s performance and publish it for use.
  2. How do you train a custom object detection model in AI Builder?
    Answer: To train a custom object detection model:
    1. Collect and upload a dataset of images containing the objects to detect.
    2. Label the objects in the images by drawing bounding boxes and assigning class names.
    3. Train the model using the labeled dataset.
    4. Test the model with new images to ensure accuracy, then publish it for use in Power Apps or Power Automate.
  3. What is the minimum data requirement for training a prediction model in AI Builder?
    Answer: For a custom prediction model, you need at least 50 records of historical data with relevant fields (e.g., input features and outcomes) to train effectively. However, more data (e.g., 1,000+ records) improves accuracy.
  4. How does AI Builder handle form processing model training?
    Answer: For a form processing model:
    1. Upload at least five sample documents (e.g., PDFs or images) with consistent layouts.
    2. Tag key fields (e.g., name, invoice number) to teach the model what to extract.
    3. Train the model, which automatically learns the layout and field mappings.
    4. Test and publish the model for use in extracting data from similar forms.
  5. What happens during the training process of an AI Builder model?
    Answer: During training, AI Builder uses the provided data to teach the model patterns and relationships (e.g., recognizing objects or predicting outcomes). This is an automated process powered by Azure Machine Learning, and once complete, the model generates insights like predictions or extracted data.

Integration with Power Automate

  1. How do you use an AI Builder model in a Power Automate flow?
    Answer: To use an AI Builder model in Power Automate:
    1. Create a new flow or edit an existing one.
    2. Add an AI Builder action (e.g., “Predict” or “Extract text and information from an image”).
    3. Select the published AI Builder model.
    4. Map the input (e.g., file content or text) and configure outputs to use in subsequent steps (e.g., saving to Dataverse).
  2. What is the ‘Predict’ action in Power Automate, and how does it work with AI Builder?
    Answer: The “Predict” action allows you to use a published prediction model in a flow. You provide input data (e.g., customer details), and the model returns a prediction (e.g., likelihood of purchase). The output can then trigger actions like sending alerts or updating records.
  3. Can you schedule a flow to use an AI Builder model periodically?
    Answer: Yes, you can use a scheduled trigger in Power Automate (e.g., daily or weekly) to run a flow that uses an AI Builder model, such as processing new documents or generating predictions based on updated data.
  4. How would you automate invoice processing using AI Builder and Power Automate?
    Answer: Create a form processing model to extract data (e.g., invoice number, amount) from invoices. In Power Automate:
    1. Trigger the flow when a new invoice is uploaded (e.g., to SharePoint).
    2. Use the AI Builder “Process and extract information from a document” action with the model.
    3. Save the extracted data to a database or send it for approval.
  5. What is the benefit of using the ‘Asynchronous Pattern’ setting in AI Builder actions in Power Automate?
    Answer: The “Asynchronous Pattern” (enabled by default) ensures that the flow waits for the AI model to complete processing and return results properly, improving reliability for complex tasks like document extraction or predictions.

Custom Models and Practical Scenarios

  1. How would you use AI Builder to detect defective products in a manufacturing scenario?
    Answer: Build a custom object detection model:
    1. Collect images of defective and non-defective products.
    2. Label defects in the images.
    3. Train and publish the model.
    4. Integrate it into a Power Automate flow triggered by a camera feed, alerting staff when defects are detected.
  2. How can AI Builder help with customer sentiment analysis?
    Answer: Use a prebuilt sentiment analysis model or train a custom text classification model with customer feedback data (e.g., positive/negative labels). In Power Automate, process incoming feedback (e.g., emails) and route negative responses to a support team.
  3. What steps would you take to build a churn prediction model in AI Builder?
    Answer:
    1. Gather historical customer data (e.g., purchase history, support tickets, churn status).
    2. Create a prediction model in AI Builder, selecting relevant fields as predictors.
    3. Train the model and evaluate its accuracy.
    4. Use it in a flow to score new customers and trigger retention actions for high-risk cases.
  4. How do you test a custom model before using it in a production environment?
    Answer: After training, use the “Quick Test” feature in AI Builder to input sample data and review the output (e.g., predictions or extracted fields). Adjust the model if needed, then test it in a Power Automate flow with real-world data before publishing.
  5. How can AI Builder assist in automating expense reporting?
    Answer: Use a prebuilt receipt processing model or a custom form processing model to extract data (e.g., date, amount) from receipts. In Power Automate, trigger the flow on receipt upload, extract data, and store it in a database or submit it for approval.

Advanced Topics

  1. What data sources can AI Builder connect to for training custom models?
    Answer: AI Builder can connect to data in Microsoft Dataverse, SharePoint, OneDrive, Excel, or other sources via Power Platform connectors, depending on the model type.
  2. How do you improve the accuracy of a custom AI Builder model?
    Answer: Provide more diverse and representative training data, refine labels or tags, adjust model parameters (if applicable), and retrain the model. Testing and iterating based on results also help.
  3. What is the role of Dataverse in AI Builder?
    Answer: Dataverse serves as a centralized data storage platform for training and managing AI Builder models. It stores input data, model outputs, and enables integration with Power Apps and Power Automate.
  4. Can you retrain an AI Builder model after it’s published?
    Answer: Yes, you can retrain a published model by adding new data or refining existing data in AI Builder, then retraining and republishing it to improve performance or adapt to new scenarios.
  5. How does AI Builder handle large datasets for training?
    Answer: AI Builder processes large datasets efficiently using Azure’s cloud infrastructure. However, for optimal performance, it’s recommended to preprocess data (e.g., remove duplicates, normalize values) before training.

Scenario-Based Questions

  1. A company wants to automate email categorization. How would you implement this with AI Builder?
    Answer: Train a custom text classification model with labeled emails (e.g., “urgent,” “inquiry”). In Power Automate, trigger the flow on email receipt, use the model to classify the email, and route it to the appropriate team or folder.
  2. How would you use AI Builder to identify products in a retail store’s images?
    Answer: Build a custom object detection model with labeled images of products. Integrate it into a Power Apps app or Power Automate flow to analyze photos from store cameras, identifying product placement or stock levels.
  3. A business needs to predict supplier delays. How would you approach this with AI Builder?
    Answer: Collect historical supplier data (e.g., delivery times, order sizes). Create a prediction model in AI Builder, train it, and use it in a Power Automate flow to score new orders and notify managers of potential delays.
  4. How can AI Builder streamline HR onboarding with document processing?
    Answer: Use a form processing model to extract data from employee forms (e.g., ID, address). In Power Automate, trigger the flow on form submission, process the document, and populate Dataverse or an HR system with the extracted data.
  5. What would you do if an AI Builder model fails to produce accurate results in a live flow?
    Answer: Investigate by reviewing the training data quality, testing with new samples, and checking for errors in the flow configuration. Retrain the model with improved data or adjust the flow logic, then monitor performance after redeployment.