Mastering AI Fundamentals: Prepare for AI-900 Exam with Detailed Sample Questions

Mastering AI Fundamentals: Prepare for AI-900 Exam with Detailed Sample Questions

Table of Contents:

1. Introduction
2. Key Elements of Artificial Intelligence
3. Principles of Responsible AI
4. Machine Learning Models
5. Pre-processing Data for Model Prediction
6. Confusion Matrix in Classification Models
7. Featurization in Model Training
8. Compute Resources in Azure Machine Learning Studio
9. Object Detection Models
10. Cognitive Services for Document Scanning
11. Providing Information to Developers for Custom Vision Models
12. Sentiment Analysis for Customer Reviews
13. Entities in Lewis Applications
14. Components for Creating a Web Chat Bot
15. Building a Virtual Assistant with Azure Bot Service
16. Extending Bot Capabilities with Skills

Key Elements of Artificial Intelligence

Artificial intelligence (AI) encompasses several key elements that are crucial in understanding its functioning. These elements include object detection, automated machine learning, software creation, solution deployment, and providing information about solutions' possibilities and limitations. Object detection plays a vital role in computer vision by identifying and locating objects in images or videos. Automated machine learning automates the time-consuming process of machine learning tasks. When we think of AI, we envision the creation of software that imitates human behavior. All these elements collectively contribute to the development and implementation of artificial intelligence systems.

In the field of responsible AI development, Microsoft has identified six guiding principles. One of these principles is transparency, which focuses on making AI solutions' behavior, possibilities, and limitations explicit to users. Fairness is another crucial principle, aiming to reduce bias and ensure equal recommendations for individuals with similar characteristics. Reliability and safety are essential to ensure AI systems respond appropriately in various situations and operate reliably. Privacy and security protect personal and business information from unauthorized access. Inclusiveness ensures that AI developers address potential barriers and do not exclude any groups unintentionally. Lastly, accountability focuses on holding humans responsible and accountable for the actions and impacts of highly autonomous AI systems.

Machine Learning Models

In the world of machine learning, different types of models play distinct roles in data analysis. Supervised machine learning models, such as regression models, are trained with labeled data to predict a numeric value Based on input features. These models rely on historical data to learn the true relationship between the inputs and the labels. Unsupervised models, on the other HAND, are used to uncover Patterns and clusters in data sets without the presence of labeled data. This process aids in segmenting customers and personalizing marketing strategies. Both supervised and unsupervised models have their specific use cases and applications in the field of machine learning.

Pre-processing Data for Model Prediction

Before training a machine learning model, pre-processing of the data is essential to ensure accurate predictions. During the pre-processing stage, data is prepared by selecting features that influence the label prediction. This process involves narrowing down the features or characteristics of the data that are Relevant to the prediction task. Additionally, the data is divided into training and test sets to evaluate the model's performance. Other steps in the pre-processing stage include scaling and normalizing the data to ensure consistency and eliminate any biases. By following these pre-processing steps, machine learning models can make more accurate predictions based on the provided data.

Confusion Matrix in Classification Models

A confusion matrix is a table that describes the performance of a classification model. It compares the actual labels in the dataset with the predicted labels generated by the model. The matrix shows the number of true positives, true negatives, false positives, and false negatives. For instance, in a binary classification task, the confusion matrix would be a 2x2 table. Each cell represents the number of instances that are correctly or wrongly classified. This matrix helps evaluate the model's accuracy and identify any misclassifications, which is crucial in assessing the model's performance.

Featurization in Model Training

Featurization is the process of selecting labels and features, as well as scaling and normalizing them, in preparation for model training. It involves utilizing domain knowledge to determine which features are relevant to the prediction task. Featurization encompasses various steps, such as identifying the entities that impact the model's results and selecting the most significant features. This process ensures that the model learns from high-quality data and produces accurate predictions. Feature selection, scaling, and normalization are crucial steps in featurization that contribute to the effectiveness of machine learning models.

Compute Resources in Azure Machine Learning Studio

In Azure Machine Learning Studio, there are four types of compute resources available. These resources cater to different requirements and computing needs. The first type is the CPU or GPU-based compute instance, which allows users to Create virtual machines with different sizes according to their needs. The Second Type is a cluster of compute nodes that enable auto scaling, specifying the minimum and maximum number of nodes for job execution. The third option is the inference cluster, which involves creating or utilizing an existing Azure Kubernetes cluster for model deployment. Lastly, there is the option of attaching existing compute resources, such as virtual machines or data lakes, for processing. Azure Machine Learning Studio provides a range of compute resources to ensure flexibility and scalability in machine learning tasks.

Object Detection Models

Object detection is a critical technique in computer vision that involves identifying and locating objects in images or videos. It plays a significant role in various applications such as surveillance, autonomous vehicles, and image recognition. Object detection models provide three key pieces of information about each detected object. Firstly, the model determines the class or category that the object belongs to. Additionally, it assigns a probability score to indicate the confidence level of the classification. Finally, the model provides the coordinates of the bounding box that encloses each detected object. These components collectively enable the model to accurately identify and locate objects within an image or video.

Cognitive Services for Document Scanning

In the realm of document scanning, two APIs offered by Azure's Computer Vision service are particularly relevant. The Optical Character Recognition (OCR) API is designed for extracting small amounts of text from images. It is suitable for scenarios where the text characters are minimal, with the image being less text-dominant. On the other hand, the Read API is specifically designed for extracting text from text-dominant images, making it the ideal choice for scanning documents with multiple pages. Thus, when faced with the task of scanning documents, especially those with lengthy content, the Read API is the recommended solution.

Providing Information to Developers for Custom Vision Models

When using the Custom Vision portal to create and train custom vision models, developers need specific information to utilize these models effectively. Four essential pieces of information should be provided to developers. Firstly, the project ID is necessary to identify and access the intended project. Secondly, the model name indicates the specific model within the project. The prediction key grants access to the prediction endpoint, allowing developers to make predictions using the model. These pieces of information facilitate the integration and usage of custom vision models within development projects.

Sentiment Analysis for Customer Reviews

Sentiment analysis is a valuable tool in analyzing customer reviews and feedback. By assigning a sentiment score to each review, businesses can gain insights into customer satisfaction. Sentiment scores typically range between 0 and 1, with scores close to 0 indicating negative sentiment and scores close to 1 representing positive sentiment. The given review, "The prices were ridiculously high; we could stay at the palace for that price. The Water in the shower was cold, no hot water whatsoever," clearly conveys a negative sentiment. Therefore, the sentiment score for this review would be close to zero, reflecting the dissatisfaction expressed by the customer.

Entities in Lewis Applications

Lewis applications utilize three essential terms: intents, entities, and utterances. Utterances refer to user queries or sentences that require action from the application. Intents represent the actions the application should take in response to the uttered query. Each utterance can have only one intent but can involve multiple entities, which modify the intent. Entities act as metadata for intents, providing additional information to enhance the application's understanding of the query. The four types of entities that can be created during the authoring of Lewis applications are machine-learned entities, list entities, regex entities, and pattern.any entities.

Components for Creating a Web Chat Bot

To build a simple web chat bot, two components are essential: a knowledge base and a bot service. The knowledge base contains existing FAQ documents or question-answer pairs. This knowledge base is built using natural language processing (NLP) models to enable the bot to comprehend queries phrased in various ways. The bot service acts as the interface between the knowledge base and the user, allowing the bot to respond to user queries via chat interfaces or other channels. The combination of a knowledge base and a bot service forms the foundation for resolving user support queries effectively.

Building a Virtual Assistant with Azure Bot Service

Azure Bot Service is a valuable tool for constructing personal virtual assistants. This service enables the connection of virtual assistants with various input channels and devices. By using the Bot Framework and Azure Bot Service, developers can build bots that serve as virtual assistants. These virtual assistants can then leverage the capabilities of skills, which are reusable building blocks. Skills extend the functionality of the virtual assistant by performing specific tasks. By combining Azure Bot Service, the Bot Framework, and skills, developers can create powerful and personalized virtual assistants.

Extending Bot Capabilities with Skills

Skills are an integral part of extending the capabilities of a root bot, such as a virtual assistant. They enable a root bot to Consume and utilize one or more skills, which are essentially bots themselves. Skills are utilized to enhance the functionality of a root bot by performing specific tasks. The consumption of skills by a root bot allows for access to additional capabilities and expertise that can be leveraged to meet user requirements. Skills enable root bots to handle more complex scenarios and provide users with a comprehensive and personalized experience.

This article covered the fundamentals of artificial intelligence, including the key elements, principles of responsible AI, machine learning models, pre-processing data, confusion matrix in classification models, featurization, compute resources in Azure Machine Learning Studio, object detection models, cognitive services for document scanning, providing information to developers for custom vision models, sentiment analysis for customer reviews, entities in Lewis applications, components for creating a web chat bot, building a virtual assistant with Azure Bot Service, and extending bot capabilities with skills.