Use this App to prepare and succeed in the Microsoft Azure AI-900 Azure AI Fundamentals Certification. This Azure AI-900 Azure AI Fundamentals Certification Preparation App provides: - 200+ Azure AI-900 Questions and Detailed Answers and References - 100+ Machine Learning Basics Questions and Answers - 100+ Machine Learning Advanced Questions and Answers - Scorecard - Countdown timer - Machine Learning CheatSheets - Machine Learning Interview Questions and Answers - Machine Learning Latest News The Azure AI Fundamentals AI-900 Exam Prep App covers: Azure AI 900, ML, NLP, Describe Artificial Intelligence workloads and considerations, Describe fundamental principles of machine learning on Azure, Describe features of computer vision workloads on Azure, Describe features of Natural Language Processing (NLP) workloads on Azure , Describe features of conversational AI workloads on Azure, QnA Maker service, Language Understanding service (LUIS), Speech service, Translator Text service, Form Recognizer service, Face service, Custom Vision service, Computer Vision service, facial detection, facial recognition, and facial analysis solutions, optical character recognition solutions, object detection solutions, image classification solutions, azure Machine Learning designer, automated ML UI, conversational AI workloads, anomaly detection workloads, forecasting workloads identify features of anomaly detection work, NLP, Modelling, Data Engineering, Computer Vision, Exploratory Data Analysis, ML implementation and Operations, Quiz and Brain Teaser for Azure AI Fundamentals AI-900, Machine Learning, Kafka, SQl, NoSQL, Python, DocumentDB, linear regression, logistic regression, Sampling, dataset, statistical interaction, selection bias, non-Gaussian distribution, bias-variance trade-off, Normal Distribution, correlation and covariance, Point Estimates and Confidence Interval, A/B Testing, p-value, statistical power of sensitivity, over-fitting and under-fitting, regularization, Law of Large Numbers, Confounding Variables, Survivorship Bias, univariate, bivariate and multivariate, Resampling, ROC curve, TF/IDF vectorization, Cluster Sampling, etc. This App can help you: - Identify features of common AI workloads - identify prediction/forecasting workloads - identify features of anomaly detection workloads - identify computer vision workloads - identify natural language processing or knowledge mining workloads - identify conversational AI workloads - Identify guiding principles for responsible AI - describe considerations for fairness in an AI solution - describe considerations for reliability and safety in an AI solution - describe considerations for privacy and security in an AI solution - describe considerations for inclusiveness in an AI solution - describe considerations for transparency in an AI solution - describe considerations for accountability in an AI solution - Identify common types of computer vision solution: - Identify Azure tools and services for computer vision tasks - identify features and uses for key phrase extraction - identify features and uses for entity recognition - identify features and uses for sentiment analysis - identify features and uses for language modeling - identify features and uses for speech recognition and synthesis - identify features and uses for translation identify capabilities of the Text Analytics service - identify capabilities of the Language Understanding service (LUIS) - identify capabilities of the Speech service - identify capabilities of the Translator Text service - identify features and uses for webchat bots - identify common characteristics of conversational AI solutions - identify capabilities of the QnA Maker service - identify capabilities of the Azure Bot service Note and disclaimer: We are not affiliated with Microsoft or Azure. The questions are put together based on the certification study guide and materials available online. The questions in this app should help you pass the exam but it is not guaranteed. We are not responsible for any exam you did not pass. - Identify common machine learning types - identify regression machine learning scenarios - identify classification machine learning scenarios - identify clustering machine learning scenarios - identify features and labels in a dataset for machine learning - describe how training and validation datasets are used in machine learning - describe how machine learning algorithms are used for model training - select and interpret model evaluation metrics for classification and regression - describe common features of data ingestion and preparation - describe feature engineering and selection - describe common features of model training and evaluation - describe common features of model deployment and management - Describe capabilities of no-code machine learning with Azure Machine Learning studio Important: To succeed with the real exam, do not memorize the answers in this app. It is very important that you understand why a question is right or wrong and the concepts behind it by carefully reading the reference documents in the answers.