Welcome to the Microsoft AI-900 Azure AI Fundamentals certification exam, which will open your eyes to the intriguing fields of Artificial Intelligence and Machine Learning that are supported by the flexible Microsoft Azure services. With this exam, you can demonstrate your understanding of AI and ML ideas and how they integrate with the Azure platform.
You will start on a transforming journey as a candidate for this certification that cuts across both technical and non-technical backgrounds. This test welcomes everybody, making it accessible and interesting for everyone, regardless of your past data science and software engineering skills or where you are in your AI journey.
Microsoft AI-900 Certification Exam Questions and Answers
Goal of AI-900 Certification Exam: The goal of AI-900 is to equip you with the fundamentals of machine learning on Azure, allowing you to access a wide range of AI applications and concerns. You will delve into the fascinating field of computer vision as you gain knowledge about how AI can see and comprehend our surroundings. You will also explore the fascinating field of Natural Language Processing (NLP), where you will see how AI can comprehend and engage with human language.
Although prior knowledge of client-server apps and cloud fundamentals may be helpful, it is not a must. You may use Azure AI Fundamentals as a stepping stone to additional Azure role-based certifications like Azure Data Scientist Associate or Azure AI Engineer Associate since it welcomes students from different backgrounds.
What You’ll Learn: You will learn how AI is reshaping industries, improving daily experiences, and revolutionizing how we interact with technology as you set out on this momentous adventure. Your certification journey doesn’t end here; rather, it serves as a stepping stone to an AI-powered future where your knowledge will be essential in determining how the world will be in the future.
So, are you prepared to go into the world of artificial intelligence and machine learning with the might of Microsoft Azure? Together, let’s start this fascinating journey, and with the Microsoft AI-900 Azure AI Fundamentals certification, you may attain your full potential as an AI pioneer.
Correct Answer: A
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Analyzing sales data to identify trends and patterns represents an AI workload, where machine learning techniques can be applied to gain insights from data and make predictions.
Correct Answer: B
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The primary goal of supervised machine learning is to classify data into predefined categories based on labeled examples, where the model is trained using a dataset with known outcomes.
Correct Answer: B
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Identifying objects in images represents a computer vision workload, where AI models can be used to detect and recognize objects, faces, or patterns in images.
Correct Answer: C
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The primary application of sentiment analysis in NLP is to analyze emotions and opinions expressed in text data, determining whether the sentiment is positive, negative, or neutral.
Correct Answer: C
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Predictive Analytics Workload involves training AI models to predict future outcomes or behaviors based on historical data patterns and trends.
Correct Answer: B
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Feature engineering involves extracting and selecting relevant features from raw data to improve the performance and effectiveness of a machine learning model.
Correct Answer: C
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Natural Language Processing (NLP) Workload is used to analyze and process natural language text data, including tasks like sentiment analysis, language translation, and entity recognition.
Correct Answer: C
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Convolutional Neural Networks (CNN) are commonly used in computer vision to classify objects in an image, as they can automatically learn and recognize patterns and features.
Correct Answer: C
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The primary goal of named entity recognition (NER) in NLP is to identify and categorize entities in text, such as names of people, dates, locations, and other specific entities.
Correct Answer: C
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Azure Machine Learning is commonly used for building, training, and deploying machine learning models on Azure. It provides a scalable and integrated environment for the end-to-end machine learning workflow.
Correct Answer: A
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Data privacy and ethics are essential considerations in AI workloads to ensure that data is handled responsibly, protecting individuals’ privacy, and avoiding bias or discriminatory outcomes.
Correct Answer: D
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Hyperparameters are the settings or configurations that control the learning process of a machine learning model. Examples include learning rate, the number of hidden layers in a neural network, and the number of iterations during training.
Correct Answer: D
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Anomaly Detection Workload is best suited for detecting credit card fraud, as it can identify unusual patterns or outliers in data, which may indicate fraudulent activities.
Correct Answer: B
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The purpose of object detection in computer vision is to detect and recognize objects or patterns within an image, allowing AI models to identify multiple objects and their locations.
Correct Answer: D
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Machine Translation is a common NLP technique used for language translation tasks, where AI models are trained to translate text from one language to another.
Correct Answer: A
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Explainable AI refers to AI models that can interpret and explain their decision-making process, providing insights into why a certain prediction or decision was made. It is essential to ensure transparency, accountability, and user trust in AI applications.
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