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Microsoft Azure AI Fundamentals Practice Test (AI-900)

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Microsoft Azure AI Fundamentals AI-900 Information

The Microsoft Certified: Azure AI Fundamentals (AI-900) exam is an entry-level certification designed for individuals seeking foundational knowledge of artificial intelligence (AI) and machine learning (ML) concepts and their applications within the Microsoft Azure platform. The AI-900 exam covers essential AI workloads such as anomaly detection, computer vision, and natural language processing, and it emphasizes responsible AI principles, including fairness, transparency, and accountability. While no deep technical background is required, a basic familiarity with technology and Azure’s services can be helpful, making this certification accessible to a wide audience, from business decision-makers to early-career technologists.

The exam covers several major domains, starting with AI workloads and considerations, which introduces candidates to various types of AI solutions and ethical principles. Next, it delves into machine learning fundamentals, explaining core concepts like data features, model training, and types of machine learning such as classification and clustering. The exam also emphasizes specific Azure tools for implementing AI solutions, such as Azure Machine Learning Studio for visual model-building, the Computer Vision service for image analysis, and Azure Bot Service for conversational AI. Additionally, candidates learn how natural language processing (NLP) tasks, including sentiment analysis, translation, and speech recognition, are managed within Azure’s language and speech services.

Achieving the AI-900 certification demonstrates a solid understanding of AI and ML basics and prepares candidates for more advanced Azure certifications in data science or AI engineering. It’s an excellent credential for those exploring how AI solutions can be effectively used within the Azure ecosystem, whether to aid business decision-making or to set a foundation for future roles in AI and data analytics.

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  • Free Microsoft Azure AI Fundamentals AI-900 Practice Test

  • 20 Questions
  • Unlimited
  • 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 generative AI workloads on Azure
Question 1 of 20

A retailer wants to analyze customer reviews to determine overall customer satisfaction.

Which AI workload is best suited for this task?

  • Computer Vision workload

  • Content Moderation workload

  • Document Intelligence workload

  • Natural Language Processing (NLP) workload

Question 2 of 20

A bank wants to segment its customers into different categories based on their spending habits and transaction history to tailor marketing strategies.

Which machine learning technique is most suitable for this objective?

  • Classification

  • Reinforcement Learning

  • Regression

  • Clustering

Question 3 of 20

A retailer wants to implement a system that can track and count individuals in surveillance video to monitor foot traffic in their store.

Which type of computer vision solution would best meet this need?

  • Optical Character Recognition (OCR)

  • Image Classification

  • Object Detection

  • Facial Detection and Analysis

Question 4 of 20

Which of the following best explains why an AI solution requires regular updates and maintenance after deployment to ensure its reliability and safety?

  • To significantly reduce the initial cost of developing the AI model.

  • To change the underlying AI architecture from a neural network to a decision tree.

  • To fulfill a one-time deployment checklist required by software vendors.

  • To address potential data drift and maintain the model's performance.

Question 5 of 20

A company wants to automatically create unique marketing slogans based on their brand values and target audience.

Which technology approach is most suitable for generating these slogans?

  • Implementing predictive analytics to forecast market trends

  • Using clustering algorithms to segment customer data

  • Applying sentiment analysis to gauge customer opinions

  • Utilizing generative models for content creation

Question 6 of 20

You are building an AI-powered customer service chatbot that will be used by a global audience, including customers who rely on assistive technologies such as screen readers and voice-control software. To make the chatbot more accessible, you add semantic labels for every UI element, ensure full keyboard navigation, and include descriptive alt-text for any images generated by the system. Which Microsoft Responsible AI principle are you primarily addressing with these design decisions?

  • Fairness

  • Transparency

  • Inclusiveness

  • Accountability

Question 7 of 20

Azure OpenAI Service's image generation models can create images based on audio prompts provided by users.

  • True

  • False

Question 8 of 20

Your software development team wants to implement an AI assistant that can generate code snippets based on natural language descriptions.

Which Azure OpenAI model should they use for this purpose?

  • DALL·E

  • Codex

  • GPT-3's text-davinci-003

  • Azure's Computer Vision API

Question 9 of 20

A company wants to implement an AI chatbot that can produce human-like responses to customer inquiries.

Which Azure service capability would best support this solution?

  • Use Azure OpenAI Service for natural language generation

  • Deploy a chatbot using Azure Bot Service Gallery

  • Utilize Azure Cognitive Services' Speech Recognition

  • Use Azure Cognitive Search to retrieve relevant information

Question 10 of 20

A company collected data to develop a machine learning model that predicts the final selling price of products based on factors like 'Production Cost', 'Marketing Budget', 'Competitor Prices' and 'Time on Market'.

In this context, which variable is the label for the model?

  • Competitor Prices

  • Final Selling Price

  • Marketing Budget

  • Production Cost

Question 11 of 20

An online retailer wants to understand patterns in customer behavior based on purchase history, browsing behavior, and demographic data to better tailor its marketing strategies.

Which machine learning technique is most appropriate for this task?

  • Time Series Analysis

  • Classification

  • Clustering

  • Regression

Question 12 of 20

A global e-commerce company wants their product descriptions to be accessible to customers in multiple languages automatically within their app.

Which Azure service should they use to implement this multilingual text conversion feature?

  • Azure AI Speech service

  • Azure AI Translator service

  • Azure AI Language service

Question 13 of 20

You are developing an application that needs to determine the attitude expressed in customer comments, categorizing them as positive, negative, or neutral.

Which feature of the Azure AI Language service should you use?

  • Key Phrase Extraction

  • Translation

  • Entity Recognition

  • Sentiment Analysis

Question 14 of 20

An AI engineer is working on a project that involves analyzing vast amounts of unstructured data, such as images and speech. She needs to build a model that can automatically learn hierarchical representations from raw data without extensive feature engineering.

Which machine learning technique is most appropriate for this scenario?

  • Deep Learning

  • Regression Algorithms

  • Clustering Algorithms

  • Decision Trees

Question 15 of 20

A company wants to extract structured information such as names of people, places, organizations, and dates from a large collection of unstructured text documents.

Which natural language processing technique should they use to achieve this?

  • Key Phrase Extraction

  • Language Translation

  • Sentiment Analysis

  • Entity Recognition

Question 16 of 20

An organization needs to digitally extract text from a large number of scanned documents and images containing both printed and handwritten text. They require a solution that can process unstructured data effectively.

Which feature of Azure AI services is most suitable for their needs?

  • Text Analytics to analyze and interpret text sentiment

  • Computer Vision's text reading capability

  • Form Recognizer to analyze and extract structured data

  • Face API to detect and analyze faces in images

Question 17 of 20

As a data scientist at a software development company, you are considering models for generating synthetic data to enhance your testing datasets.

Which feature of generative AI models makes them suitable for this task?

  • They can classify data into specific categories with high precision.

  • They can reduce data dimensionality while retaining key features.

  • They can generate new data instances similar to the training data.

  • They can identify anomalies by learning normal data patterns.

Question 18 of 20

A developer needs to detect faces in photos and retrieve 27 facial landmark points, head-pose angles, and a limited age-estimate attribute. Which Azure service should they use?

  • Azure Cognitive Search service

  • Azure Computer Vision service

  • Azure Video Indexer service

  • Azure Face service

Question 19 of 20

You are building an application that enables users to produce images by providing descriptive text inputs.

Which feature of Azure OpenAI Service would you utilize to implement this functionality?

  • Leverage the service's ability to generate images from text descriptions.

  • Implement image analysis to extract information from images.

  • Use code generation features to create image-rendering scripts.

  • Apply language translation capabilities to interpret user inputs.

Question 20 of 20

In machine learning, which of the following best describes the purpose of a validation dataset?

  • To collect new data for expanding the training dataset

  • To test the final model performance on unseen data after training is complete

  • To adjust model hyperparameters and prevent overfitting by evaluating the model's performance during training

  • To train the model by providing examples for it to learn from