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Free Microsoft Azure AI Fundamentals AI-900 Practice Test
Prepare for the Microsoft Azure AI Fundamentals AI-900 exam with this free practice test. Randomly generated and customizable, this test allows you to choose the number of questions.
- Questions: 15
- Time: 15 minutes (60 seconds per question)
- Included Objectives:Describe Artificial Intelligence Workloads and ConsiderationsDescribe Fundamental Principles of Machine Learning on AzureDescribe Features of Computer Vision Workloads on AzureDescribe Features of Natural Language Processing (NLP) Workloads on AzureDescribe features of generative AI workloads on Azure
A company wants to implement an AI system that can automatically extract key information such as dates, names, and locations from large volumes of unstructured text documents.
Which type of AI workload would best accomplish this task?
Computer vision
Predictive analytics
Anomaly detection
Natural language processing
Answer Description
Natural language processing is used to analyze and understand human language in text form. It enables the extraction of meaningful information from unstructured text data, such as identifying entities like dates, names, and locations. This is a process known as entity recognition.
Computer vision is related to image and video analysis.
Predictive analytics involves forecasting future trends from data.
Anomaly detection focuses on identifying outliers or unusual patterns in datasets.
Ask Bash
Bash is our AI bot, trained to help you pass your exam. AI Generated Content may display inaccurate information, always double-check anything important.
What specific tasks can natural language processing perform beyond entity recognition?
How does natural language processing differ from machine learning?
Can you provide examples of common applications that utilize natural language processing?
A company needs to extract and classify specific information such as names of people, organizations, locations, and dates from customer feedback data to gain insights.
Which feature of Azure AI services should they use to achieve this?
Entity Recognition
Sentiment Analysis
Language Modeling
Key Phrase Extraction
Answer Description
Entity Recognition is the Azure AI feature that identifies and classifies named entities in text, such as names of people, organizations, locations, and dates. This allows unstructured text data to be converted into structured information that can be analyzed and used for insights.
Sentiment Analysis determines the emotional tone (positive, negative, neutral) expressed in text but does not extract specific entities.
Key Phrase Extraction identifies the main points or key phrases in text but does not categorize them into entity types.
Language Modeling involves understanding and generating human language but is not used for extracting and classifying entities from text.
Ask Bash
Bash is our AI bot, trained to help you pass your exam. AI Generated Content may display inaccurate information, always double-check anything important.
What is Entity Recognition and how does it work?
How does Azure AI implement Entity Recognition?
What are the differences between Entity Recognition and Key Phrase Extraction?
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?
Clustering
Classification
Regression
Time Series Analysis
Answer Description
Clustering is the most appropriate technique for discovering inherent groupings or patterns in data without predefined labels. In this scenario, the retailer aims to segment customers based on similarities to tailor marketing strategies for each group.
Classification involves assigning data points to predefined categories using labeled data, which is not suitable here since the categories are not known beforehand.
Regression is used for predicting continuous numerical values, not for identifying patterns or groupings.
Time Series Analysis focuses on data points collected over time, which does not align with the goal of understanding customer behavior patterns in this context.
Ask Bash
Bash is our AI bot, trained to help you pass your exam. AI Generated Content may display inaccurate information, always double-check anything important.
What is clustering in machine learning?
How does clustering help in customer segmentation?
What is the difference between supervised and unsupervised learning?
A retail company wants to automate the process of extracting text from scanned documents to input into their accounting system.
Which computer vision solution should they use?
Facial Analysis
Object Detection
Optical Character Recognition (OCR)
Image Classification
Answer Description
Optical Character Recognition (OCR) is the appropriate solution for extracting text from images or scanned documents. OCR technology converts images of text into machine-encoded text, enabling the company to automate data entry from documents.
Image Classification categorizes images based on their overall content but does not extract textual information.
Object Detection identifies and locates objects within an image but does not interpret text.
Facial Analysis focuses on detecting and analyzing human facial features, which is not relevant to extracting text from documents.
Ask Bash
Bash is our AI bot, trained to help you pass your exam. AI Generated Content may display inaccurate information, always double-check anything important.
What is Optical Character Recognition (OCR) and how does it work?
What are the limitations of using OCR technology?
How does OCR compare to other computer vision techniques like image classification and object detection?
A manufacturing company wants to implement an AI system that can automatically identify defects in products by analyzing images captured during production.
Which AI workload should they focus on?
Computer Vision
Natural Language Processing (NLP)
Knowledge Mining
Content Moderation
Answer Description
Computer vision involves processing and analyzing visual information such as images and videos. For a task that requires analyzing images to identify defects, computer vision is the appropriate AI workload. Natural language processing (NLP) deals with text and speech data, knowledge mining focuses on extracting information from large datasets, and content moderation is used for filtering inappropriate content.
Ask Bash
Bash is our AI bot, trained to help you pass your exam. AI Generated Content may display inaccurate information, always double-check anything important.
What exactly is Computer Vision and how does it work?
What kinds of applications can benefit from Computer Vision?
What are some challenges faced in implementing Computer Vision?
In Azure Machine Learning, which component is used to deploy trained models to provide real-time predictions via web services?
Azure Machine Learning Endpoints
Azure Machine Learning Compute Instances
Azure Machine Learning Pipelines
Azure Blob Storage
Answer Description
Azure Machine Learning Endpoints allow you to deploy trained machine learning models as web services, enabling real-time predictions through RESTful APIs.
Azure Machine Learning Pipelines are used to build and manage automated workflows.
Azure Machine Learning Compute Instances provide development environments for machine learning tasks.
Azure Blob Storage is a service for storing unstructured data.
Ask Bash
Bash is our AI bot, trained to help you pass your exam. AI Generated Content may display inaccurate information, always double-check anything important.
What are Azure Machine Learning Endpoints?
How do I create an Azure Machine Learning Endpoint?
What is the difference between Azure Machine Learning Pipelines and Endpoints?
A company wants to implement a feature that converts audio input from users into text data for processing.
Which of the following capabilities should they use?
Sentiment Analysis
Speech Recognition
Key Phrase Extraction
Speech Synthesis
Answer Description
Speech Recognition is the capability that converts spoken language into text data. It allows applications to transcribe audio input from users for further processing, such as analysis or storage.
Key Phrase Extraction identifies important phrases in text but does not process audio.
Speech Synthesis converts text into spoken audio, which is the opposite of what is needed.
Sentiment Analysis determines the sentiment in text data but does not handle audio input.
Ask Bash
Bash is our AI bot, trained to help you pass your exam. AI Generated Content may display inaccurate information, always double-check anything important.
What is Speech Recognition and how does it work?
What are some common applications of Speech Recognition?
How is Speech Recognition different from Speech Synthesis?
An organization wants to group customers into segments based on similarities in their behavior, but they don't have labeled data.
Which machine learning technique should they utilize?
Classification
Time Series Analysis
Clustering
Regression
Answer Description
Clustering - This is the correct answer. Clustering is an unsupervised machine learning technique used to group data into segments based on similarities without requiring labeled data. It is ideal for grouping customers into segments based on their behavior.
Regression is used for predicting continuous numerical values based on input features, not for grouping data or segmenting customers.
Classification is a supervised learning technique where the goal is to categorize data into predefined classes. It requires labeled data, which is not available in this case.
Time Series Analysis is used to analyze data that is collected over time (e.g., stock prices, sales data) to identify trends or patterns. It is not focused on segmenting data into groups based on behavior.
Ask Bash
Bash is our AI bot, trained to help you pass your exam. AI Generated Content may display inaccurate information, always double-check anything important.
What is clustering in machine learning?
What are some common algorithms used for clustering?
How is clustering different from classification?
An Azure service can automatically categorize text data based on the emotions conveyed within the content.
True
False
Answer Description
This statement is True.
The Azure AI Language service provides sentiment analysis, which assesses the emotional tone of text. It categorizes sentiments as positive, neutral, or negative, allowing organizations to analyze customer feedback, reviews, or social media mentions to gain valuable insights into public opinion.
Ask Bash
Bash is our AI bot, trained to help you pass your exam. AI Generated Content may display inaccurate information, always double-check anything important.
What is sentiment analysis and how does it work in Azure AI?
What are the practical applications of sentiment analysis?
What are the differences between positive, neutral, and negative sentiments?
Which of the following scenarios is most suitable for using a regression machine learning model?
Classifying emails as important or not important
Determining if a transaction is fraudulent
Estimating future stock prices based on historical data
Grouping customers based on their purchasing behavior
Answer Description
Estimating the future price of a stock based on historical data involves predicting a continuous numerical value, which is the essence of regression. Regression models are used when the output variable is a real or continuous value. The other options are examples of classification or clustering tasks, not regression.
Ask Bash
Bash is our AI bot, trained to help you pass your exam. AI Generated Content may display inaccurate information, always double-check anything important.
What is a regression machine learning model?
What are some common use cases for regression models?
How does regression differ from classification in machine learning?
Sarah is a data analyst who needs to create a predictive model for sales forecasting but has limited experience in machine learning. She wants to use an Azure Machine Learning feature that simplifies the model creation process by automatically exploring different algorithms and tuning parameters.
Which feature should she use?
Azure Cognitive Services
Azure Machine Learning Designer
Automated Machine Learning
Azure Data Lake Analytics
Answer Description
Sarah should use Automated Machine Learning. This feature in Azure Machine Learning automates the process of model selection and hyperparameter tuning by experimenting with multiple algorithms and configurations to find the best model for her data. It is designed to help users who may not have deep machine learning expertise.
The Azure Machine Learning Designer allows users to build models using a drag-and-drop interface but still requires manual selection and tuning of algorithms.
Azure Data Lake Analytics is used for big data processing and query jobs, not for training machine learning models.
Azure Cognitive Services provide pre-built AI capabilities but do not allow for custom model training specific to Sarah's sales forecasting needs.
Ask Bash
Bash is our AI bot, trained to help you pass your exam. AI Generated Content may display inaccurate information, always double-check anything important.
What is Automated Machine Learning (AutoML)?
What are hyperparameters in machine learning?
How does Azure Machine Learning Designer differ from Automated Machine Learning?
Which model in Azure OpenAI Service enables developers to convert natural language prompts into code snippets?
BERT
Codex
GPT-3
DALL·E
Answer Description
Codex, available in Azure OpenAI Service, is designed specifically to convert natural language prompts into code snippets, making it ideal for developers who need to generate code from textual descriptions.
GPT-3 is a powerful language model capable of generating text, but Codex is the model specifically optimized for generating code from natural language prompts.
DALL·E is used to generate images from text descriptions, not for converting natural language into code snippets.
BERT is a language model designed for understanding the context of text, but it is not focused on generating code from natural language prompts.
Ask Bash
Bash is our AI bot, trained to help you pass your exam. AI Generated Content may display inaccurate information, always double-check anything important.
What is Azure OpenAI Service and how does it relate to Codex?
How does Codex differ from GPT-3 in terms of functionality?
What types of programming languages does Codex support for code generation?
A company wants to develop a model that can determine if a transaction is fraudulent or legitimate. What type of machine learning task is appropriate for this scenario?
Regression
Classification
Dimensionality Reduction
Clustering
Answer Description
Classification - This is the correct answer. Fraud detection is a classification task because the model needs to classify each transaction as either fraudulent or legitimate, which involves assigning data points to predefined categories or labels.
Regression is used for predicting continuous numerical values for example sales forecasts or prices, not for classifying transactions into categories such as "fraudulent" or "legitimate."
Clustering is an unsupervised learning technique used to group data based on similarities, but it is not suitable for determining whether a transaction is fraudulent or legitimate, which requires labeled data and a classification approach.
Dimensionality Reduction is used to reduce the number of features in the data, typically for improving performance or visualization, but it is not a task in itself for determining fraud or legitimacy.
Ask Bash
Bash is our AI bot, trained to help you pass your exam. AI Generated Content may display inaccurate information, always double-check anything important.
What is the difference between classification and regression in machine learning?
Can you explain what clustering is and how it differs from classification?
What is dimensionality reduction, and why is it important in machine learning?
Which of the following is a feature of OCR solutions?
Analyzing facial features to recognize emotions
Translating spoken language into text
Detecting and classifying objects within an image
Extracting text content from images or documents
Answer Description
Extracting text content from images or documents - This is the correct answer. Optical Character Recognition (OCR) solutions are specifically designed to extract text content from images or scanned documents, such as reading printed or handwritten text and converting it into machine-readable format.
Detecting and classifying objects within an image - This is the functionality of object detection or image classification, not OCR. These solutions identify and classify objects in images, but they do not extract text.
Analyzing facial features to recognize emotions - This refers to facial recognition and facial analysis solutions, which focus on detecting and analyzing facial features to determine emotions, not OCR.
Translating spoken language into text - This is the function of speech-to-text solutions, not OCR. Speech-to-text converts spoken language into written text, while OCR deals with extracting text from images or documents.
Ask Bash
Bash is our AI bot, trained to help you pass your exam. AI Generated Content may display inaccurate information, always double-check anything important.
What are some common applications of OCR technology?
What types of documents can OCR systems process?
How does OCR technology work?
Which type of machine learning workload involves synthesizing new content similar to examples it has been trained on?
Data Classification
Anomaly Detection
Content Synthesis tasks
Regression Analysis
Answer Description
Content Synthesis tasks - This is the correct answer. Content synthesis tasks involve generating new content that is similar to the examples the model has been trained on. This is typically seen in generative models, where the system can create new, original content such as text, images, or music based on the patterns it has learned.
Anomaly Detection focuses on identifying outliers or unusual patterns in data, not on generating new content. It is used for detecting abnormal behavior or data points.
Data Classification involves categorizing data into predefined classes or categories, such as labeling emails as spam or not. It does not involve generating new content.
Regression Analysis is used to predict numerical values based on historical data, such as forecasting sales or stock prices. It is not focused on generating new content.
Ask Bash
Bash is our AI bot, trained to help you pass your exam. AI Generated Content may display inaccurate information, always double-check anything important.
What are generative models in machine learning?
What is the difference between content synthesis and data classification?
Can you provide examples of applications for content synthesis tasks?
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