Visit here for our full Microsoft AI-900 exam dumps and practice test questions.
Question 61:
Which of the following Azure services can be used to extract structured data from unstructured text in documents, such as invoices or receipts?
A) Azure Form Recognizer
B) Azure Computer Vision
C) Azure Text Analytics
D) Azure Speech Service
Answer: A) Azure Form Recognizer
Explanation:
Azure Form Recognizer is the most suitable Azure service for extracting structured data from unstructured text in documents like invoices, receipts, and forms. It is an AI-powered document extraction service that uses machine learning models to analyze and extract relevant information from a variety of document types, such as handwritten or printed text, tables, and key-value pairs.
Key features of Azure Form Recognizer:
Customizable: It can be trained to recognize specific fields in documents, making it useful for custom document types.
OCR (Optical Character Recognition): It can extract text from scanned documents or images, which is then processed into structured data.
Document Layout Analysis: It identifies the structure of a document, including text, tables, and other layout features, making it easy to extract data from complex document formats.
Pre-built models: Azure offers pre-built models to extract data from common forms like invoices and receipts, reducing the need for custom training.
A) Azure Form Recognizer is designed specifically for document analysis and data extraction. It is tailored for situations where you need to extract key-value pairs, tables, and other structured data from unstructured text.
B) Azure Computer Vision is another image processing service, but it is more focused on analyzing images and extracting features like text (using OCR), objects, and other visual elements. While Computer Vision can read and extract text from images, it is not designed for the advanced extraction of structured data from forms.
C) Azure Text Analytics focuses on natural language processing (NLP) tasks like sentiment analysis, language detection, and entity recognition. It is not designed to handle the extraction of structured data from unstructured documents.
D) Azure Speech Service is designed for speech-to-text, text-to-speech, and speaker identification tasks. It does not deal with extracting data from documents, making it irrelevant for this scenario.
Question 62:
Which Azure service would you use to deploy a machine learning model and monitor its performance in production?
A) Azure Machine Learning
B) Azure Databricks
C) Azure Kubernetes Service (AKS)
D) Azure App Service
Answer: A) Azure Machine Learning
Explanation:
Azure Machine Learning is the most appropriate service for deploying machine learning models and monitoring their performance in production. This fully managed service enables data scientists and developers to create, train, and deploy machine learning models at scale, with built-in tools for monitoring and managing the models once they are deployed.
Key features of Azure Machine Learning:
Model Deployment: Azure Machine Learning provides several deployment options, such as Azure Kubernetes Service (AKS), Azure Container Instances (ACI), and IoT Edge for deploying models into production environments.
Model Monitoring: The platform offers tools to monitor deployed models, track performance metrics, and detect issues like data drift, helping you to ensure that your models are running as expected in production.
Model Versioning: Azure Machine Learning helps manage different versions of your models and facilitates rollback in case of performance degradation.
End-to-End Workflow: The service provides an integrated environment for all phases of the machine learning lifecycle, from data preprocessing and model training to deployment and monitoring.
B) Azure Databricks is a platform designed for big data processing and machine learning, built on Apache Spark. While it is great for training models and processing large datasets, Azure Databricks does not provide the same level of deployment and monitoring capabilities as Azure Machine Learning.
C) Azure Kubernetes Service (AKS) is a container orchestration service that is often used in conjunction with Azure Machine Learning for deploying and managing machine learning models in containers at scale. While AKS can host deployed models, it does not provide comprehensive monitoring and model management features like Azure Machine Learning does.
D) Azure App Service is a platform-as-a-service (PaaS) offering designed for hosting web applications and APIs. While you can deploy models as APIs using Azure App Service, it lacks the dedicated machine learning deployment and monitoring features that Azure Machine Learning offers.
Question 63:
Which of the following services is most suitable for building conversational AI applications such as chatbots?
A) Azure Language Understanding (LUIS)
B) Azure Machine Learning
C) Azure Cognitive Search
D) Azure Text Analytics
Answer: A) Azure Language Understanding (LUIS)
Explanation:
Azure Language Understanding (LUIS) is the most suitable service for building conversational AI applications, such as chatbots. LUIS is a natural language processing (NLP) service that allows you to build language models that can understand user intents and extract key information from text or speech. It is specifically designed to help developers create applications that can understand and respond to human language.
Key features of LUIS:
Intent Recognition: It identifies the intent behind a user’s query (e.g., booking a flight, ordering food).
Entity Extraction: It extracts specific information, such as dates, locations, and quantities, from user input.
Integration with other Azure services: LUIS can be integrated with Azure Bot Services, allowing you to create conversational agents or chatbots that can interact with users through multiple channels like websites, mobile apps, and messaging platforms.
Customization: Developers can train LUIS models with custom intents and entities tailored to their application’s specific use cases.
B) Azure Machine Learning is a platform for building and deploying machine learning models but is not specifically designed for building conversational AI applications. It is better suited for tasks like predictive analytics, image classification, and other machine learning workloads.
C) Azure Cognitive Search is a search-as-a-service solution that enables developers to build powerful search applications. While it can be used to improve the search capabilities within a chatbot, it is not a conversational AI platform and does not provide natural language understanding for creating chatbots.
D) Azure Text Analytics provides natural language processing capabilities for text analysis, such as sentiment analysis, entity recognition, and language detection. While it can be useful in processing text data, it is not designed to build conversational AI applications like LUIS.
Question 64:
Which Azure service is best for building custom image classification models without requiring deep knowledge of machine learning?
A) Azure Custom Vision
B) Azure Computer Vision
C) Azure Machine Learning
D) Azure Databricks
Answer: A) Azure Custom Vision
Explanation:
Azure Custom Vision is the best service for building custom image classification models without requiring deep knowledge of machine learning. Custom Vision provides an easy-to-use interface for training custom models to classify images into predefined categories. It uses transfer learning, allowing you to leverage pre-trained models and fine-tune them with your own labeled image data.
Key features of Azure Custom Vision:
Ease of Use: You can easily upload images, label them according to categories, and train a model with just a few clicks.
Custom Model Training: Custom Vision supports training models for specific tasks, such as object detection, image classification, and image segmentation.
Transfer Learning: It uses transfer learning, which allows you to start with a pre-trained model and fine-tune it to your specific needs, reducing the amount of labeled data required.
Integration: Once trained, the model can be deployed as an API for integration with applications.
B) Azure Computer Vision provides pre-built models for image analysis tasks, such as object detection, text extraction, and image categorization. However, it is not designed for custom image classification tasks like Custom Vision. Computer Vision is more suited for using pre-trained models to analyze general images.
C) Azure Machine Learning is a comprehensive platform for building, training, and deploying machine learning models. While it can be used for custom image classification, it requires more expertise in machine learning compared to Custom Vision, which is tailored specifically for image classification tasks.
D) Azure Databricks is a platform for big data processing and machine learning, built on Apache Spark. It is better suited for large-scale data processing tasks and training complex models but is not specifically optimized for building custom image classification models as Custom Vision is.
Question 65:
Which of the following Azure services can be used to analyze text data to determine sentiment, key phrases, and named entities in the text?
A) Azure Text Analytics
B) Azure Language Understanding (LUIS)
C) Azure Cognitive Search
D) Azure Machine Learning
Answer: A) Azure Text Analytics
Explanation:
Azure Text Analytics is the best service for analyzing text data to determine sentiment, key phrases, and named entities. It is a part of Azure Cognitive Services and provides several NLP capabilities that can be applied to text data to derive valuable insights.
Key features of Azure Text Analytics:
Sentiment Analysis: It analyzes text and determines whether the sentiment is positive, negative, or neutral. This is useful for understanding customer feedback, reviews, and social media posts.
Key Phrase Extraction: It extracts key phrases or keywords from text that represent the main concepts or topics discussed.
Named Entity Recognition (NER): It identifies entities such as people, organizations, locations, dates, and other important items mentioned in the text.
Language Detection: It automatically detects the language in which the text is written.
B) Azure Language Understanding (LUIS) is primarily focused on building conversational AI applications, such as chatbots. It helps understand user intents and extract entities from conversations but does not provide the same set of NLP features for general text analysis as Azure Text Analytics.
C) Azure Cognitive Search is a search-as-a-service solution and provides full-text search capabilities, but it does not offer the same text analytics features like sentiment analysis and entity extraction as Azure Text Analytics.
D) Azure Machine Learning is a comprehensive machine learning platform, but it is not a specialized service for text analytics. While you can build custom models using Azure Machine Learning for text analysis, Text Analytics provides a much more streamlined solution for general text processing tasks like sentiment analysis and named entity recognition.
Question 66:
Which Azure service allows you to automate workflows by integrating with multiple services and systems, including AI and machine learning models?
A) Azure Logic Apps
B) Azure Cognitive Services
C) Azure Machine Learning
D) Azure Databricks
Answer: A) Azure Logic Apps
Explanation:
Azure Logic Apps is the best service for automating workflows and integrating with multiple services and systems, including AI and machine learning models. It enables you to automate tasks and business processes by building workflows that connect various Azure services, on-premises systems, and third-party applications.
Key features of Azure Logic Apps:
Automation of Business Processes: You can design workflows to automate processes like data transfer, notifications, and even machine learning predictions.
Integration with AI: Logic Apps integrates with AI and machine learning services, such as Azure Cognitive Services and Azure Machine Learning, to trigger workflows based on AI predictions or insights.
Pre-built Connectors: It includes many pre-built connectors for popular services (e.g., Office 365, Salesforce, Twitter), making it easy to connect disparate systems.
Conditional Logic: You can add conditions, loops, and other complex logic to workflows, allowing for sophisticated process automation.
Low-code Platform: The platform is designed to be low-code, enabling both technical and non-technical users to create workflows without writing complex code.
B) Azure Cognitive Services is a suite of pre-built AI services that provides vision, speech, language, and decision-making capabilities. While Cognitive Services can provide AI functionality, it does not handle the automation of workflows.
C) Azure Machine Learning is a platform for building, training, and deploying machine learning models, but it is not designed to automate workflows or integrate multiple systems. Machine Learning is focused on model development rather than workflow automation.
D) Azure Databricks is a collaborative analytics platform designed for big data processing and machine learning. While it is useful for large-scale data analysis and model training, it does not provide workflow automation or integration features like Logic Apps.
Question 67:
Which Azure service should you use for real-time analytics of streaming data from IoT devices or sensors?
A) Azure Stream Analytics
B) Azure Databricks
C) Azure Event Hubs
D) Azure IoT Hub
Answer: A) Azure Stream Analytics
Explanation:
Azure Stream Analytics is the most suitable service for real-time analytics of streaming data from IoT devices or sensors. Stream Analytics is a fully managed real-time analytics service that can process large amounts of streaming data, such as telemetry data from IoT devices, log files, and social media feeds, and provide real-time insights.
Key features of Azure Stream Analytics:
Real-Time Data Processing: It can ingest, process, and analyze streaming data in real-time, providing valuable insights as the data is generated.
Integration with IoT Services: It can integrate with Azure IoT Hub and Azure Event Hubs to collect data from IoT devices and sensors and perform real-time analytics.
Scalability: The service can scale automatically to accommodate varying data volumes and processing requirements.
Output to Multiple Destinations: You can send the processed data to various destinations, such as Azure SQL Database, Azure Data Lake Storage, or even Power BI for real-time visualization.
Query Language: Stream Analytics uses a familiar SQL-like query language, making it easy to create data processing pipelines without needing deep knowledge of programming.
B) Azure Databricks is designed for big data processing and machine learning workloads. While it can process large volumes of data, it is not specifically optimized for real-time analytics of streaming data like Stream Analytics.
C) Azure Event Hubs is a service for ingesting large amounts of event data, such as telemetry from IoT devices. However, Event Hubs is more focused on event ingestion and does not perform analytics. You would typically use Event Hubs as an input source for Stream Analytics to analyze the data.
D) Azure IoT Hub is a managed service for connecting, monitoring, and managing IoT devices. While it can facilitate the ingestion of IoT device data, it does not offer real-time analytics like Stream Analytics.
Question 68:
Which Azure service would you use to analyze large volumes of text data and detect specific entities, such as names of people, organizations, and locations?
A) Azure Text Analytics
B) Azure Cognitive Search
C) Azure Machine Learning
D) Azure Databricks
Answer: A) Azure Text Analytics
Explanation:
Azure Text Analytics is the most suitable service for analyzing large volumes of text data and detecting specific entities such as names of people, organizations, and locations. Text Analytics is a natural language processing (NLP) service that provides various text analysis capabilities, including entity recognition, sentiment analysis, and language detection.
Key features of Azure Text Analytics:
Entity Recognition: The service automatically identifies named entities (e.g., people, organizations, locations) in text, which is useful for extracting structured information from unstructured text.
Sentiment Analysis: It can analyze text data to determine the overall sentiment (positive, negative, or neutral).
Language Detection: Text Analytics can automatically detect the language of a given text, making it versatile for multilingual applications.
Key Phrase Extraction: The service can extract key phrases from text, which can help identify important concepts or topics.
B) Azure Cognitive Search is a search-as-a-service solution that can help index and search large volumes of data, including text. While it offers full-text search and some AI capabilities, it is not specialized in entity recognition or other NLP tasks like Text Analytics.
C) Azure Machine Learning provides a platform for building and deploying custom machine learning models. While it can be used for text analysis, it does not provide the pre-built, easy-to-use capabilities for entity recognition and sentiment analysis that Text Analytics offers.
D) Azure Databricks is a collaborative platform for big data processing and machine learning but does not specialize in text analysis tasks like entity recognition, sentiment analysis, or key phrase extraction.
Question 69:
Which of the following services allows you to train and deploy custom computer vision models without requiring deep knowledge of machine learning?
A) Azure Custom Vision
B) Azure Machine Learning
C) Azure Databricks
D) Azure Cognitive Services
Answer: A) Azure Custom Vision
Explanation:
Azure Custom Vision allows you to train and deploy custom computer vision models with minimal expertise in machine learning. It is a user-friendly service that lets you upload images, label them, and train a model that can recognize and classify objects in those images.
Key features of Azure Custom Vision:
Easy Image Classification: Users can easily upload and label images to create custom classification models.
Transfer Learning: Custom Vision uses transfer learning, which allows it to fine-tune pre-trained models with your own labeled data. This significantly reduces the need for large datasets and deep technical knowledge.
Object Detection: In addition to classification, Custom Vision supports object detection, where the model can identify and locate specific objects within images.
Deployable Models: Once the model is trained, it can be deployed as an API, making it easy to integrate into other applications.
No Deep ML Expertise Required: Custom Vision is designed to be accessible for people with little to no experience in machine learning, making it a great option for users who want to add AI to their applications without writing complex code.
B) Azure Machine Learning is a powerful platform for training and deploying machine learning models but requires more in-depth knowledge of machine learning techniques, algorithms, and frameworks. It is better suited for advanced users and larger, more complex projects.
C) Azure Databricks is designed for large-scale data processing and machine learning workloads. It provides a collaborative environment for data scientists and engineers but is more suited for big data tasks than for training simple computer vision models.
D) Azure Cognitive Services is a suite of pre-built AI services for vision, language, and decision-making. While it offers image recognition and other computer vision services, it does not provide the same customizability or ease of use for training your own models as Custom Vision does.
Question 70:
Which Azure service would you use to create a chatbot that can understand natural language and provide relevant responses to user queries?
A) Azure Language Understanding (LUIS)
B) Azure Bot Services
C) Azure Cognitive Search
D) Azure Databricks
Answer: B) Azure Bot Services
Explanation:
Azure Bot Services is the most suitable service for creating a chatbot that can understand natural language and provide relevant responses to user queries. Azure Bot Services provides an integrated environment for developing, testing, and deploying conversational AI bots.
Key features of Azure Bot Services:
Easy Integration with LUIS: Bot Services can integrate with Azure Language Understanding (LUIS) to enable the bot to understand natural language, detect intents, and extract entities from user input.
Multiple Channel Support: Bots created with Azure Bot Services can be deployed across multiple channels, such as web apps, Microsoft Teams, Facebook Messenger, Slack, and more.
Conversation Management: Azure Bot Services allows you to manage conversations, maintain context, and handle user queries interactively.
Built-in Templates: It provides templates and pre-built code that make it easier to start building bots for different use cases, from simple Q&A bots to more complex scenarios.
A) Azure Language Understanding (LUIS) is a powerful tool for understanding user intents and extracting entities from text, but LUIS alone does not provide the full functionality to create a complete chatbot. LUIS is often used in conjunction with Azure Bot Services to enable natural language understanding in chatbots.
C) Azure Cognitive Search is a search service, not an AI chatbot platform. While it can be used to enhance search capabilities within a bot, it does not provide the core functionality needed for building a chatbot.
D) Azure Databricks is designed for large-scale data processing and machine learning, not for building conversational AI applications. It is better suited for data analytics and machine learning workflows.
Question 71:
Which Azure service should you use to automate the deployment and management of machine learning models at scale?
A) Azure Machine Learning
B) Azure Cognitive Services
C) Azure Databricks
D) Azure Kubernetes Service (AKS)
Answer: A) Azure Machine Learning
Explanation:
A) Azure Machine Learning is the correct service for automating the deployment and management of machine learning models at scale. Azure Machine Learning provides a comprehensive environment that allows you to build, train, and deploy machine learning models. It offers tools for model versioning, automated training, and model deployment to scalable endpoints, making it ideal for managing models in a production environment. Features like AutoML, pipelines, and security governance further simplify the process of automating workflows and ensuring smooth, automated operations.
B) Azure Cognitive Services is a suite of pre-built AI services that can be used for various tasks, such as speech recognition, language understanding, and computer vision. However, it is not specifically designed for the automation of machine learning models. Instead, Cognitive Services provides APIs for performing AI-related tasks using pre-built models rather than providing a full infrastructure for automating and managing custom models.
C) Azure Databricks is a big data analytics and machine learning platform built on Apache Spark. While it is powerful for data engineering and building machine learning models, it does not offer the same level of automation and management tools as Azure Machine Learning. Databricks is excellent for processing large datasets and collaborating on data science projects but lacks the same model deployment and scaling capabilities as Azure Machine Learning.
D) Azure Kubernetes Service (AKS) is a container orchestration service that is useful for deploying containerized machine learning models, but it does not provide the end-to-end automation tools necessary for managing the machine learning lifecycle. Azure ML integrates with AKS, but AKS alone does not provide the specialized tools for building, training, and deploying machine learning models at scale.
Question 72:
Which Azure service should you use to analyze and interpret human speech to extract valuable information such as sentiment or keywords?
A) Azure Speech Service
B) Azure Language Understanding (LUIS)
C) Azure Text Analytics
D) Azure Cognitive Search
Answer: A) Azure Speech Service
Explanation:
A) Azure Speech Service is the correct service for analyzing and interpreting human speech to extract valuable information such as sentiment or keywords. This service allows you to transcribe spoken language into text, making it easier to analyze and extract insights. With features like speech-to-text, speaker identification, and real-time keyword detection, Azure Speech Service is ideal for processing audio data and converting it into useful text for further analysis. It integrates seamlessly with other services like Text Analytics for sentiment analysis and entity recognition.
B) Azure Language Understanding (LUIS) is designed to process text, not audio. It focuses on interpreting written language and extracting user intents and entities from text input. LUIS is useful for creating applications that understand natural language, but it does not directly handle speech or provide capabilities for speech-to-text conversion, which Azure Speech Service excels at.
C) Azure Text Analytics provides tools for analyzing written text, such as sentiment analysis and key phrase extraction. However, it does not support the processing of audio or spoken language. Text Analytics is a great option for understanding text, but it cannot handle the speech-to-text conversion required for analyzing human speech directly.
D) Azure Cognitive Search is a service for indexing and searching large amounts of structured and unstructured data. While it is useful for text-based queries and searches, it does not offer speech recognition capabilities or tools for analyzing audio content like Azure Speech Service does.
Question 73:
Which Azure service provides a comprehensive set of APIs for understanding and processing text, including sentiment analysis, entity recognition, and key phrase extraction?
A) Azure Text Analytics
B) Azure Machine Learning
C) Azure Cognitive Search
D) Azure Cognitive Services
Answer: A) Azure Text Analytics
Explanation:
A) Azure Text Analytics is the correct service for understanding and processing text. It provides a set of APIs that can analyze text to extract sentiment, recognize entities, and identify key phrases. Text Analytics is a fully managed service that leverages natural language processing (NLP) to help users gain insights from unstructured text. Its core features include sentiment analysis, which classifies text as positive, neutral, or negative; entity recognition, which identifies important entities such as names of people, places, organizations, and dates; and key phrase extraction, which identifies significant phrases within text.
B) Azure Machine Learning is a broader platform for building, training, and deploying machine learning models. While it can be used for text analytics through custom models, it does not provide out-of-the-box APIs for sentiment analysis, entity recognition, and key phrase extraction like Text Analytics does. Azure ML requires additional configuration and custom model development for these tasks.
C) Azure Cognitive Search is primarily used for search and indexing text data, and while it offers some text analysis capabilities, it is not focused on NLP tasks such as sentiment analysis or entity recognition. Search is best for building powerful search applications but does not offer the advanced NLP capabilities found in Text Analytics.
D) Azure Cognitive Services is a suite of AI services, including Text Analytics, but it is a broader offering that includes a range of AI services, such as Speech, Vision, and Decision services. While Text Analytics is part of Cognitive Services, the correct answer is specifically Azure Text Analytics because it is focused solely on text processing and analysis.
Question 74:
Which Azure service would you use to perform advanced analytics and build machine learning models on big data, such as data stored in Azure Data Lake or Azure Blob Storage?
A) Azure Databricks
B) Azure Synapse Analytics
C) Azure Machine Learning
D) Azure Data Factory
Answer: A) Azure Databricks
Explanation:
A) Azure Databricks is the correct service for performing advanced analytics and building machine learning models on big data stored in Azure Data Lake or Azure Blob Storage. Azure Databricks is a unified data analytics platform that integrates with Apache Spark, which allows for distributed data processing. It provides an environment for data scientists and engineers to collaborate on large-scale data analytics and machine learning projects. With its powerful integration with big data storage solutions like Azure Data Lake and Blob Storage, Databricks enables fast processing and scalable analytics, making it ideal for big data environments.
B) Azure Synapse Analytics is an analytics service that brings together big data and data warehousing. It is great for integrating data from various sources and running analytical queries on large datasets. However, Synapse Analytics is more focused on data integration, querying, and visualization, rather than building and deploying machine learning models on big data, which is the focus of Databricks.
C) Azure Machine Learning is a comprehensive platform for building, training, and deploying machine learning models. While it is an excellent tool for working with machine learning workflows, Azure ML is not specifically built for big data analytics. Azure Databricks provides better scalability and tools for handling large datasets in a distributed computing environment.
D) Azure Data Factory is an ETL (extract, transform, load) service that helps orchestrate data workflows. It is useful for moving and transforming data but does not provide the advanced analytics or machine learning tools needed for building models on big data, which is why Databricks is the more suitable option for these tasks.
Question 75:
Which Azure service provides a platform for building, deploying, and managing conversational AI applications, such as chatbots?
A) Azure Bot Services
B) Azure Cognitive Search
C) Azure Language Understanding (LUIS)
D) Azure Machine Learning
Answer: A) Azure Bot Services
Explanation:
A) Azure Bot Services is the correct platform for building, deploying, and managing conversational AI applications, such as chatbots. It provides a full set of tools and services to create intelligent bots that can interact with users through various channels like websites, Microsoft Teams, Facebook Messenger, and more. Azure Bot Services integrates seamlessly with other Azure AI services, including Language Understanding (LUIS) and Speech Services, to create more sophisticated, context-aware conversational agents. It also offers templates and pre-built solutions to help accelerate bot development.
B) Azure Cognitive Search is a search service designed for indexing and searching data. While it can be used in conjunction with chatbots for retrieving information, it does not provide the necessary features for creating and managing conversational AI applications.
C) Azure Language Understanding (LUIS) is an NLP service that helps bots understand user intent and extract entities from text. While LUIS is crucial for enabling natural language understanding in bots, it does not provide the full platform for bot development and deployment. Azure Bot Services includes LUIS as part of its bot-building capabilities.
D) Azure Machine Learning is a powerful platform for building and deploying machine learning models but is not specifically designed for building conversational AI applications. While Azure ML could be used to build models that enhance bots, Azure Bot Services is the more appropriate platform for building and managing chatbots.
Question 76:
Which Azure service should you use to identify and track objects in images and video streams?
A) Azure Cognitive Services – Computer Vision
B) Azure Machine Learning
C) Azure Bot Services
D) Azure Synapse Analytics
Answer: A) Azure Cognitive Services – Computer Vision
Explanation:
A) Azure Cognitive Services – Computer Vision is the correct service for identifying and tracking objects in images and video streams. This service provides several APIs for analyzing images and videos, including object detection, facial recognition, and identifying landmarks. With object detection, you can locate and track objects within an image or video. It also supports reading text from images and generating descriptions based on the content of the images.
B) Azure Machine Learning is a comprehensive platform for building, training, and deploying machine learning models. While it can be used to train custom computer vision models for object detection, it requires more setup and effort compared to Azure Cognitive Services – Computer Vision, which provides ready-to-use, pre-trained models for a variety of vision tasks.
C) Azure Bot Services is focused on creating conversational AI applications like chatbots and does not handle image or video analysis. While bots may integrate with services like Computer Vision to respond to user queries, it is not directly relevant to object detection or image recognition.
D) Azure Synapse Analytics is an analytics service primarily used for big data and data warehousing. It can analyze large datasets and perform queries on data, but it does not provide specific capabilities for image or video processing like Computer Vision.
Question 77:
Which Azure service would you use to analyze text data to identify sentiment, key phrases, entities, and language?
A) Azure Cognitive Services – Text Analytics
B) Azure Cognitive Services – Speech
C) Azure Bot Services
D) Azure Synapse Analytics
Answer: A) Azure Cognitive Services – Text Analytics
Explanation:
A) Azure Cognitive Services – Text Analytics is the correct service for analyzing text data to identify sentiment, key phrases, entities, and language. Text Analytics provides several NLP capabilities, such as sentiment analysis, named entity recognition, key phrase extraction, and language detection. This service can be applied to various use cases, such as analyzing customer feedback, understanding social media content, or extracting insights from large amounts of text data.
B) Azure Cognitive Services – Speech focuses on converting speech to text, recognizing speech, and translating languages. It does not provide the same capabilities for text analysis as Text Analytics. Speech services are great for applications that involve spoken input but are not designed for text analysis tasks like sentiment analysis or entity recognition.
C) Azure Bot Services is used for developing conversational agents and chatbots, enabling them to interact with users. While bots can be integrated with Text Analytics for sentiment analysis, Bot Services itself does not provide text analysis features directly.
D) Azure Synapse Analytics is a cloud-based analytics service designed for data integration, analytics, and querying large datasets. While it can process text data in some cases, it is not specifically intended for the natural language processing tasks offered by Text Analytics.
Question 78:
Which Azure service allows you to create and deploy machine learning models in an automated and scalable way without needing extensive coding knowledge?
A) Azure Machine Learning
B) Azure Cognitive Services
C) Azure Databricks
D) Azure AutoML
Answer: D) Azure AutoML
Explanation:
D) Azure AutoML is the correct answer for creating and deploying machine learning models in an automated and scalable way without needing extensive coding knowledge. Azure AutoML is a feature within Azure Machine Learning that automatically selects the best model and configuration for a given dataset, making it easier for users who may not have deep data science expertise to build machine learning models. It automates the process of training models, selecting algorithms, and optimizing hyperparameters, allowing users to focus on understanding the results rather than on complex coding tasks.
A) Azure Machine Learning is a more comprehensive platform for building, training, and deploying machine learning models. While it provides powerful tools for data scientists and developers, it requires more hands-on coding and configuration than AutoML.
B) Azure Cognitive Services provides pre-built AI models for a variety of tasks such as vision, speech, and language processing. It is more focused on using pre-trained models than on building custom machine learning models. Cognitive Services is excellent for tasks like image recognition, language understanding, and speech recognition, but it doesn’t support the same level of model customization as Azure AutoML.
C) Azure Databricks is a collaborative environment for big data and machine learning. It is ideal for data engineering and building custom machine learning models on large datasets but requires coding knowledge, especially in Python or Scala. It is not designed for automated machine learning workflows like AutoML.
Question 79:
Which Azure service would you use to easily build, train, and deploy a deep learning model using pre-built templates and a graphical interface?
A) Azure Machine Learning Studio
B) Azure Databricks
C) Azure Cognitive Services
D) Azure Synapse Analytics
Answer: A) Azure Machine Learning Studio
Explanation:
A) Azure Machine Learning Studio is the correct service for easily building, training, and deploying a deep learning model using pre-built templates and a graphical interface. Machine Learning Studio offers a drag-and-drop interface that simplifies the model-building process. It allows users to design, train, and deploy machine learning models without writing extensive code. With pre-built templates, users can quickly build models for tasks like classification, regression, and clustering. This is ideal for those who prefer a low-code/no-code environment.
B) Azure Databricks is a powerful platform for big data processing and machine learning that supports deep learning frameworks like TensorFlow and PyTorch. However, it requires coding knowledge and is not as focused on providing a low-code interface as Machine Learning Studio. Databricks is more suited for advanced users and large-scale data processing tasks.
C) Azure Cognitive Services offers pre-built AI models for tasks like vision, speech, and language processing. While it simplifies access to AI models, it does not provide tools for building deep learning models from scratch like Machine Learning Studio. It is designed for using pre-trained models rather than training custom models.
D) Azure Synapse Analytics is primarily focused on data warehousing and analytics, rather than model training and deployment. While Synapse Analytics is powerful for big data analytics, it does not provide the tools required to build deep learning models like Azure Machine Learning Studio does.
Question 80:
Which Azure service would you use to store large amounts of unstructured data, such as images, audio, and video files?
A) Azure Blob Storage
B) Azure Data Lake Storage
C) Azure SQL Database
D) Azure Cosmos DB
Answer: A) Azure Blob Storage
Explanation:
A) Azure Blob Storage is the ideal service for storing large amounts of unstructured data, such as images, audio, and video files. Blob Storage is designed to handle unstructured data, which includes data that doesn’t follow a fixed schema, like media files, logs, and backups. It offers high scalability and can store massive amounts of data with low-latency access, making it a perfect choice for media storage. Additionally, Blob Storage supports tiered storage, allowing you to optimize costs based on access frequency.
B) Azure Data Lake Storage is another storage option for big data workloads. While it is optimized for storing structured and unstructured data, Data Lake Storage is more suitable for analytics and processing large volumes of data with tools like Azure Databricks and Azure Synapse Analytics. Blob Storage is more commonly used for simple, high-volume unstructured storage, like media files.
C) Azure SQL Database is a relational database service designed for structured data and does not support storing unstructured data like images or videos efficiently. While it can store binary data (like images) in columns, it is not designed to handle large files such as audio or video in the way Blob Storage does.
D) Azure Cosmos DB is a globally distributed, multi-model database that can store unstructured data. However, it is more suitable for scenarios requiring low-latency access to highly transactional data rather than for storing large media files. Cosmos DB excels in scenarios like IoT, real-time applications, and globally distributed systems but is not optimized for large file storage like Blob Storage.
