HOTSPOT (Drag and Drop is not supported)You have a large dataset that contains motor vehicle sales data.You need to train an automated machine learning (automated ML) model to predict vehicle sale values based on the type of vehicle.Which task should you select? To answer, select the appropriate task in the answer area.Note: Each correct selection is worth one point.Hot Area:
Answer(s): A
Box: RegressionFor predictions we use a regression model.Note:Azure Automated Machine Learning (Automated ML) regression models are used to predict continuous numerical values, such as house prices or stock prices, by automatically exploring various algorithms and hyperparameters. This process is fully automated, allowing data scientists to build and train models efficiently without needing to manually configure the algorithms or tune hyperparameters.Box 2: View additional configuration settingsIn Azure Automated Machine Learning (AutoML), "View additional configuration settings" allows you to customize the training job beyond the default settings, giving you more control over the model development process. This includes options like specifying the primary metric for evaluation, enabling ensemble learning, controlling which models are used, and configuring data featurization and explainability settings.Incorrect:* View featurization settingsIn Azure Automated Machine Learning (AutoML), you can view and customize featurization settings within the Azure Machine Learning studio to control how data is prepared for training. This includes enabling or disabling automatic featurization, modifying feature types, and handling missing values.
https://learn.microsoft.com/en-us/azure/machine-learning/concept-automated-ml?view=azureml-api-2
Which Azure Machine Learning capability should you use to quickly build and deploy a predictive model without extensive coding?
Answer(s): D
To easily build and deploy a predictive model using Azure Machine Learning, start by selecting the appropriate tools for your task, such as Azure Machine Learning Studio or AutoML, and follow a streamlined process of data preparation, model training, and deployment.AutoML stands for Automated Machine Learning. It's a process that automates the various tasks involved in building machine learning models, including data preparation, feature selection, model training, hyperparameter tuning, and model evaluation, according to IBM. This automation makes machine learning more accessible to a wider range of users, including those without extensive machine learning expertise.
HOTSPOT (Drag and Drop is not supported)Select the answer that correctly completes the sentence.Hot Area:
Box: categorical.When building a classification model, labels must be________.For an Azure classification model, labels must be meaningful text strings that clearly identify the class for each data point. The specific values depend on the classification type, such as "Romance" or "Comedy" for movie scripts in a single-label scenario, or "Comedy" and "Romance" simultaneously in a multi-label setup. Maintaining consistency and avoiding ambiguity in these labels is crucial for accurate model training and performance.Key requirements for labels:Meaningful and Descriptive:Each label should represent a distinct category that the model is intended to recognize.Consistent:All data points belonging to the same class must have the exact same label.Clear Separation:For single-label classification, labels should be clearly distinguishable from each other to avoid confusion.Specific to the Problem:The labels you define should directly align with the classification task you're trying to solve.
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-label-data
HOTSPOT (Drag and Drop is not supported)For each of the following statements, select Yes if the statement is true. Otherwise, select No.Note: Each correct selection is worth one point.Hot Area:
Box 1: YesYes - Generative AI enables software applications to generate new content, such as language dialogs and images.Generative AI, sometimes called gen AI, is artificial intelligence (AI) that can create original content such as text, images, video, audio or software code in response to a user's prompt or request.Box 2: YesYes - The difference between a large language model (LLM) and a small language model (SLM) is the number of variables in the model.The primary difference between a Large Language Model (LLM) and a Small Language Model (SLM) is the number of variables (or parameters) in their neural networks, with LLMs having billions to trillions of parameters and SLMs having millions to a few billion, allowing SLMs to be more efficient for domain-specific tasks and edge computing.Box 3: NoNo - Generative AI is a type of supervised learning.Generative AI is not solely a type of supervised learning; it is a broad AI field that utilizes various Machine Learning (ML) techniques, including unsupervised learning, supervised learning, and reinforcement learning, often in combination, to learn patterns from existing data and generate new content. While supervised learning is used in some aspects, particularly for fine-tuning models, unsupervised learning is often foundational for the initial training of generative models.Traditional AI, also known as narrow or weak AI, is rules-based and best at performing predefined tasks, such as automating workflows or making decisions based on fixed algorithms. It's usually trained using supervised learning techniques. Generative AI, also known as creative or strong AI, generates unique output then fine- tunes it based on human guidance and correction. It's trained using unsupervised learning techniques.
https://www.ibm.com/think/topics/generative-ai https://www.ibm.com/think/topics/small-language-models https://www.microsoft.com/en-sg/ai/ai-101/generative-ai-vs-other-types-of-ai
Box: Orchestration workflow______________ enables you to connect conversational language understanding (CLU) questions answering projects, and Language Understanding applications.Orchestration is the feature that connects CLU with question answering and Language Understanding applications. Orchestration acts as a router, directing user input to the correct project (like CLU or Question Answering) based on the predicted intent, while key phrase extraction is a tool for identifying main topics within a text.Incorrect:* Key phrase extractionKey phrase extraction is a separate process.It identifies and extracts the main topics or concepts from a block of text.
https://azure.microsoft.com/en-au/products/ai-services/conversational-language-understanding
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