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:
Answer(s): A
Box 1: YesAutomated machine learning, also referred to as automated ML or AutoML, is the process of automating the time consuming, iterative tasks of machine learning model development. It allows data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity all while sustaining model quality.Box 2: NoBox 3: YesDuring training, Azure Machine Learning creates a number of pipelines in parallel that try different algorithms and parameters for you. The service iterates through ML algorithms paired with feature selections, where each iteration produces a model with a training score. The higher the score, the better the model is considered to "fit" your data. It will stop once it hits the exit criteria defined in the experiment.Box 4: NoApply automated ML when you want Azure Machine Learning to train and tune a model for you using the target metric you specify.The label is the column you want to predict.
https://azure.microsoft.com/en-us/services/machine-learning/automatedml/#features
Box 1: YesIn machine learning, if you have labeled data, that means your data is marked up, or annotated, to show the target, which is the answer you want your machine learning model to predict. In general, data labeling can refer to tasks that include data tagging, annotation, classification, moderation, transcription, or processing.Box 2: NoBox 3: NoAccuracy is simply the proportion of correctly classified instances. It is usually the first metric you look at when evaluating a classifier. However, when the test data is unbalanced (where most of the instances belong to one of the classes), or you are more interested in the performance on either one of the classes, accuracy doesn't really capture the effectiveness of a classifier.
https://www.cloudfactory.com/data-labeling-guide https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance
You use Azure Machine Learning designer to publish an inference pipeline.Which two parameters should you use to access the web service? Each correct answer presents part of the solution.Note: Each correct selection is worth one point.
Answer(s): C,D
You can consume a published pipeline in the Published pipelines page. Select a published pipeline and find the REST endpoint of it.To consume the pipeline, you need:The REST endpoint for your serviceThe Primary Key for your service
https://docs.microsoft.com/en-in/learn/modules/create-regression-model-azure-machine-learning-designer/ deploy-service
HOTSPOT (Drag and Drop is not supported)To complete the sentence, select the appropriate option in the answer area.Hot Area:
To perform real-time inferencing, you must deploy a pipeline as a real-time endpoint. Real-time endpoints must be deployed to an Azure Kubernetes Service cluster.
https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer#deploy
HOTSPOT (Drag and Drop is not supported)Select the answer that correctly completes the sentence.Hot Area:
In the most basic sense, regression refers to prediction of a numeric target.Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable.You use this module to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions.Incorrect Answers:Classification is a machine learning method that uses data to determine the category, type, or class of an item or row of data.Clustering, in machine learning, is a method of grouping data points into similar clusters. It is also called segmentation.Over the years, many clustering algorithms have been developed. Almost all clustering algorithms use the features of individual items to find similar items. For example, you might apply clustering to find similar people by demographics. You might use clustering with text analysis to group sentences with similar topics or sentiment.
https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/linear-regression https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/machine-learning-initialize- model-clustering
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