You create a machine learning model by using the Azure Machine Learning designer. You publish the model as a real-time service on an Azure KubernetesService (AKS) inference compute cluster. You make no changes to the deployed endpoint configuration.You need to provide application developers with the information they need to consume the endpoint.Which two values should you provide to application developers? Each correct answer presents part of the solution.NOTE: Each correct selection is worth one point.
Answer(s): C,E
Deploying an Azure Machine Learning model as a web service creates a REST API endpoint. You can send data to this endpoint and receive the prediction returned by the model.You create a web service when you deploy a model to your local environment, Azure Container Instances, Azure Kubernetes Service, or field-programmable gate arrays (FPGA). You retrieve the URI used to access the web service by using the Azure Machine Learning SDK. If authentication is enabled, you can also use theSDK to get the authentication keys or tokens.Example:# URL for the web servicescoring_uri = '<your web service URI>'# If the service is authenticated, set the key or tokenkey = '<your key or token>'
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-consume-web-service
HOTSPOT (Drag and Drop is not supported)You collect data from a nearby weather station. You have a pandas dataframe named weather_df that includes the following data:The data is collected every 12 hours: noon and midnight.You plan to use automated machine learning to create a time-series model that predicts temperature over the next seven days. For the initial round of training, you want to train a maximum of 50 different models.You must use the Azure Machine Learning SDK to run an automated machine learning experiment to train these models.You need to configure the automated machine learning run.How should you complete the AutoMLConfig definition? To answer, select the appropriate options in the answer area.NOTE: Each correct selection is worth one point.Hot Area:
Answer(s): A
Box 1: forcasting Task: The type of task to run. Values can be 'classification', 'regression', or 'forecasting' depending on the type of automated ML problem to solve.Box 2: temperature The training data to be used within the experiment. It should contain both training features and a label column (optionally a sample weights column).Box 3: observation_time time_column_name: The name of the time column. This parameter is required when forecasting to specify the datetime column in the input data used for building the time series and inferring its frequency. This setting is being deprecated. Please use forecasting_parameters instead.Box 4: 7 "predicts temperature over the next seven days"max_horizon: The desired maximum forecast horizon in units of time-series frequency. The default value is 1.Units are based on the time interval of your training data, e.g., monthly, weekly that the forecaster should predict out. When task type is forecasting, this parameter is required.Box 5: 50 "For the initial round of training, you want to train a maximum of 50 different models."Iterations: The total number of different algorithm and parameter combinations to test during an automated ML experiment.
https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.You create a model to forecast weather conditions based on historical data.You need to create a pipeline that runs a processing script to load data from a datastore and pass the processed data to a machine learning model training script.Solution: Run the following code:Does the solution meet the goal?
The two steps are present: process_step and train_stepData_input correctly references the data in the data store.Note:Data used in pipeline can be produced by one step and consumed in another step by providing a PipelineData object as an output of one step and an input of one or more subsequent steps.PipelineData objects are also used when constructing Pipelines to describe step dependencies. To specify that a step requires the output of another step as input, use a PipelineData object in the constructor of both steps.For example, the pipeline train step depends on the process_step_output output of the pipeline process step: from azureml.pipeline.core import Pipeline, PipelineData from azureml.pipeline.steps import PythonScriptStep datastore = ws.get_default_datastore() process_step_output = PipelineData("processed_data", datastore=datastore) process_step = PythonScriptStep(script_name="process.py", arguments=["--data_for_train", process_step_output], outputs=[process_step_output], compute_target=aml_compute, source_directory=process_directory) train_step = PythonScriptStep(script_name="train.py", arguments=["--data_for_train", process_step_output], inputs=[process_step_output], compute_target=aml_compute, source_directory=train_directory) pipeline = Pipeline(workspace=ws, steps=[process_step, train_step])
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?view=azure-ml-py
You run an experiment that uses an AutoMLConfig class to define an automated machine learning task with a maximum of ten model training iterations. The task will attempt to find the best performing model based on a metric named accuracy.You submit the experiment with the following code:You need to create Python code that returns the best model that is generated by the automated machine learning task.Which code segment should you use?
Answer(s): D
The get_output method returns the best run and the fitted model.
https://notebooks.azure.com/azureml/projects/azureml-getting-started/html/how-to-use-azureml/automated-machine-learning/classification/auto-ml- classification.ipynb
You plan to use the Hyperdrive feature of Azure Machine Learning to determine the optimal hyperparameter values when training a model.You must use Hyperdrive to try combinations of the following hyperparameter values. You must not apply an early termination policy.-learning_rate: any value between 0.001 and 0.1-batch_size: 16, 32, or 64You need to configure the sampling method for the Hyperdrive experiment.Which two sampling methods can you use? Each correct answer is a complete solution.NOTE: Each correct selection is worth one point.
Answer(s): C,D
C: Bayesian sampling is based on the Bayesian optimization algorithm and makes intelligent choices on the hyperparameter values to sample next. It picks the sample based on how the previous samples performed, such that the new sample improves the reported primary metric.Bayesian sampling does not support any early termination policyExample:from azureml.train.hyperdrive import BayesianParameterSampling from azureml.train.hyperdrive import uniform, choice param_sampling = BayesianParameterSampling( {"learning_rate": uniform(0.05, 0.1),"batch_size": choice(16, 32, 64, 128)})D: In random sampling, hyperparameter values are randomly selected from the defined search space. Random sampling allows the search space to include both discrete and continuous hyperparameters.Incorrect Answers:B: Grid sampling can be used if your hyperparameter space can be defined as a choice among discrete values and if you have sufficient budget to exhaustively search over all values in the defined search space. Additionally, one can use automated early termination of poorly performing runs, which reduces wastage of resources.Example, the following space has a total of six samples:from azureml.train.hyperdrive import GridParameterSamplingfrom azureml.train.hyperdrive import choiceparam_sampling = GridParameterSampling( {"num_hidden_layers": choice(1, 2, 3),"batch_size": choice(16, 32)})
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters
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