HOTSPOT (Drag and Drop is not supported)You are running Python code interactively in a Conda environment. The environment includes all required Azure Machine Learning SDK and MLflow packages.You must use MLflow to log metrics in an Azure Machine Learning experiment named mlflow-experiment.How should you complete the code? 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: mlflow.set_tracking_uri(ws.get_mlflow_tracking_uri())In the following code, the get_mlflow_tracking_uri() method assigns a unique tracking URI address to the workspace, ws, and set_tracking_uri() points the MLflow tracking URI to that address. mlflow.set_tracking_uri(ws.get_mlflow_tracking_uri())Box 2: mlflow.set_experiment(experiment_name)Set the MLflow experiment name with set_experiment() and start your training run with start_run().Box 3: mlflow.start_run()Box 4: mlflow.log_metric Then use log_metric() to activate the MLflow logging API and begin logging your training run metrics.
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow
DRAG DROP (Drag and Drop is not supported)You are creating a machine learning model that can predict the species of a penguin from its measurements. You have a file that contains measurements for three species of penguin in comma-delimited format.The model must be optimized for area under the received operating characteristic curve performance metric, averaged for each class.You need to use the Automated Machine Learning user interface in Azure Machine Learning studio to run an experiment and find the best performing model.Which five actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.Select and Place:
Step 1:Create and select a new dataset by uploading he command-delimited file of penguin data.Step 2: Select the Classification task typeStep 3: Set the Primary metric configuration setting to Accuracy.The available metrics you can select is determined by the task type you choose.Primary metrics for classification scenarios:Post thresholded metrics, like accuracy, average_precision_score_weighted, norm_macro_recall, and precision_score_weighted may not optimize as well for datasets which are very small, have very large class skew (class imbalance), or when the expected metric value is very close to 0.0 or 1.0. In those cases,AUC_weighted can be a better choice for the primary metric.Step 4: Configure the automated machine learning run by selecting the experiment name, target column, and compute targetStep 5: Run the automated machine learning experiment and review the results.
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-configure-auto-train
HOTSPOT (Drag and Drop is not supported)You are tuning a hyperparameter for an algorithm. The following table shows a data set with different hyperparameter, training error, and validation errors.Use the drop-down menus to select the answer choice that answers each question based on the information presented in the graphic.Hot Area:
Box 1: 4 Choose the one which has lower training and validation error and also the closest match.Minimize variance (difference between validation error and train error).Box 2: 5 Minimize variance (difference between validation error and train error).
https://medium.com/comet-ml/organizing-machine-learning-projects-project-management-guidelines-2d2b85651bbd
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?
Answer(s): B
The two steps are present: process_step and train_stepThe training data input is not setup correctly.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
train_step is missing.
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