You use the Two-Class Neural Network module in Azure Machine Learning Studio to build a binary classification model. You use the Tune ModelHyperparameters module to tune accuracy for the model.You need to configure the Tune Model Hyperparameters module.Which two values should you use? Each correct answer presents part of the solution.NOTE: Each correct selection is worth one point.
Answer(s): B,D
D: For Number of learning iterations, specify the maximum number of times the algorithm should process the training cases.E: For Hidden layer specification, select the type of network architecture to create.Between the input and output layers you can insert multiple hidden layers. Most predictive tasks can be accomplished easily with only one or a few hidden layers.
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/two-class-neural-network
HOTSPOT (Drag and Drop is not supported)You are running a training experiment on remote compute in Azure Machine Learning.The experiment is configured to use a conda environment that includes the mlflow and azureml-contrib-run packages.You must use MLflow as the logging package for tracking metrics generated in the experiment.You need to complete the script for the 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: import mlflow Import the mlflow and Workspace classes to access MLflow's tracking URI and configure your workspace.Box 2: mlflow.start_run()Set the MLflow experiment name with set_experiment() and start your training run with start_run().Box 3: mlflow.log_metric(' ..')Use log_metric() to activate the MLflow logging API and begin logging your training run metrics.Box 4: mlflow.end_run()Close the run:run.endRun()
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow
You create a binary classification model by using Azure Machine Learning Studio.You must tune hyperparameters by performing a parameter sweep of the model. The parameter sweep must meet the following requirements:-iterate all possible combinations of hyperparameters-minimize computing resources required to perform the sweepYou need to perform a parameter sweep of the model.Which parameter sweep mode should you use?
Answer(s): D
Maximum number of runs on random grid: This option also controls the number of iterations over a random sampling of parameter values, but the values are not generated randomly from the specified range; instead, a matrix is created of all possible combinations of parameter values and a random sampling is taken over the matrix. This method is more efficient and less prone to regional oversampling or undersampling.If you are training a model that supports an integrated parameter sweep, you can also set a range of seed values to use and iterate over the random seeds as well. This is optional, but can be useful for avoiding bias introduced by seed selection.Incorrect Answers:B: If you are building a clustering model, use Sweep Clustering to automatically determine the optimum number of clusters and other parameters.C: Entire grid: When you select this option, the module loops over a grid predefined by the system, to try different combinations and identify the best learner. This option is useful for cases where you don't know what the best parameter settings might be and want to try all possible combination of values.
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/tune-model-hyperparameters
You are building a recurrent neural network to perform a binary classification.You review the training loss, validation loss, training accuracy, and validation accuracy for each training epoch.You need to analyze model performance.You need to identify whether the classification model is overfitted.Which of the following is correct?
Answer(s): B
An overfit model is one where performance on the train set is good and continues to improve, whereas performance on the validation set improves to a point and then begins to degrade.
https://machinelearningmastery.com/diagnose-overfitting-underfitting-lstm-models/
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 have a Python script named train.py in a local folder named scripts. The script trains a regression model by using scikit-learn. The script includes code to load a training data file which is also located in the scripts folder.You must run the script as an Azure ML experiment on a compute cluster named aml-compute.You need to configure the run to ensure that the environment includes the required packages for model training. You have instantiated a variable named aml- compute that references the target compute cluster.Solution: Run the following code:Does the solution meet the goal?
There is a missing line: conda_packages=['scikit-learn'], which is needed.Correct example:sk_est = Estimator(source_directory='./my-sklearn-proj',script_params=script_params,compute_target=compute_target,entry_script='train.py',conda_packages=['scikit-learn'])Note:The Estimator class represents a generic estimator to train data using any supplied framework.This class is designed for use with machine learning frameworks that do not already have an Azure Machine Learning pre-configured estimator. Pre-configured estimators exist for Chainer, PyTorch, TensorFlow, and SKLearn.Example:from azureml.train.estimator import Estimatorscript_params = {# to mount files referenced by mnist dataset'--data-folder': ds.as_named_input('mnist').as_mount(),'--regularization': 0.8}
https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.estimator.estimator
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