Microsoft DP-100 Practice Test - Questions Answers, Page 17

The Median Stopping policy computes running averages across all runs and cancels runs whose best performance is worse than the median of the running averages.If no policy is specified, the hyperparameter tuning service will let all training runs execute to completion.

Reference:https://docs.microsoft.com/en-us/python/api/azureml-train- core/azureml.train.hyperdrive.medianstoppingpolicy

https://docs.microsoft.com/en-us/python/api/azureml-train- core/azureml.train.hyperdrive.truncationselectionpolicy

https://docs.microsoft.com/en-us/python/api/azureml-train- core/azureml.train.hyperdrive.banditpolicy

Use get_details_with_logs() to fetch the run details and logs created by the run.

You can monitor Azure Machine Learning runs and view their logs with the Azure Machine Learning studio.

Incorrect Answers:

A: You can view the metrics of a trained model using run.get_metrics(). E: get_output() gets the output of the step as PipelineData.

Reference:

https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.steprun https://docs.microsoft.com/en-us/azure/machine-learning/how-to-monitor-view-training-logs

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.

Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/two-class-neural-network

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.

Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/tune-model-hyperparameters

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.

Reference:

https://machinelearningmastery.com/diagnose-overfitting-underfitting-lstm-models/

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 Estimator

script_params = {

# to mount files referenced by mnist dataset

'--data-folder': ds.as_named_input('mnist').as_mount(),

'--regularization': 0.8

}

Reference:

https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.estimator.estimator

AD: The algorithm terminates when the centroids stabilize or when a specified number of iterations are completed.

C: A measure of how well the centroids represent the members of their clusters is the residual sum of squares or RSS, the squared distance of each vector from its centroid summed over all vectors. RSS is the objective function and our goal is to minimize it.

Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/k-means-clustering https://nlp.stanford.edu/IR-book/html/htmledition/k-means-1.html

To translate a corpus of English text to French, we need to build a recurrent neural network (RNN).

Note: RNNs are designed to take sequences of text as inputs or return sequences of text as outputs, or both. They're called recurrent because the network's hidden layers have a loop in which the output and cell state from each time step become inputs at the next time step. This recurrence serves as a form of memory. It allows contextual information to flow through the network so that relevant outputs from previous time steps can be applied to network operations at the current time step.

Reference: https://towardsdatascience.com/language-translation-with-rnns-d84d43b40571

The evaluation metrics available for binary classification models are: Accuracy, Precision, Recall, F1 Score, and AUC.

Note: A very natural question-is: 'Out of the individuals whom the model, how many were classified correctly (TP)?'

This question-can be answered by looking at the Precision of the model, which is the proportion of positives that are classified correctly.

Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance