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.

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You are creating a new experiment in Azure Learning learning Studio.

One class has a much smaller number of observations than the other classes in the training

You need to select an appropriate data sampling strategy to compensate for the class imbalance.

Solution: You use the Synthetic Minority Oversampling Technique (SMOTE) sampling mode.

Does the solution meet the goal?

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You create an Azure Machine Learning compute resource to train models.

The compute resource is configured as follows:

✑ Minimum nodes: 2

✑ Maximum nodes: 4

You must decrease the minimum number of nodes and increase the maximum number of nodes to the following values:

✑ Minimum nodes: 0

✑ Maximum nodes: 8

You need to reconfigure the compute resource.

What are three possible ways to achieve this goal? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

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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 train a classification model by using a logistic regression algorithm.

You must be able to explain the model’s predictions by calculating the importance of each feature, both as an overall global relative importance value and as a measure of local importance for a specific set of predictions.

You need to create an explainer that you can use to retrieve the required global and local feature importance values.

Solution: Create a TabularExplainer.

Does the solution meet the goal?

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You have an Azure Machine Learning (ML) model deployed to an online endpoint.

You need to review container logs from the endpoint by using Azure Ml Python SDK v2. The logs must include the console log from the inference server with print/log statements from the models scoring script.

What should you do first?

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You create a multi-class image classification deep learning model that uses a set of labeled images. You create a script file named train.py that uses the PyTorch 1.3 framework to train the model.

You must run the script by using an estimator. The code must not require any additional Python libraries to be installed in the environment for the estimator. The time required for model training must be minimized.

You need to define the estimator that will be used to run the script.

Which estimator type should you use?

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You create a multi-class image classification deep learning model that uses a set of labeled images. You create a script file named train.py that uses the PyTorch 1.3 framework to train the model.

You must run the script by using an estimator. The code must not require any additional Python libraries to be installed in the environment for the estimator. The time required for model training must be minimized.

You need to define the estimator that will be used to run the script.

Which estimator type should you use?

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DRAG DROP

You have an Azure Machine Learning workspace that contains a CPU-based compute cluster and an Azure Kubernetes Services (AKS) inference cluster. You create a tabular dataset containing data that you plan to use to create a classification model.

You need to use the Azure Machine Learning designer to create a web service through which client applications can consume the classification model by submitting new data and getting an immediate prediction as a response.

Which three 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.

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Correct Answer:

Explanation:

Step 1: Create and start a Compute Instance

To train and deploy models using Azure Machine Learning designer, you need compute on which to run the training process, test the model, and host the model in a deployed service.

There are four kinds of compute resource you can create:

Compute Instances: Development workstations that data scientists can use to work with data and models.

Compute Clusters: Scalable clusters of virtual machines for on-demand processing of experiment

code.

Inference Clusters: Deployment targets for predictive services that use your trained models.

Attached Compute: Links to existing Azure compute resources, such as Virtual Machines or Azure Databricks clusters.

Step 2: Create and run a training pipeline..

After you’ve used data transformations to prepare the data, you can use it to train a machine learning model. Create and run a training pipeline

Step 3: Create and run a real-time inference pipeline

After creating and running a pipeline to train the model, you need a second pipeline that performs the same data transformations for new data, and then uses the trained model to inference (in other words, predict) label values based on its features. This pipeline will form the basis for a predictive service that you can publish for applications to use.

Reference: https://docs.microsoft.com/en-us/learn/modules/create-classification-model-azure-machine-learning-designer/

HOTSPOT

You have a multi-class image classification deep learning model that uses a set of labeled photographs.

You create the following code to select hyperparameter values when training the model.

For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

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Correct Answer:

Explanation:

Box 1: Yes

Hyperparameters are adjustable parameters you choose to train a model that govern the training process itself. Azure Machine Learning allows you to automate hyperparameter exploration in an efficient manner, saving you significant time and resources. You specify the range of hyperparameter values and a maximum number of training runs. The system then automatically launches multiple simultaneous runs with different parameter configurations and finds the configuration that results in the best performance, measured by the metric you choose. Poorly performing training runs are automatically early terminated, reducing wastage of compute resources. These resources are instead used to explore other hyperparameter configurations.

Box 2: Yes

uniform (low, high) – Returns a value uniformly distributed between low and high

Box 3: No

Bayesian sampling does not currently support any early termination policy.

Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters

You are building recurrent neural network to perform a binary classification.

The training loss, validation loss, training accuracy, and validation accuracy of each training epoch has been provided. You need to identify whether the classification model is over fitted.

Which of the following is correct?

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HOTSPOT

You are evaluating a Python NumPy array that contains six data points defined as follows:

data = [10, 20, 30, 40, 50, 60]

You must generate the following output by using the k-fold algorithm implantation in the Python Scikit-learn machine learning library:

train: [10 40 50 60], test: [20 30]

train: [20 30 40 60], test: [10 50]

train: [10 20 30 50], test: [40 60]

You need to implement a cross-validation to generate the output.

How should you complete the code segment? To answer, select the appropriate code segment in the dialog box in the answer area. NOTE: Each correct selection is worth one point.

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Correct Answer:

Explanation:

Box 1: k-fold

Box 2: 3

K-Folds cross-validator provides train/test indices to split data in train/test sets. Split dataset into k consecutive folds (without shuffling by default).

The parameter n_splits ( int, default=3) is the number of folds. Must be at least 2.

Box 3: data

Example: Example:

>>> 

>>> from sklearn.model_selection import KFold

>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])

>>> y = np.array([1, 2, 3, 4])

>>> kf = KFold(n_splits=2)

>>> kf.get_n_splits(X)

2

>>> print(kf)

KFold(n_splits=2, random_state=None, shuffle=False)

>>> for train_index, test_index in kf.split(X):

… print("TRAIN:", train_index, "TEST:", test_index)

… X_train, X_test = X[train_index], X[test_index]

… y_train, y_test = y[train_index], y[test_index]

TRAIN: [2 3] TEST: [0 1]

TRAIN: [0 1] TEST: [2 3]

Reference: https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html