Microsoft Updated DP-100 Exam Questions and Answers by taran

Step 1: Select a model feature to be evaluated.

Step 2: Select a binary classification or regression model.

Register your models within Azure Machine Learning. For convenience, store the results in a dictionary, which maps the id of the registered model (a string in name:version format) to the predictor itself.

Example:

model_dict = {}

lr_reg_id = register_model("fairness_logistic_regression", lr_predictor)

model_dict[lr_reg_id] = lr_predictor

svm_reg_id = register_model("fairness_svm", svm_predictor)

model_dict[svm_reg_id] = svm_predictor

Step 3: Select a metric to be measured

Precompute fairness metrics.

Create a dashboard dictionary using Fairlearn's metrics package.

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]

References:

https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KF old.html

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.

Key-based authentication.

Web services deployed on AKS have key-based auth enabled by default. ACI-deployed services have key-based auth disabled by default, but you can enable it by setting auth_enabled = TRUE when creating the ACI web service. The following is an example of creating an ACI deployment configuration with key-based auth enabled.

deployment_config <- aci_webservice_deployment_config(cpu_cores = 1,

memory_gb = 1,

auth_enabled = TRUE)