DP-100 Exam Dumps - Microsoft Azure Questions and Answers

If you have an existing Conda environment on your local computer, then you can use the service to create an environment object. By using this strategy, you can reuse your local interactive environment on remote runs.

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.

Box 1: dataFrame

Syntax: pandas.melt(frame, id_vars=None, value_vars=None, var_name=None, value_name='value', col_level=None)[source]

Where frame is a DataFrame

Box 2: shop

Paramter id_vars id_vars : tuple, list, or ndarray, optional

Column(s) to use as identifier variables.

Box 3: ['2017','2018']

value_vars : tuple, list, or ndarray, optional

Column(s) to unpivot. If not specified, uses all columns that are not set as id_vars.

Example:

df = pd.DataFrame({'A': {0: 'a', 1: 'b', 2: 'c'},

'B': {0: 1, 1: 3, 2: 5},

'C': {0: 2, 1: 4, 2: 6}})

pd.melt(df, id_vars=['A'], value_vars=['B', 'C'])

A variable value

0 a B 1

1 b B 3

2 c B 5

3 a C 2

4 b C 4

5 c C 6

References:

https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.melt.html

Step 1: Create an Azure DevOps project

Step 2: Create a release pipeline

Sign in to your Azure DevOps organization and navigate to your project.

Go to Pipelines, and then select New pipeline.

Step 3: Install the Machine Learning extension for Azure Pipelines

You must install and configure the Azure CLI and ML extension.

Step 4: Create a service connection

How to set up your service connection

Select AzureMLWorkspace for the scope level, then fill in the following subsequent parameters.

Note: How to enable model triggering in a release pipeline

Go to your release pipeline and add a new artifact. Click on AzureML Model artifact then select the appropriate AzureML service connection and select from the available models in your workspace.

Enable the deployment trigger on your model artifact as shown here. Every time a new version of that model is registered, a release pipeline will be triggered.

Instead use the Synthetic Minority Oversampling Technique (SMOTE) sampling mode.

Note: SMOTE is used to increase the number of underepresented cases in a dataset used for machine learning. SMOTE is a better way of increasing the number of rare cases than simply duplicating existing cases.

Box 1: Mutual Information.

The mutual information score is particularly useful in feature selection because it maximizes the mutual information between the joint distribution and target variables in datasets with many dimensions.

Box 2: MedianValue

MedianValue is the feature column, , it is the predictor of the dataset.

Scenario: The MedianValue and AvgRoomsinHouse columns both hold data in numeric format. You need to select a feature selection algorithm to analyze the relationship between the two columns in more detail.

References:

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/filter-based-feature-selection

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()

Box 1: Accuracy

Scenario: You want to configure hyperparameters in the model learning process to speed the learning phase by using hyperparameters. In addition, this configuration should cancel the lowest performing runs at each evaluation interval, thereby directing effort and resources towards models that are more likely to be successful.

Box 2: R-Squared

Box 1: Replace using MICE

Replace using MICE: For each missing value, this option assigns a new value, which is calculated by using a method described in the statistical literature as "Multivariate Imputation using Chained Equations" or "Multiple Imputation by Chained Equations". With a multiple imputation method, each variable with missing data is modeled conditionally using the other variables in the data before filling in the missing values.

Scenario: The AccessibilityToHighway column in both datasets contains missing values. The missing data must be replaced with new data so that it is modeled conditionally using the other variables in the data before filling in the missing values.

Box 2: Propagate

Cols with all missing values indicate if columns of all missing values should be preserved in the output.

References:

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data