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?
Answer:
B
The scikit-learn estimator provides a simple way of launching a scikit-learn training job on a compute target. It is implemented through the SKLearn class, which can be used to support single-node CPU training.
Example:
from azureml.train.sklearn import SKLearn
}
estimator = SKLearn(source_directory=project_folder,
compute_target=compute_target,
entry_script='train_iris.py'
)
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-scikit-learn
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?
Answer:
B
The scikit-learn estimator provides a simple way of launching a scikit-learn training job on a compute target. It is implemented through the SKLearn class, which can be used to support single-node CPU training.
Example:
from azureml.train.sklearn import SKLearn
}
estimator = SKLearn(source_directory=project_folder,
compute_target=compute_target,
entry_script='train_iris.py'
)
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-scikit-learn
DRAG DROP -
You create machine learning models by using Azure Machine Learning.
You plan to train and score models by using a variety of compute contexts. You also plan to create a new compute resource in Azure Machine Learning studio.
You need to select the appropriate compute types.
Which compute types should you select? To answer, drag the appropriate compute types to the correct requirements. Each compute type may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Select and Place:
Answer:
Box 1: Compute cluster -
Create a single or multi node compute cluster for your training, batch inferencing or reinforcement learning workloads.
Box 2: Inference cluster -
Box 3: Attached compute -
The compute types that can currently be attached for training include:
A remote VM -
Azure Databricks (for use in machine learning pipelines)
Azure Data Lake Analytics (for use in machine learning pipelines)
Azure HDInsight -
Box 4: Compute cluster -
Note: There are four compute types:
Compute instance -
Compute clusters -
Inference clusters -
Attached compute -
Note 2:
Compute clusters -
Create a single or multi node compute cluster for your training, batch inferencing or reinforcement learning workloads.
Attached compute -
To use compute targets created outside the Azure Machine Learning workspace, you must attach them. Attaching a compute target makes it available to your workspace. Use Attached compute to attach a compute target for training. Use Inference clusters to attach an AKS cluster for inferencing.
Inference clusters -
Create or attach an Azure Kubernetes Service (AKS) cluster for large scale inferencing.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-compute-studio
DRAG DROP -
You are building an experiment using the Azure Machine Learning designer.
You split a dataset into training and testing sets. You select the Two-Class Boosted Decision Tree as the algorithm.
You need to determine the Area Under the Curve (AUC) of the model.
Which three modules should you use in sequence? To answer, move the appropriate modules from the list of modules to the answer area and arrange them in the correct order.
Select and Place:
Answer:
Step 1: Train Model -
Two-Class Boosted Decision Tree -
First, set up the boosted decision tree model.
1. Find the Two-Class Boosted Decision Tree module in the module palette and drag it onto the canvas.
2. Find the Train Model module, drag it onto the canvas, and then connect the output of the Two-Class Boosted Decision Tree module to the left input port of the
Train Model module.
The Two-Class Boosted Decision Tree module initializes the generic model, and Train Model uses training data to train the model.
3. Connect the left output of the left Execute R Script module to the right input port of the Train Model module (in this tutorial you used the data coming from the left side of the Split Data module for training).
This portion of the experiment now looks something like this:
Step 2: Score Model -
Score and evaluate the models -
You use the testing data that was separated out by the Split Data module to score our trained models. You can then compare the results of the two models to see which generated better results.
Add the Score Model modules -
1. Find the Score Model module and drag it onto the canvas.
2. Connect the Train Model module that's connected to the Two-Class Boosted Decision Tree module to the left input port of the Score Model module.
3. Connect the right Execute R Script module (our testing data) to the right input port of the Score Model module.
Step 3: Evaluate Model -
To evaluate the two scoring results and compare them, you use an Evaluate Model module.
1. Find the Evaluate Model module and drag it onto the canvas.
2. Connect the output port of the Score Model module associated with the boosted decision tree model to the left input port of the Evaluate Model module.
3. Connect the other Score Model module to the right input port.
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?
Answer:
B
For PyTorch, TensorFlow and Chainer tasks, Azure Machine Learning provides respective PyTorch, TensorFlow, and Chainer estimators to simplify using these frameworks.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-ml-models
You create a pipeline in designer to train a model that predicts automobile prices.
Because of non-linear relationships in the data, the pipeline calculates the natural log (Ln) of the prices in the training data, trains a model to predict this natural log of price value, and then calculates the exponential of the scored label to get the predicted price.
The training pipeline is shown in the exhibit. (Click the Training pipeline tab.)
Training pipeline -
You create a real-time inference pipeline from the training pipeline, as shown in the exhibit. (Click the Real-time pipeline tab.)
Real-time pipeline -
You need to modify the inference pipeline to ensure that the web service returns the exponential of the scored label as the predicted automobile price and that client applications are not required to include a price value in the input values.
Which three modifications must you make to the inference pipeline? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer:
ACE
HOTSPOT -
You register the following versions of a model.
You use the Azure ML Python SDK to run a training experiment. You use a variable named run to reference the experiment run.
After the run has been submitted and completed, you run the following code:
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Hot Area:
Answer:
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-and-where
You are creating a classification model for a banking company to identify possible instances of credit card fraud. You plan to create the model in Azure Machine
Learning by using automated machine learning.
The training dataset that you are using is highly unbalanced.
You need to evaluate the classification model.
Which primary metric should you use?
Answer:
B
AUC_weighted is a Classification metric.
Note: AUC is the Area under the Receiver Operating Characteristic Curve. Weighted is the arithmetic mean of the score for each class, weighted by the number of true instances in each class.
Incorrect Answers:
A: normalized_mean_absolute_error is a regression metric, not a classification metric.
C: When comparing approaches to imbalanced classification problems, consider using metrics beyond accuracy such as recall, precision, and AUROC. It may be that switching the metric you optimize for during parameter selection or model selection is enough to provide desirable performance detecting the minority class.
D: normalized_root_mean_squared_error is a regression metric, not a classification metric.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml
You create a machine learning model by using the Azure Machine Learning designer. You publish the model as a real-time service on an Azure Kubernetes
Service (AKS) inference compute cluster. You make no changes to the deployed endpoint configuration.
You need to provide application developers with the information they need to consume the endpoint.
Which two values should you provide to application developers? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer:
CE
Deploying an Azure Machine Learning model as a web service creates a REST API endpoint. You can send data to this endpoint and receive the prediction returned by the model.
You create a web service when you deploy a model to your local environment, Azure Container Instances, Azure Kubernetes Service, or field-programmable gate arrays (FPGA). You retrieve the URI used to access the web service by using the Azure Machine Learning SDK. If authentication is enabled, you can also use the
SDK to get the authentication keys or tokens.
Example:
# URL for the web service
scoring_uri = '<your web service URI>'
# If the service is authenticated, set the key or token
key = '<your key or token>'
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-consume-web-service
HOTSPOT -
You collect data from a nearby weather station. You have a pandas dataframe named weather_df that includes the following data:
The data is collected every 12 hours: noon and midnight.
You plan to use automated machine learning to create a time-series model that predicts temperature over the next seven days. For the initial round of training, you want to train a maximum of 50 different models.
You must use the Azure Machine Learning SDK to run an automated machine learning experiment to train these models.
You need to configure the automated machine learning run.
How should you complete the AutoMLConfig definition? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Hot Area:
Answer:
Box 1: forcasting -
Task: The type of task to run. Values can be 'classification', 'regression', or 'forecasting' depending on the type of automated ML problem to solve.
Box 2: temperature -
The training data to be used within the experiment. It should contain both training features and a label column (optionally a sample weights column).
Box 3: observation_time -
time_column_name: The name of the time column. This parameter is required when forecasting to specify the datetime column in the input data used for building the time series and inferring its frequency. This setting is being deprecated. Please use forecasting_parameters instead.
Box 4: 7 -
"predicts temperature over the next seven days"
max_horizon: The desired maximum forecast horizon in units of time-series frequency. The default value is 1.
Units are based on the time interval of your training data, e.g., monthly, weekly that the forecaster should predict out. When task type is forecasting, this parameter is required.
Box 5: 50 -
"For the initial round of training, you want to train a maximum of 50 different models."
Iterations: The total number of different algorithm and parameter combinations to test during an automated ML experiment.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig
