Microsoft Microsoft Certifications DP-100 Questions & Answers

• Question 301:

  You run an experiment that uses an AutoMLConfig class to define an automated machine learning task with a maximum of ten model training iterations. The task will attempt to find the best performing model based on a metric named accuracy.

  You submit the experiment with the following code:

  

  You need to create Python code that returns the best model that is generated by the automated machine learning task. Which code segment should you use?

  A. best_model = automl_run.get_details()

  B. best_model = automl_run.get_metrics()

  C. best_model = automl_run.get_file_names()[1]

  D. best_model = automl_run.get_output()[1]

  Correct Answer: D

  The get_output method returns the best run and the fitted model.

  Reference:

  https://notebooks.azure.com/azureml/projects/azureml-getting-started/html/how-to-use-azureml/ automated-machine-learning/classification/auto-ml-classification.ipynb

• Question 302:

  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 create a model to forecast weather conditions based on historical data.

  You need to create a pipeline that runs a processing script to load data from a datastore and pass the processed data to a machine learning model training script.

  Solution: Run the following code:

  

  Does the solution meet the goal?

  A. Yes

  B. No

  Correct Answer: A

  The two steps are present: process_step and train_step

  Data_input correctly references the data in the data store.

  Note:

  Data used in pipeline can be produced by one step and consumed in another step by providing a PipelineData object as an output of one step and an input of one or more subsequent steps.

  PipelineData objects are also used when constructing Pipelines to describe step dependencies. To specify that a step requires the output of another step as input, use a PipelineData object in the constructor of both steps.

  For example, the pipeline train step depends on the process_step_output output of the pipeline process step:

  from azureml.pipeline.core import Pipeline, PipelineData from azureml.pipeline.steps import PythonScriptStep

  datastore = ws.get_default_datastore()

  process_step_output = PipelineData("processed_data", datastore=datastore) process_step = PythonScriptStep(script_name="process.py", arguments=["--data_for_train", process_step_output],

  outputs=[process_step_output],

  compute_target=aml_compute,

  source_directory=process_directory)

  train_step = PythonScriptStep(script_name="train.py",

  arguments=["--data_for_train", process_step_output],

  inputs=[process_step_output],

  compute_target=aml_compute,

  source_directory=train_directory)

  pipeline = Pipeline(workspace=ws, steps=[process_step, train_step])

  Reference:

  https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata? view=azure-ml-py

• Question 303:

  You use Azure Machine Learning designer to create a training pipeline for a regression model.

  You need to prepare the pipeline for deployment as an endpoint that generates predictions asynchronously for a dataset of input data values.

  What should you do?

  A. Clone the training pipeline.

  B. Create a batch inference pipeline from the training pipeline.

  C. Create a real-time inference pipeline from the training pipeline.

  D. Replace the dataset in the training pipeline with an Enter Data Manually module.

  Correct Answer: C

  You must first convert the training pipeline into a real-time inference pipeline. This process removes training modules and adds web service inputs and outputs to handle requests. Incorrect Answers:

  A: Use the Enter Data Manually module to create a small dataset by typing values.

  Reference: https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-designer-automobile-price-deploy https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/enter-data-manually

• Question 304:

  You retrain an existing model.

  You need to register the new version of a model while keeping the current version of the model in the registry.

  What should you do?

  A. Register a model with a different name from the existing model and a custom property named version with the value 2.

  B. Register the model with the same name as the existing model.

  C. Save the new model in the default datastore with the same name as the existing model. Do not register the new model.

  D. Delete the existing model and register the new one with the same name.

  Correct Answer: B

  Model version: A version of a registered model. When a new model is added to the Model Registry, it is added as Version 1. Each model registered to the same model name increments the version number.

  Reference: https://docs.microsoft.com/en-us/azure/databricks/applications/mlflow/model-registry

• Question 305:

  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 change 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.

  A. The name of the AKS cluster where the endpoint is hosted.

  B. The name of the inference pipeline for the endpoint.

  C. The URL of the endpoint.

  D. The run ID of the inference pipeline experiment for the endpoint.

  E. The key for the endpoint.

  Correct 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 = ''

  # If the service is authenticated, set the key or token

  key = ''

  Reference:

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-consume-web-service

• Question 306:

  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.

  A. Connect the output of the Apply SQL Transformation to the Web Service Output module.

  B. Replace the Web Service Input module with a data input that does not include the price column.

  C. Add a Select Columns module before the Score Model module to select all columns other than price.

  D. Replace the training dataset module with a data input that does not include the price column.

  E. Remove the Apply Math Operation module that replaces price with its natural log from the data flow.

  F. Remove the Apply SQL Transformation module from the data flow.

  Correct Answer: ACE

• Question 307:

  You train a model and register it in your Azure Machine Learning workspace. You are ready to deploy the model as a real-time web service.

  You deploy the model to an Azure Kubernetes Service (AKS) inference cluster, but the deployment fails because an error occurs when the service runs the entry script that is associated with the model deployment.

  You need to debug the error by iteratively modifying the code and reloading the service, without requiring a re-deployment of the service for each code update.

  What should you do?

  A. Modify the AKS service deployment configuration to enable application insights and re-deploy to AKS.

  B. Create an Azure Container Instances (ACI) web service deployment configuration and deploy the model on ACI.

  C. Add a breakpoint to the first line of the entry script and redeploy the service to AKS.

  D. Create a local web service deployment configuration and deploy the model to a local Docker container.

  E. Register a new version of the model and update the entry script to load the new version of the model from its registered path.

  Correct Answer: B

  How to work around or solve common Docker deployment errors with Azure Container Instances (ACI) and Azure Kubernetes Service (AKS) using Azure Machine Learning.

  The recommended and the most up to date approach for model deployment is via the Model.deploy() API using an Environment object as an input parameter. In this case our service will create a base docker image for you during deployment stage and mount the required models all in one call. The basic deployment tasks are:

  1.

  Register the model in the workspace model registry.

  2.

  Define Inference Configuration:

  a.

  Create an Environment object based on the dependencies you specify in the environment yaml file or use one of our procured environments.

  b.

  Create an inference configuration (InferenceConfig object) based on the environment and the scoring script.

  3. Deploy the model to Azure Container Instance (ACI) service or to Azure Kubernetes Service (AKS).

• Question 308:

  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?

  A. normalized_mean_absolute_error

  B. AUC_weighted

  C. accuracy

  D. normalized_root_mean_squared_error

  E. spearman_correlation

  Correct 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

• Question 309:

  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?

  A. TensorFlow

  B. PyTorch

  C. SKLearn

  D. Estimator

  Correct 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

• Question 310:

  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?

  A. Yes

  B. No

  Correct 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