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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?
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 and register a machine learning model. You plan to deploy the model as a real-time web service. Applications must use key-based authentication to use the model. You need to deploy the web service. Solution: Create an AksWebservice instance. Set the value of the auth_enabled property to True. Deploy the model to the service. Does the solution meet the goal?
You create an Azure Machine Learning workspace. You use the Azure Machine Learning SDK for Python. You must create a dataset from remote paths. The dataset must be reusable within the workspace. You need to create the dataset. How should you complete the following code segment? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
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 MimicExplainer. Does the solution meet the goal?
You create an Azure Machine Learning workspace. You must configure an event handler to send an email notification wten data drift is detected in the workspace datasets. You must minimize development efforts. You need to configure an Azure service to send the notification. Which Azure service should you use?
You create a binary classification model. The model is registered in an Azure Machine Learning workspace. You use the Azure Machine Learning Fairness SDK to assess the model fairness. You develop a training script for the model on a local machine. You need to load the model fairness metrics into Azure Machine Learning studio. What should you do?
You use Azure Machine Learning Studio to build a machine learning experiment. You need to divide data into two distinct datasets. Which module should you use?
You create a workspace by using Azure Machine Learning Studio. You must run a Python SDK v2 notebook in the workspace by using Azure Machine Learning Studio. You must preserve the current values of variables set in the notebook for the current instance. You need to maintain the state of the notebook. What should you do?
You have an Azure Machine Learning workspace named workspaces. You must add a datastore that connects an Azure Blob storage container to workspaces. You must be able to configure a privilege level. You need to configure authentication. Which authentication method should you use?
You run a script as an experiment in Azure Machine Learning. You have a Run object named run that references the experiment run. You must review the log files that were generated during the experiment run. You need to download the log files to a local folder for review. Which two code segments can you run to achieve this goal? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.
You develop and train a machine learning model to predict fraudulent transactions for a hotel booking website. Traffic to the site varies considerably. The site experiences heavy traffic on Monday and Friday and much lower traffic on other days. Holidays are also high web traffic days. You need to deploy the model as an Azure Machine Learning real-time web service endpoint on compute that can dynamically scale up and down to support demand. Which deployment compute option should you use?
You train and register a model in your Azure Machine Learning workspace. You must publish a pipeline that enables client applications to use the model for batch inferencing. You must use a pipeline with a single ParallelRunStep step that runs a Python inferencing script to get predictions from the input data. You need to create the inferencing script for the ParallelRunStep pipeline step. Which two functions should you include? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.
You are creating a new Azure Machine Learning pipeline using the designer. The pipeline must train a model using data in a comma-separated values (CSV) file that is published on a website. You have not created a dataset for this file. You need to ingest the data from the CSV file into the designer pipeline using the minimal administrative effort. Which module should you add to the pipeline in Designer?
You use Azure Machine Learning to train a model. You must use a sampling method for tuning hyperparameters. The sampling method must pick samples based on how the model performed with previous samples. You need to select a sampling method. Which sampling method should you use?
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 PFIExplainer. Does the solution meet the goal?
You create a multi-class image classification deep learning model. You train the model by using PyTorch version 1.2. You need to ensure that the correct version of PyTorch can be identified for the inferencing environment when the model is deployed. What should you do?
You use Azure Machine Learning studio to analyze a dataset containing a decimal column named column1. You need to verity that the column1 values are normally distributed. Which static should you use?
You are a lead data scientist for a project that tracks the health and migration of birds. You create a multi-class image classification deep learning model that uses a set of labeled bird photographs collected by experts. You have 100,000 photographs of birds. All photographs use the JPG format and are stored in an Azure blob container in an Azure subscription. You need to access the bird photograph files in the Azure blob container from the Azure Machine Learning service workspace that will be used for deep learning model training. You must minimize data movement. What should you do?
You create an Azure Machine Learning workspace named workspaces. You create a Python SDK v2 notebook to perform custom model training in wortcspacel. You need to run the notebook from Azure Machine Learning Studio in workspace1. What should you provision first?
You create an Azure Machine Learning workspace. You train an MLflow-formatted regression model by using tabular structured data. You must use a Responsible Al dashboard to assess the model. You need to use the Azure Machine Learning studio Ul to generate the Responsible A dashboard. What should you do first?
You have a Python script that executes a pipeline. The script includes the following code: from azureml.core import Experiment pipeline_run = Experiment(ws, 'pipeline_test').submit(pipeline) You want to test the pipeline before deploying the script. You need to display the pipeline run details written to the STDOUT output when the pipeline completes. Which code segment should you add to the test script?
You train a machine learning model. You must deploy the model as a real-time inference service for testing. The service requires low CPU utilization and less than 48 MB of RAM. The compute target for the deployed service must initialize automatically while minimizing cost and administrative overhead. Which compute target should you use?
You have machine learning models produce unfair predictions across sensitive features. You must use a post-processing technique to apply a constraint to the models to mitigate their unfairness. You need to select a post-processing technique and model type. What should you use? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
You create a training pipeline by using the Azure Machine Learning designer. You need to load data into a machine learning pipeline by using the Import Data component. Which two data sources could you use? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point
You are creating a compute target to train a machine learning experiment. The compute target must support automated machine learning, machine learning pipelines, and Azure Machine Learning designer training. You need to configure the compute target Which option should you use?
You create a script that trains a convolutional neural network model over multiple epochs and logs the validation loss after each epoch. The script includes arguments for batch size and learning rate. You identify a set of batch size and learning rate values that you want to try. You need to use Azure Machine Learning to find the combination of batch size and learning rate that results in the model with the lowest validation loss. What should you do?
You plan to run a script as an experiment using a Script Run Configuration. The script uses modules from the scipy library as well as several Python packages that are not typically installed in a default conda environment You plan to run the experiment on your local workstation for small datasets and scale out the experiment by running it on more powerful remote compute clusters for larger datasets. You need to ensure that the experiment runs successfully on local and remote compute with the least administrative effort. What should you do?
You use the Azure Machine Learning Python SDK to define a pipeline to train a model. The data used to train the model is read from a folder in a datastore. You need to ensure the pipeline runs automatically whenever the data in the folder changes. What should you do?
You have a dataset that is stored m an Azure Machine Learning workspace. You must perform a data analysis for differentiate privacy by using the SmartNoise SDK. You need to measure the distribution of reports for repeated queries to ensure that they are balanced Which type of test should you perform?
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 plan to use a Python script to run an Azure Machine Learning experiment. The script creates a reference to the experiment run context, loads data from a file, identifies the set of unique values for the label column, and completes the experiment run: from azureml.core import Run import pandas as pd run = Run.get_context() data = pd.read_csv('data.csv') label_vals = data['label'].unique() # Add code to record metrics here run.complete() The experiment must record the unique labels in the data as metrics for the run that can be reviewed later. You must add code to the script to record the unique label values as run metrics at the point indicated by the comment. Solution: Replace the comment with the following code: for label_val in label_vals: run.log('Label Values', label_val) Does the solution meet the goal?
You have a Jupyter Notebook that contains Python code that is used to train a model. You must create a Python script for the production deployment. The solution must minimize code maintenance. Which two actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.
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 plan to use a Python script to run an Azure Machine Learning experiment. The script creates a reference to the experiment run context, loads data from a file, identifies the set of unique values for the label column, and completes the experiment run: from azureml.core import Run import pandas as pd run = Run.get_context() data = pd.read_csv('data.csv') label_vals = data['label'].unique() # Add code to record metrics here run.complete() The experiment must record the unique labels in the data as metrics for the run that can be reviewed later. You must add code to the script to record the unique label values as run metrics at the point indicated by the comment. Solution: Replace the comment with the following code: run.log_table('Label Values', label_vals) Does the solution meet the goal?
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.
You manage an Azure Machine Learning workspace by using the Azure CLI ml extension v2. You need to define a YAML schema to create a compute cluster. Which schema should you use?
You use an Azure Machine Learning workspace. You have a trained model that must be deployed as a web service. Users must authenticate by using Azure Active Directory. What should you do?
You create a deep learning model for image recognition on Azure Machine Learning service using GPU-based training. You must deploy the model to a context that allows for real-time GPU-based inferencing. You need to configure compute resources for model inferencing. Which compute type should you use?
You use the designer to create a training pipeline for a classification model. The pipeline uses a dataset that includes the features and labels required for model training. You create a real-time inference pipeline from the training pipeline. You observe that the schema for the generated web service input is based on the dataset and includes the label column that the model predicts. Client applications that use the service must not be required to submit this value. You need to modify the inference pipeline to meet the requirement. What should you do?
You are implementing hyperparameter tuning by using Bayesian sampling for an Azure ML Python SDK v2-based model training from a notebook. The notebook is in an Azure Machine Learning workspace. The notebook uses a training script that runs on a compute cluster with 20 nodes. The code implements Bandit termination policy with slack_factor set to 02 and a sweep job with max_concurrent_trials set to 10. You must increase effectiveness of the tuning process by improving sampling convergence. You need to select which sampling convergence to use. What should you select?
You have an Azure Machine Learning workspace. You build a deep learning model. You need to publish a GPU-enabled model as a web service. Which two compute targets can you use? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.
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 plan to use a Python script to run an Azure Machine Learning experiment. The script creates a reference to the experiment run context, loads data from a file, identifies the set of unique values for the label column, and completes the experiment run: from azureml.core import Run import pandas as pd run = Run.get_context() data = pd.read_csv('data.csv') label_vals = data['label'].unique() # Add code to record metrics here run.complete() The experiment must record the unique labels in the data as metrics for the run that can be reviewed later. You must add code to the script to record the unique label values as run metrics at the point indicated by the comment. Solution: Replace the comment with the following code: run.upload_file('outputs/labels.csv', './data.csv') Does the solution meet the goal?
You are implementing hyperparameter tuning for a model training from a notebook. The notebook is in an Azure Machine Learning workspace. You add code that imports all relevant Python libraries. You must configure Bayesian sampling over the search space for the num_hidden_layers and batch_size hyperparameters. You need to complete the following Python code to configure Bayesian sampling. Which code segments should you use? To answer, select the appropriate options in the answer area NOTE: Each correct selection is worth one point.
You train and register a machine learning model. You create a batch inference pipeline that uses the model to generate predictions from multiple data files. You must publish the batch inference pipeline as a service that can be scheduled to run every night. You need to select an appropriate compute target for the inference service. Which compute target should you use?
You create a multi-class image classification model with automated machine learning in Azure Machine Learning. You need to prepare labeled image data as input for model training in the form of an Azure Machine Learning tabular dataset. Which data format should you use?
You are using Azure Machine Learning to monitor a trained and deployed model. You implement Event Grid to respond to Azure Machine Learning events. Model performance has degraded due to model input data changes. You need to trigger a remediation ML pipeline based on an Azure Machine Learning event. Which event should you use?
You create an Azure Machine Learning pipeline named pipeline1 with two steps that contain Python scripts. Data processed by the first step is passed to the second step. You must update the content of the downstream data source of pipeline1 and run the pipeline again You need to ensure the new run of pipeline1 fully processes the updated content. Solution: Set the allow_reuse parameter of the PythonScriptStep object of both steps to False Does the solution meet the goal?
You plan to use automated machine learning to train a regression model. You have data that has features which have missing values, and categorical features with few distinct values. You need to configure automated machine learning to automatically impute missing values and encode categorical features as part of the training task. Which parameter and value pair should you use in the AutoMLConfig class?
You are a data scientist working for a bank and have used Azure ML to train and register a machine learning model that predicts whether a customer is likely to repay a loan. You want to understand how your model is making selections and must be sure that the model does not violate government regulations such as denying loans based on where an applicant lives. You need to determine the extent to which each feature in the customer data is influencing predictions. What should you do?
You train and register an Azure Machine Learning model You plan to deploy the model to an online endpoint You need to ensure that applications will be able to use the authentication method with a non-expiring artifact to access the model. Solution: Create a managed online endpoint with the default authentication settings. Deploy the model to the online endpoint. Does the solution meet the goal?
You deploy a real-time inference service for a trained model. The deployed model supports a business-critical application, and it is important to be able to monitor the data submitted to the web service and the predictions the data generates. You need to implement a monitoring solution for the deployed model using minimal administrative effort. What should you do?
You plan to provision an Azure Machine Learning Basic edition workspace for a data science project. You need to identify the tasks you will be able to perform in the workspace. Which three tasks will you be able to perform? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.
You use Azure Machine Learning designer to create a real-time service endpoint. You have a single Azure Machine Learning service compute resource. You train the model and prepare the real-time pipeline for deployment You need to publish the inference pipeline as a web service. Which compute type should you use?
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?
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?
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?
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?
You create an MLflow model You must deploy the model to Azure Machine Learning for batch inference. You need to create the batch deployment. Which two components should you use? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point
You manage an Azure Machine Learning workspace named workspaces You must develop Python SDK v2 code to attach an Azure Synapse Spark pool as a compute target in workspaces The code must invoke the constructor of the SynapseSparkCompute class. You need to invoke the constructor. What should you use?
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 and register a machine learning model. You plan to deploy the model as a real-time web service. Applications must use key-based authentication to use the model. You need to deploy the web service. Solution: Create an AciWebservice instance. Set the value of the ssl_enabled property to True. Deploy the model to the service. Does the solution meet the goal?
You are training machine learning models in Azure Machine Learning. You use Hyperdrive to tune the hyperparameters. In previous model training and tuning runs, many models showed similar performance. You need to select an early termination policy that meets the following requirements: • accounts for the performance of all previous runs when evaluating the current run • avoids comparing the current run with only the best performing run to date Which two early termination policies should you use? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.
You register a model that you plan to use in a batch inference pipeline. The batch inference pipeline must use a ParallelRunStep step to process files in a file dataset. The script has the ParallelRunStep step runs must process six input files each time the inferencing function is called. You need to configure the pipeline. Which configuration setting should you specify in the ParallelRunConfig object for the PrallelRunStep step?
You are developing a machine learning model. You must inference the machine learning model for testing. You need to use a minimal cost compute target Which two compute targets should you use? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point
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