AI-300 ML Model Lifecycle And Operations — All Questions With Answers

Question 1mediummultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You are orchestrating a multi-step ML pipeline in Azure Machine Learning. You need to ensure that a downstream step only executes if the upstream model training step finishes successfully, while allowing the pipeline to continue even if a non-critical logging step fails. Which configuration should you use?

Question 2mediummultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You are deploying a model to an Azure Kubernetes Service (AKS) cluster. You need to ensure that the deployment handles traffic spikes by scaling based on GPU usage. Which setting must be enabled in the inference configuration?

Question 3hardmultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You need to ensure that your Azure ML models are deployed with high availability across multiple regions. Which deployment strategy should you configure?

Question 4hardmultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You are managing model versioning in Azure Machine Learning Registry. You need to promote a model from 'Staging' to 'Production' without creating a new asset version. Which command or action should you perform?

Question 5easymultiple choice

Read the full ML Model Lifecycle And Operations explanation →

A team uses Azure Machine Learning to track experiments. You need to ensure that every run is associated with a specific git commit hash to ensure reproducibility. Where should this be configured?

Question 6hardmultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You are troubleshooting a model deployment failure where the container fails to start due to missing environment variables. Where do you find the logs to identify the cause?

Question 7mediummultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You want to enforce a policy that all models must be registered before being deployed. Which feature should you use to implement this constraint?

Question 8easymultiple choice

Read the full ML Model Lifecycle And Operations explanation →

A model is exhibiting data drift. You have created a drift monitor. What is the next step to automate the retraining?

Question 9hardmultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You are running a distributed training job using the 'PyTorch' framework on Azure Machine Learning. You need to configure the 'DistributionConfiguration'. Which setting is mandatory for multi-node training?

Question 10mediummultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You are configuring an 'Azure Machine Learning Compute Cluster' for a heavy training job. You want to ensure it shuts down automatically when no jobs are running. What setting should you configure?

Question 11hardmultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You have an Azure Machine Learning pipeline that uses 'PipelineData' to pass information between steps. You want to share data between a training step and a scoring step. What is the recommended way to persist this data?

Question 12easymultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You are reviewing the 'Run History' in Azure Machine Learning. You want to compare the training time of two different experiments. Which UI feature should you use?

Question 13mediummultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You are debugging an Azure ML pipeline. You want to see the stdout of a specific step that failed. How do you access this?

Question 14mediummultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You have an Azure ML environment that needs to access a private Azure SQL database. How should you configure the connection?

Question 15hardmultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You are configuring a 'Managed Online Endpoint' with SSL termination. Where do you manage the SSL certificates?

Question 16mediummultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You want to perform hyperparameter tuning using the 'HyperDrive' service. You have a requirement to stop poor-performing runs early to save compute costs. Which policy should you use?

Question 17hardmultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You are configuring a 'Managed Online Endpoint' for a very large model (10GB+). The deployment is failing during the 'pulling image' phase. What is the most likely cause?

Question 18mediummulti select

Read the full ML Model Lifecycle And Operations explanation →

You are monitoring model drift. Which TWO features are required to configure a Data Drift Monitor?

Question 19hardmultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You have an automated deployment pipeline. You want to run an integration test on the model after deployment. Which tool is best suited for this?

Question 20mediummulti select

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Which THREE types of information are found in a Run Object in Azure ML?

Question 21hardmulti select

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Which THREE settings are part of the 'InferenceConfig' object in Azure ML?

Question 22mediummulti select

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You are using Azure Machine Learning Pipelines. Which THREE triggers can be used to start a pipeline?

Question 23easymulti select

Read the full ML Model Lifecycle And Operations explanation →

Which TWO languages are natively supported for the Azure ML SDK?

Question 24hardmulti select

Read the full ML Model Lifecycle And Operations explanation →

You need to implement a retraining trigger based on performance degradation. Which TWO metrics should you monitor to decide when to retrain?

Question 25mediummulti select

Read the full ML Model Lifecycle And Operations explanation →

You are configuring a CI/CD pipeline for model deployment. Which THREE actions must you perform to ensure model traceability?

Question 26mediummultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You are building an Azure Machine Learning pipeline. You need to ensure that the pipeline components are reusable and versioned independently. What should you use?

Question 27easymultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You need to monitor the data drift of a model deployed in Azure ML. What is the first step you must take?

Question 28easymultiple choice

Read the full ML Model Lifecycle And Operations explanation →

Your team needs to share a model across different workspaces. What is the most efficient way to achieve this in Azure Machine Learning?

Question 29hardmultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You are implementing a retraining trigger for a demand forecasting model. You want to trigger a pipeline execution only when the drift metric for the 'Price' feature exceeds a predefined threshold. Which service should you integrate with Azure Machine Learning?

Question 30mediummultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You are deploying a model via a Managed Online Endpoint. You want to implement a Canary deployment where 10% of traffic is sent to a new model version. How do you configure this?

Question 31mediummultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You are configuring a batch scoring job. You need to ensure that the job processes data in parallel to reduce completion time. What property should you adjust in the 'ParallelRunConfig'?

Question 32hardmultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You are using MLflow to track experiments in Azure Machine Learning. You need to log a custom metric that is calculated every 100 iterations. Which MLflow function should you use?

Question 33mediummultiple choice

Study the full Python automation breakdown →

You are defining an Azure Machine Learning environment for a training job. The environment requires a specific set of Python libraries. What is the best practice for defining these dependencies?

Question 34easymultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You are moving a model from a local environment to Azure Machine Learning. Which file is required to define the entry script for the model inference?

Question 35mediummultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You need to ensure that training data is encrypted at rest in the Blob Storage linked to your Azure Machine Learning workspace. How do you ensure this?

Question 36easymultiple choice

Read the full ML Model Lifecycle And Operations explanation →

What is the primary purpose of a 'Labeling Project' in Azure Machine Learning?

Question 37hardmultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You are deploying a model that requires a high-memory compute for inference. You are using a 'Managed Online Endpoint'. Where do you specify the instance type for this deployment?

Question 38easymultiple choice

Read the full ML Model Lifecycle And Operations explanation →

What is the purpose of a 'Datastore' in Azure Machine Learning?

Question 39easymultiple choice

Read the full ML Model Lifecycle And Operations explanation →

Which Azure Machine Learning resource provides a pre-configured environment for development?

Question 40hardmultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You are automating model registration using the Azure ML CLI. You need to ensure the registration only happens if the model accuracy is above 0.9. How do you implement this condition?

Question 41easymultiple choice

Read the full ML Model Lifecycle And Operations explanation →

Which service allows you to track ML models and their associated artifacts?

Question 42easymultiple choice

Read the full ML Model Lifecycle And Operations explanation →

What is the 'Workspace' in Azure Machine Learning?

Question 43easymultiple choice

Read the full ML Model Lifecycle And Operations explanation →

What is the benefit of using 'Azure Machine Learning Datasets' (or Data Assets) over raw storage paths?

Question 44mediummultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You have an automated model training pipeline that is failing intermittently due to compute availability. What should you configure to ensure the pipeline is more resilient?

Question 45hardmultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You are implementing a custom container for model training. You need to push the image to the 'Azure Container Registry' (ACR) linked to your workspace. What is the correct command?

Question 46mediummultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You need to ensure that your model inference code has access to the latest secret keys without hardcoding them. What is the recommended integration?

Question 47hardmulti select

Read the full ML Model Lifecycle And Operations explanation →

You are managing model lifecycle security. Which THREE actions are recommended to secure your ML models?

Question 48mediummultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You are creating a 'Pipeline' and want to share a dataset across multiple steps. What is the most efficient way to access this data?

Question 49hardmulti select

Read the full ML Model Lifecycle And Operations explanation →

Which THREE components are required to define a 'Managed Online Deployment'?

Question 50easymulti select

Read the full ML Model Lifecycle And Operations explanation →

Which TWO types of compute can be used for training in Azure Machine Learning?

Question 51mediummultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You are configuring a 'Managed Online Endpoint' for production. You want to ensure high availability. What should you configure?

Question 52mediummulti select

Read the full ML Model Lifecycle And Operations explanation →

You are optimizing your Azure ML pipeline performance. Which THREE steps should you take to reduce execution time?

Question 53mediummulti select

Read the full ML Model Lifecycle And Operations explanation →

You are preparing a model for deployment. Which THREE items should you include in the model package?

Question 54hardmulti select

Read the full ML Model Lifecycle And Operations explanation →

Which THREE metrics can be logged during training to track performance in Azure ML?

Question 55easymulti select

Read the full ML Model Lifecycle And Operations explanation →

Which TWO actions can you perform in the Azure Machine Learning studio?

Question 56hardmulti select

Read the full ML Model Lifecycle And Operations explanation →

You are troubleshooting a deployment. Which THREE logs are most helpful to check?

Question 57easymulti select

Read the full ML Model Lifecycle And Operations explanation →

Which TWO resources are created inside an Azure Machine Learning workspace?

Question 58mediummulti select

Read the full ML Model Lifecycle And Operations explanation →

Which THREE security features are essential for a production Azure ML deployment?

Question 59hardmulti select

Read the full ML Model Lifecycle And Operations explanation →

Which THREE items are captured in the experiment lineage in Azure Machine Learning?

Question 60mediummultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You are an MLOps engineer at a financial services company. Your team trains a fraud detection model weekly using an Azure Machine Learning pipeline. Compliance requires that every model registered in the workspace is traceable to the exact training run, dataset version, and code commit that produced it. You need to ensure this lineage is automatically captured for every registered model without manual documentation. What should you do?

Question 61hardmultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You manage a managed online endpoint in Azure Machine Learning that serves a recommendation model. The model was trained months ago, and you now need to deploy a new version trained on recent data with zero downtime. You must be able to send a small percentage of live traffic to the new version to validate it before full rollout. What should you do?

Question 62easymultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You are an MLOps engineer preparing to retrain a demand forecasting model on a schedule. The training data resides in an Azure Machine Learning datastore backed by Azure Blob Storage. You need the training job to run automatically every night at 02:00 UTC without manual intervention. What should you create?

Question 63mediummulti select

Read the full ML Model Lifecycle And Operations explanation →

You are responsible for monitoring a deployed model on an Azure Machine Learning managed online endpoint. The model's input feature distribution has drifted from the training baseline, and you need to detect this automatically and act on it. Which two actions should you take? (Choose two.)

Question 1mediummultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You are orchestrating a multi-step ML pipeline in Azure Machine Learning. You need to ensure that a downstream step only executes if the upstream model training step finishes successfully, while allowing the pipeline to continue even if a non-critical logging step fails. Which configuration should you use?

Question 2mediummultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You are deploying a model to an Azure Kubernetes Service (AKS) cluster. You need to ensure that the deployment handles traffic spikes by scaling based on GPU usage. Which setting must be enabled in the inference configuration?

Question 3hardmultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You need to ensure that your Azure ML models are deployed with high availability across multiple regions. Which deployment strategy should you configure?

Question 4hardmultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You are managing model versioning in Azure Machine Learning Registry. You need to promote a model from 'Staging' to 'Production' without creating a new asset version. Which command or action should you perform?

Question 5easymultiple choice

Read the full ML Model Lifecycle And Operations explanation →

A team uses Azure Machine Learning to track experiments. You need to ensure that every run is associated with a specific git commit hash to ensure reproducibility. Where should this be configured?

Question 6hardmultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You are troubleshooting a model deployment failure where the container fails to start due to missing environment variables. Where do you find the logs to identify the cause?

Question 7mediummultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You want to enforce a policy that all models must be registered before being deployed. Which feature should you use to implement this constraint?

Question 8easymultiple choice

Read the full ML Model Lifecycle And Operations explanation →

A model is exhibiting data drift. You have created a drift monitor. What is the next step to automate the retraining?

Question 9hardmultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You are running a distributed training job using the 'PyTorch' framework on Azure Machine Learning. You need to configure the 'DistributionConfiguration'. Which setting is mandatory for multi-node training?

Question 10mediummultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You are configuring an 'Azure Machine Learning Compute Cluster' for a heavy training job. You want to ensure it shuts down automatically when no jobs are running. What setting should you configure?

Question 11hardmultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You have an Azure Machine Learning pipeline that uses 'PipelineData' to pass information between steps. You want to share data between a training step and a scoring step. What is the recommended way to persist this data?

Question 12easymultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You are reviewing the 'Run History' in Azure Machine Learning. You want to compare the training time of two different experiments. Which UI feature should you use?

Question 13mediummultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You are debugging an Azure ML pipeline. You want to see the stdout of a specific step that failed. How do you access this?

Question 14mediummultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You have an Azure ML environment that needs to access a private Azure SQL database. How should you configure the connection?

Question 15hardmultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You are configuring a 'Managed Online Endpoint' with SSL termination. Where do you manage the SSL certificates?

Question 16mediummultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You want to perform hyperparameter tuning using the 'HyperDrive' service. You have a requirement to stop poor-performing runs early to save compute costs. Which policy should you use?

Question 17hardmultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You are configuring a 'Managed Online Endpoint' for a very large model (10GB+). The deployment is failing during the 'pulling image' phase. What is the most likely cause?

Question 18mediummulti select

Read the full ML Model Lifecycle And Operations explanation →

You are monitoring model drift. Which TWO features are required to configure a Data Drift Monitor?

Question 19hardmultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You have an automated deployment pipeline. You want to run an integration test on the model after deployment. Which tool is best suited for this?

Question 20mediummulti select

Read the full ML Model Lifecycle And Operations explanation →

Which THREE types of information are found in a Run Object in Azure ML?

Question 21hardmulti select

Read the full ML Model Lifecycle And Operations explanation →

Which THREE settings are part of the 'InferenceConfig' object in Azure ML?

Question 22mediummulti select

Read the full ML Model Lifecycle And Operations explanation →

You are using Azure Machine Learning Pipelines. Which THREE triggers can be used to start a pipeline?

Question 23easymulti select

Read the full ML Model Lifecycle And Operations explanation →

Which TWO languages are natively supported for the Azure ML SDK?

Question 24hardmulti select

Read the full ML Model Lifecycle And Operations explanation →

You need to implement a retraining trigger based on performance degradation. Which TWO metrics should you monitor to decide when to retrain?

Question 25mediummulti select

Read the full ML Model Lifecycle And Operations explanation →

You are configuring a CI/CD pipeline for model deployment. Which THREE actions must you perform to ensure model traceability?

Question 26mediummultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You are building an Azure Machine Learning pipeline. You need to ensure that the pipeline components are reusable and versioned independently. What should you use?

Question 27easymultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You need to monitor the data drift of a model deployed in Azure ML. What is the first step you must take?

Question 28easymultiple choice

Read the full ML Model Lifecycle And Operations explanation →

Your team needs to share a model across different workspaces. What is the most efficient way to achieve this in Azure Machine Learning?

Question 29hardmultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You are implementing a retraining trigger for a demand forecasting model. You want to trigger a pipeline execution only when the drift metric for the 'Price' feature exceeds a predefined threshold. Which service should you integrate with Azure Machine Learning?

Question 30mediummultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You are deploying a model via a Managed Online Endpoint. You want to implement a Canary deployment where 10% of traffic is sent to a new model version. How do you configure this?

Question 31mediummultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You are configuring a batch scoring job. You need to ensure that the job processes data in parallel to reduce completion time. What property should you adjust in the 'ParallelRunConfig'?

Question 32hardmultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You are using MLflow to track experiments in Azure Machine Learning. You need to log a custom metric that is calculated every 100 iterations. Which MLflow function should you use?

Question 33mediummultiple choice

Study the full Python automation breakdown →

You are defining an Azure Machine Learning environment for a training job. The environment requires a specific set of Python libraries. What is the best practice for defining these dependencies?

Question 34easymultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You are moving a model from a local environment to Azure Machine Learning. Which file is required to define the entry script for the model inference?

Question 35mediummultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You need to ensure that training data is encrypted at rest in the Blob Storage linked to your Azure Machine Learning workspace. How do you ensure this?

Question 36easymultiple choice

Read the full ML Model Lifecycle And Operations explanation →

What is the primary purpose of a 'Labeling Project' in Azure Machine Learning?

Question 37hardmultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You are deploying a model that requires a high-memory compute for inference. You are using a 'Managed Online Endpoint'. Where do you specify the instance type for this deployment?

Question 38easymultiple choice

Read the full ML Model Lifecycle And Operations explanation →

What is the purpose of a 'Datastore' in Azure Machine Learning?

Question 39easymultiple choice

Read the full ML Model Lifecycle And Operations explanation →

Which Azure Machine Learning resource provides a pre-configured environment for development?

Question 40hardmultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You are automating model registration using the Azure ML CLI. You need to ensure the registration only happens if the model accuracy is above 0.9. How do you implement this condition?

Question 41easymultiple choice

Read the full ML Model Lifecycle And Operations explanation →

Which service allows you to track ML models and their associated artifacts?

Question 42easymultiple choice

Read the full ML Model Lifecycle And Operations explanation →

What is the 'Workspace' in Azure Machine Learning?

Question 43easymultiple choice

Read the full ML Model Lifecycle And Operations explanation →

What is the benefit of using 'Azure Machine Learning Datasets' (or Data Assets) over raw storage paths?

Question 44mediummultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You have an automated model training pipeline that is failing intermittently due to compute availability. What should you configure to ensure the pipeline is more resilient?

Question 45hardmultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You are implementing a custom container for model training. You need to push the image to the 'Azure Container Registry' (ACR) linked to your workspace. What is the correct command?

Question 46mediummultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You need to ensure that your model inference code has access to the latest secret keys without hardcoding them. What is the recommended integration?

Question 47hardmulti select

Read the full ML Model Lifecycle And Operations explanation →

You are managing model lifecycle security. Which THREE actions are recommended to secure your ML models?

Question 48mediummultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You are creating a 'Pipeline' and want to share a dataset across multiple steps. What is the most efficient way to access this data?

Question 49hardmulti select

Read the full ML Model Lifecycle And Operations explanation →

Which THREE components are required to define a 'Managed Online Deployment'?

Question 50easymulti select

Read the full ML Model Lifecycle And Operations explanation →

Which TWO types of compute can be used for training in Azure Machine Learning?

Question 51mediummultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You are configuring a 'Managed Online Endpoint' for production. You want to ensure high availability. What should you configure?

Question 52mediummulti select

Read the full ML Model Lifecycle And Operations explanation →

You are optimizing your Azure ML pipeline performance. Which THREE steps should you take to reduce execution time?

Question 53mediummulti select

Read the full ML Model Lifecycle And Operations explanation →

You are preparing a model for deployment. Which THREE items should you include in the model package?

Question 54hardmulti select

Read the full ML Model Lifecycle And Operations explanation →

Which THREE metrics can be logged during training to track performance in Azure ML?

Question 55easymulti select

Read the full ML Model Lifecycle And Operations explanation →

Which TWO actions can you perform in the Azure Machine Learning studio?

Question 56hardmulti select

Read the full ML Model Lifecycle And Operations explanation →

You are troubleshooting a deployment. Which THREE logs are most helpful to check?

Question 57easymulti select

Read the full ML Model Lifecycle And Operations explanation →

Which TWO resources are created inside an Azure Machine Learning workspace?

Question 58mediummulti select

Read the full ML Model Lifecycle And Operations explanation →

Which THREE security features are essential for a production Azure ML deployment?

Question 59hardmulti select

Read the full ML Model Lifecycle And Operations explanation →

Which THREE items are captured in the experiment lineage in Azure Machine Learning?

Question 60mediummultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You are an MLOps engineer at a financial services company. Your team trains a fraud detection model weekly using an Azure Machine Learning pipeline. Compliance requires that every model registered in the workspace is traceable to the exact training run, dataset version, and code commit that produced it. You need to ensure this lineage is automatically captured for every registered model without manual documentation. What should you do?

Question 61hardmultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You manage a managed online endpoint in Azure Machine Learning that serves a recommendation model. The model was trained months ago, and you now need to deploy a new version trained on recent data with zero downtime. You must be able to send a small percentage of live traffic to the new version to validate it before full rollout. What should you do?

Question 62easymultiple choice

Read the full ML Model Lifecycle And Operations explanation →

You are an MLOps engineer preparing to retrain a demand forecasting model on a schedule. The training data resides in an Azure Machine Learning datastore backed by Azure Blob Storage. You need the training job to run automatically every night at 02:00 UTC without manual intervention. What should you create?

Question 63mediummulti select

Read the full ML Model Lifecycle And Operations explanation →

You are responsible for monitoring a deployed model on an Azure Machine Learning managed online endpoint. The model's input feature distribution has drifted from the training baseline, and you need to detect this automatically and act on it. Which two actions should you take? (Choose two.)