Implement machine learning model lifecycle and operations

You manage several registered models in an Azure Machine Learning workspace. A fraud-detection model has 12 versions, and versions 1 through 8 are obsolete and should no longer appear in default listings or be selectable for new deployments. However, compliance requires that these older versions remain retrievable for audit purposes and are not permanently deleted. What should you do?

You are training a classification model in Azure Machine Learning using a command job that logs a validation metric called 'val_auc' with MLflow. You want to automate hyperparameter tuning to maximize this metric across combinations of learning rate and batch size, sampling values randomly and stopping poorly performing trials early. Which approach should you use to configure the tuning job?

You maintain a fraud-detection model deployed to a managed online endpoint in Azure Machine Learning. A model monitor already computes prediction drift and feature-attribution drift on a recurring schedule. The business requires that when the monitored metric exceeds a defined threshold, a retraining pipeline is automatically kicked off without a data scientist manually reviewing the results first. Which approach best implements this automated retraining trigger?

You are configuring an automated machine learning classification job in Azure Machine Learning to predict fraudulent transactions. Only about 2% of the transactions in your training dataset are labeled as fraud. Your business goal is to correctly identify as many fraudulent transactions as possible while keeping false positives manageable. Which primary metric should you configure for the AutoML job to best guide model selection?

Your team has an existing Python training script that trains a scikit-learn regression model and logs metrics with MLflow. You need to run this script on an Azure Machine Learning compute cluster as a reproducible job, passing a registered data asset and a learning-rate argument, while ensuring the run appears in a specific experiment for later comparison. Which Azure Machine Learning job type should you configure?

You are running a hyperparameter tuning sweep job in Azure Machine Learning to optimize a deep learning model. The sweep uses random sampling over a large search space, and each trial is expensive to run on GPU compute. You want to automatically stop poorly performing trials early to reduce cost, while still allowing promising trials to complete. Which configuration should you add to the sweep job?

You are an ML engineer at a logistics company. A data scientist has trained a model and logged it with MLflow, and you now need to deploy it to a managed online endpoint for real-time scoring. The endpoint must serve two model versions simultaneously so you can compare responses before committing. You want to minimize the amount of custom code required for the deployment. What should you do?

Your team has a registered MLflow model that must score a nightly file drop of roughly 5 million records stored in a Blob datastore. Latency is not important, but the job must finish within a maintenance window and produce a single consolidated output file. You need to create the most cost-effective managed inference solution in Azure Machine Learning. What should you do?

You deploy an MLflow model to an Azure Machine Learning batch endpoint to score a folder of 500,000 small CSV files. The initial scoring job runs very slowly, and monitoring shows the compute cluster nodes are underutilized with frequent overhead between individual file reads. You need to improve throughput without changing the scoring logic. Which batch deployment setting should you adjust?

You are deploying a scikit-learn model to an Azure Machine Learning batch endpoint using a custom scoring script. During execution, the endpoint must process thousands of CSV files, and each mini-batch should return one row of predictions per input row. Which function in your scoring script is responsible for processing each mini-batch of files and returning the results?

Your team trained a demand-forecasting model that must score a 40 GB CSV file dropped into Azure Blob Storage every night. The job has no latency requirement and results are written back to a data lake for downstream reporting. You want to minimize compute cost by only provisioning resources when the scoring job runs. Which Azure Machine Learning deployment approach best fits this workload?

You are training a classification model in Azure Machine Learning using several experiment jobs, each logging accuracy, F1 score, and AUC through MLflow autologging. Your team lead asks you to identify which job produced the best-performing model and to visually compare the logged metrics side by side across all runs within the same experiment. Which approach should you use in the Azure Machine Learning studio?

Your team ran 15 separate training jobs in Azure Machine Learning, each logged to the same MLflow experiment with a logged metric named 'val_f1_score'. A stakeholder asks you to identify which single job produced the highest validation F1 score so it can be promoted for registration. You want the fastest approach that requires no re-execution of the jobs. What should you do?

You maintain an Azure Machine Learning training pipeline that trains a model and then registers it. The business requires that the model is only registered when its validation accuracy exceeds 0.85; otherwise the pipeline should skip registration and finish successfully without failing. You want to implement this within the pipeline itself using components. What is the most appropriate approach?

You operate a fraud-detection model on an Azure Machine Learning managed online endpoint. Confirmed fraud labels arrive weeks after scoring, so ground-truth accuracy cannot be evaluated in near real time. Leadership wants an early warning when the model's behavior shifts, without waiting for labels. Which monitoring signal should you configure in the Azure ML model monitor?

You are training a large deep learning model in Azure Machine Learning using a PyTorch script that already implements DistributedDataParallel. You want to run the job across 4 nodes with 4 GPUs each on a GPU compute cluster, and you need Azure Machine Learning to launch one process per GPU and set the correct environment variables (RANK, WORLD_SIZE, MASTER_ADDR) for the distributed process group. How should you configure the command job?

You are training a large deep learning model on Azure Machine Learning using a compute cluster of 8 GPU nodes. The model no longer fits into the memory of a single GPU, so you need to shard the model parameters, gradients, and optimizer states across all available GPUs to reduce per-device memory consumption. Which approach should you configure for the training job?

You deployed a model to a managed online endpoint in Azure Machine Learning. The deployment succeeded, but every scoring request returns an HTTP 424 error. You need to find the root cause quickly by inspecting the container's stdout/stderr output where the scoring script's init() and run() functions write their messages. Which action should you take first?

Your team registered an MLflow model that predicts loan approvals. Before deploying it to production, compliance requires you to evaluate whether the model behaves fairly across protected demographic groups such as age and gender. You want to generate this assessment inside your Azure Machine Learning workspace using a supported, low-code approach. Which action best meets this requirement?

You are running an automated machine learning (AutoML) classification job in Azure Machine Learning using the Python SDK v2. Your team wants to review every candidate model AutoML trained, including each model's hyperparameters and validation metrics, so they can audit and reproduce the best trial later. You need to identify where this information is captured with minimal additional configuration. Where should you look to retrieve the per-trial hyperparameters and metrics?