The correct approach is to create an Azure Machine Learning sweep job that runs multiple training trials with different hyperparameter combinations. Microsoft documents sweep jobs as the standard SDK v2 mechanism for automated hyperparameter tuning. A sweep job defines a search space for the tunable parameters, selects a sampling algorithm such as random, grid, or Bayesian sampling, identifies a primary optimization metric, and executes multiple child trials automatically.
Each trial receives a different combination of values sampled from the configured search space. This allows the same underlying training command or script to be reused while Azure Machine Learning supplies different hyperparameter values for each run. The number of trials and the degree of parallelism can also be controlled through settings such as max_total_trials and max_concurrent_trials.
Option A performs no tuning because only one fixed configuration is evaluated. Option B changes parameters too late because hyperparameter tuning belongs to the training and validation lifecycle, not post-deployment operation. Option C relies on defaults and does not explore alternative configurations.
Therefore, the required solution is D: Create a tuning job that runs multiple trials with different parameter values.
Study Guide Reference: Implement machine learning model lifecycle and operations — hyperparameter tuning, sweep jobs, search spaces, sampling algorithms, trial execution, and experiment optimization.
