The correct answer is E. Both B and D. Here's why:

• mlflow.log_artifact() (Option B) is designed for logging files, models, or images, not specifically for parameters, metrics, or tags.
• mlflow.log_metric() (Option D) logs numerical metrics like accuracy or loss, indicating model performance.
• mlflow.start_run() (Option C) initiates a new MLflow run but doesn't log parameters, metrics, or tags directly.
• mlflow.set_tag() (Option A) assigns tags as key-value pairs for run organization.

Given the question's focus on storing parameters, metrics, and tags, the best choice combines mlflow.log_metric() for metrics and mlflow.set_tag() for tags. Note that parameters are usually logged with mlflow.log_param(), not listed here, making E the optimal answer among the provided options.