The correct answer is A, Check the node/ephemeral_storage/used_bytes metric by using Metrics Explorer.
The node/ephemeral_storage/used_bytes metric reports the total amount of ephemeral storage used by Pods on each node1. You can use Metrics Explorer to query and visualize this metric and filter it by node name, namespace, or Pod name2. This way, you can identify which Pods are consuming the most ephemeral storage and causing disk pressure on the nodes. You do not need to have execute access to the workloads or nodes to use Metrics Explorer.
The other options are incorrect because they require execute access to the workloads or nodes, which you do not have. The df -h and du -sh * commands are Linux commands that can measure disk usage, but you need to run them inside the Pods or on the nodes, which is not possible in your scenario34.
Reference:
Monitoring metrics for Kubernetes system components, Node metrics, node/ephemeral_storage/used_bytes. Using Metrics Explorer, Querying metrics. How do I find out disk space utilization information using Linux command line?, df command. How to check disk space in Linux from the command line, du command.

The best option for ensuring that the warehouse system accurately reflects product inventory at the time orders are placed and minimizing the impact on customers is to increase the number of Pod replicas. Increasing the number of Pod replicas will increase the scalability and availability of your microservice, which will allow it to handle more Pub/Sub messages and update stock information faster. This way, you can reduce the backlog of undelivered messages and oldest unacknowledged message age, which are causing delays in updating product inventory. You can use Horizontal Pod Autoscaler or Cloud Monitoring metrics-based autoscaling to automatically adjust the number of Pod replicas based on load or custom metrics.

Explanation
The best option for ensuring continued operations until the microservice is fixed is to add a HTTP liveness probe to the microservice's deployment. A HTTP liveness probe is a type of probe that checks if a Pod is alive by sending an HTTP request and expecting a success response code. If the probe fails, Kubernetes will restart the Pod. You can add a HTTP liveness probe to your microservice's deployment by using a livenessProbe field in your Pod spec. This way, you can ensure that any Pod that returns 403 errors after running for more than five hours will be restarted automatically and resume normal operations.