In a GCP Dataflow job, you notice that a specific ParDo operation is taking a considerable amount of time, causing a bottleneck. Which strategy could effectively alleviate this issue?

*Explanation:* To address the bottleneck caused by a slow ParDo operation in a GCP Dataflow job, an effective strategy is to **implement dynamic work rebalancing in your ParDo operation**. This allows for the efficient distribution of workloads and can alleviate performance issues. Increasing the disk size of Dataflow workers won't significantly impact ParDo performance. Changing the programming language is unlikely to resolve the bottleneck. Streaming Engine is for streaming jobs, not for optimizing ParDo. Adding more Cloud Dataflow Shuffle partitions is specific to data distribution tasks and may not directly alleviate ParDo bottlenecks.