Cloud Composer 2 Airflow tasks are failing with high total worker memory usage and worker pod evictions. How can you resolve this? (Pick two.)

High worker memory usage and pod evictions indicate that the Airflow workers are running out of memory. Increasing the maximum number of workers distributes the workload across more pods, reducing the memory burden on individual workers. Reducing worker concurrency means each worker handles fewer tasks simultaneously, further decreasing its memory footprint. This combination directly addresses the memory exhaustion and eviction issues. Increasing the Cloud Composer 2 environment size (e.g., medium to large) would provide more resources overall, but it's a broader solution that might be overkill if the issue is specifically worker memory management. Increasing the DAG file parsing interval is unrelated to worker memory issues; it affects how often DAGs are scanned for updates. Increasing memory for the Airflow triggerer is also irrelevant, as triggerers manage deferred tasks, not the execution of active tasks by workers.