Keda is automatically autoscaling for max replicacount

[Tejesh-73J]

im using keda in my service it is automatically performing HPA to max replica count and here is my yaml configuration

Spec:
  Failed Jobs History Limit:  3
  Job Target Ref:
    Parallelism:  1
    Template:
      Metadata:
        Creation Timestamp:  <nil>
      Spec:
        Containers:
          Command:
            python3.8
            startAnalysisFromKafka.py
            --topic
            2
          Env:
            Name:             profile
            Value:            dev
          Image:              751394677851.dkr.ecr.ap-south-1.amazonaws.com/ecg-analysis-service-mvm2:tejesh_diff-kafka-queues_2024-04-23-03H-46M_2c94726
          Image Pull Policy:  IfNotPresent
          Name:               job-2-day-ecg-analysis-service-mvm2
          Resources:
            Limits:
              Cpu:     6
              Memory:  32Gi
            Requests:
              Cpu:     350m
              Memory:  8Gi
        Node Selector:
          Ng:                    Dev-Ecg-Analysis
        Restart Policy:          OnFailure
    Ttl Seconds After Finished:  100
  Max Replica Count:             2
  Min Replica Count:             0
  Polling Interval:              60
  Rollout:
    Propagation Policy:  foreground
    Strategy:            gradual
  Scaling Strategy:
    Strategy:                     accurate
  Successful Jobs History Limit:  1
  Triggers:
    Metadata:
      Activation Lag Threshold:  0
      Bootstrap Servers:         kafka.dev.vigolocal:9092
      Consumer Group:            ecg_analysis_consumer_2_Day
      Lag Threshold:             1
      Offset Reset Policy:       latest
      Topic:                     ecgAnalysis_2_days
    Type:                        kafka

expected behaviour : it should do HPA when the message arives in the kafka, i have set the lag to 1 and scalign strategy to accurate so that the scaling happens only when the message is arrived