Azure ML Studio服务连接测试报错：缺失ContainerName元数据属性

在Azure ML Studio中配置服务连接并使用Microsoft Entra身份验证测试连接时，收到错误提示：

"Required metadata property ContainerName is missing"

但在Azure门户中直接访问存储账户下的容器文件时，Microsoft Entra身份验证可以正常工作：

目前不确定错误原因，怀疑可能是Azure Machine Learning Studio的Bug。相关资源通过以下Bicep模板部署，配置看似无误，但可能存在遗漏：

#disable-next-line no-unused-params
param busUnit string
#disable-next-line no-unused-params
param costCode string
#disable-next-line no-unused-params
param reg string
param env string
param project string
param appTags object
param keyVaultId string
param loc string

var addAppTags = {
}
var appTagsComb = union(appTags, addAppTags)

var envConfigMap = {
  sbx: {
    secGrpId: 'mysbxid'
  }
  dev: {
 
    secGrpId: 'mydevid'
  }
  ppd: {
  
    secGrpId: 'myppdid'
  }
  prd: {

    secGrpId: 'myprdid'
  }
}

resource logAnalyticsWorkspace 'Microsoft.OperationalInsights/workspaces@2023-09-01' = {
  name: 'log-${project}-${env}'
  location: loc
  properties: {
    sku: {
      name: 'PerGB2018'
    }
    retentionInDays: 90
    workspaceCapping: {
      dailyQuotaGb: 1
    }
  }
}

resource appInsights 'Microsoft.Insights/components@2020-02-02' = {
  name: 'appi-${project}-${env}'
  location: loc
  kind: 'web'
  properties: {
    Application_Type: 'web'
    WorkspaceResourceId: logAnalyticsWorkspace.id
  }
  tags: appTagsComb
}

output MlIdentity string = machineLearning.identity.principalId

resource containerRegistry 'Microsoft.ContainerRegistry/registries@2023-07-01' = {
  #disable-next-line BCP334
  name: 'cr${project}${env}'
  location: loc
  tags: appTagsComb
  sku: {
    name: 'Basic'
  }
  properties: {
    adminUserEnabled: true
  }
}

resource storage 'Microsoft.Storage/storageAccounts@2023-04-01' = {
  #disable-next-line BCP334
  name: 'st${project}${env}'
  location: loc
  sku: {
    name: 'Standard_LRS'
  }
  kind: 'StorageV2'
  properties: {
    allowBlobPublicAccess: false
    supportsHttpsTrafficOnly: true
    minimumTlsVersion: 'TLS1_2'
    isHnsEnabled: false
    encryption: {
      services: {
        file: {
          keyType: 'Account'
          enabled: true
        }
        blob: {
          keyType: 'Account'
          enabled: true
        }
      }
      keySource: 'Microsoft.Storage'
    }
    accessTier: 'Hot'
  }
  tags: appTags
}

resource blobServices 'Microsoft.Storage/storageAccounts/blobServices@2023-04-01' = {
  parent: storage
  name: 'default'
  properties: {
    cors: {
      corsRules: []
    }
    deleteRetentionPolicy: {
      enabled: true
      days: 7
    }
  }
}

resource containers 'Microsoft.Storage/storageAccounts/blobServices/containers@2021-06-01' = {
  parent: blobServices
  #disable-next-line BCP334
  name: 'bc${project}${env}'
  properties: {
    publicAccess: 'None'
  
  }
}

resource machineLearning 'Microsoft.MachineLearningServices/workspaces@2024-04-01' = {
  name: 'mlw-${project}-${env}'
  location: loc
  tags: appTagsComb
  identity: {
    type: 'SystemAssigned'
  }
  properties: {
    applicationInsights: appInsights.id
    containerRegistry: containerRegistry.id
    keyVault: keyVaultId
    storageAccount: storage.id
  }
}

resource amlci 'Microsoft.MachineLearningServices/workspaces/computes@2024-04-01' = {
  name: 'mlw-${project}-${env}-cluster'
  parent: machineLearning
  location: loc
  identity: {
    type: 'SystemAssigned'
  }
  properties: {
    computeType: 'AmlCompute'
    properties: {
      vmSize: 'Standard_DS3_v2'
      subnet: null
      osType: 'Linux'
      scaleSettings: {
        maxNodeCount: 5
        minNodeCount: 0
      }
    }
  }
}

resource amlds 'Microsoft.MachineLearningServices/workspaces/datastores@2023-04-01' = {
  name: 'mlw${project}${env}datastore'
  parent: machineLearning
  properties: {
    credentials: {
      credentialsType: 'None'
    }
    datastoreType: 'AzureBlob'
    accountName: storage.name
    containerName: containers.name
    endpoint: 'core.windows.net'
    protocol: 'https'
    serviceDataAccessAuthIdentity: 'WorkspaceSystemAssignedIdentity'
  }
}

resource containerGroup 'Microsoft.ContainerInstance/containerGroups@2023-05-01' = {
  name: 'ci${project}${env}'
  location: loc
  properties: {
    containers: [
      {
        name: 'ci${project}${env}'
        properties: {
          image: 'mcr.microsoft.com/azuredocs/aci-helloworld'
          ports: [
            {
              port: 80
              protocol: 'TCP'
            }
          ]
          resources: {
            requests: {
              cpu: 1
              memoryInGB: 2
            }
          }
        }
      }
    ]
    osType: 'Linux'
    restartPolicy: 'Always'
    ipAddress: {
      type: 'Public'
      ports: [
        {
          port: 80
          protocol: 'TCP'
        }
      ]
    }
  }
}

@description('This is the built-in azureml data scientist role.')
resource AzureMLDataScientistRoleDefinition 'Microsoft.Authorization/roleDefinitions@2022-04-01' existing = {
  scope: resourceGroup()
  name: 'f6c7c914-8db3-469d-8ca1-694a8f32e121'
}

@description('This is the built-in Storage Blob Data Contributor role.')
resource StorageBlobDataContributorRoleDefinition 'Microsoft.Authorization/roleDefinitions@2022-04-01' existing = {
  scope: resourceGroup()
  name: 'ba92f5b4-2d11-453d-a403-e96b0029c9fe'
}

resource AzureMLDataScientistRoleAssignment 'Microsoft.Authorization/roleAssignments@2022-04-01' = {
  name: guid(machineLearning.id, AzureMLDataScientistRoleDefinition.id)
  properties: {
    roleDefinitionId: AzureMLDataScientistRoleDefinition.id
    principalId: envConfigMap[env].secGrpId
    principalType: 'Group'
  }
  scope: machineLearning
}

resource StorageBlobDataContributorRoleAssignment 'Microsoft.Authorization/roleAssignments@2022-04-01' = {
  name: guid(storage.id, StorageBlobDataContributorRoleDefinition.id)
  properties: {
    roleDefinitionId: StorageBlobDataContributorRoleDefinition.id
    principalId: envConfigMap[env].secGrpId
    principalType: 'Group'
  }
  scope: storage
}

我尝试给容器资源添加以下metadata参数，但错误依旧：

metadata: {
      ContainerName: 'bc${project}${env}'
    }

内容的提问来源于stack exchange，提问作者Muhammad Pathan