Question 1mediummultiple choice
Read the full Collaborating within and across teams to manage data and models explanation →PMLE Collaborating within and across teams to manage data and models • Complete Question Bank
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Refer to the exhibit.
```
{
"bindings": [
{
"role": "roles/aiplatform.user",
"members": [
"user:alice@example.com",
"group:data-scientists@example.com"
]
},
{
"role": "roles/aiplatform.customCodeServiceAgent",
"members": [
"serviceAccount:vertex-ai@project.iam.gserviceaccount.com"
]
}
],
"etag": "BwXahRc1X3w="
}
```Drag steps to the numbered slots on the right, or tap a step then tap a slot.
Drag a concept onto its matching description — or click a concept then click the description.
Production ML pipeline framework by Google
ML toolkit for Kubernetes-based workflows
Unified stream and batch data processing service
Managed Apache Airflow workflow orchestration
Serverless ML pipeline orchestration on Vertex AI
{
"bindings": [
{
"role": "roles/aiplatform.user",
"members": [
"user:data-scientist@example.com"
],
"condition": {
"title": "prefix_condition",
"expression": "resource.name.startsWith('projects/project-id/locations/us-central1/models/dev-')"
}
}
]
}components:
- name: evaluate
container: gcr.io/my-project/evaluate:latest
outputs:
metric: {type: double}
- name: gate
type: condition
inputs:
metric: ${{ evaluate.outputs.metric }}
condition: ${{ inputs.metric }} > 0.8
actions:
- deploy
- name: deploy
container: gcr.io/my-project/deploy:latest
dependsOn: gate