DP-203 Develop data processing Practice Question
Exhibit
Refer to the exhibit.
{
"$schema": "https://schema.management.azure.com/schemas/2019-04-01/deploymentTemplate.json#",
"contentVersion": "1.0.0.0",
"resources": [
{
"type": "Microsoft.Synapse/workspaces/integrationRuntimes",
"apiVersion": "2021-06-01-preview",
"name": "[concat(parameters('workspaceName'), '/AutoResolveIntegrationRuntime')]",
"properties": {
"type": "Managed",
"typeProperties": {
"computeProperties": {
"location": "AutoResolve",
"dataFlowProperties": {
"computeType": "General",
"coreCount": 8,
"timeToLive": 10
},
"pipelineExternalComputeScaleProperties": {
"numberOfExternalNodes": 1,
"numberOfPipelineNodes": 1
}
}
}
}
}
]
}You are deploying an Azure Synapse workspace using an ARM template. The template includes a Managed integration runtime with 'AutoResolve' location and a TTL of 10 minutes for data flows. After deployment, you notice that the first data flow execution takes a long time to start. What is the most likely cause?
⚠ Common exam trap
The trap here is that candidates may attribute the slow first execution to insufficient compute resources (Option A) rather than recognizing that the TTL setting directly controls cluster reuse and cold-start latency.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The TTL setting is too low, causing the cluster to be recreated frequently.
The first data flow execution takes a long time because the TTL (time-to-live) setting of 10 minutes causes the cluster to be deallocated shortly after the previous run. When a new data flow starts after the TTL expires, a new cluster must be provisioned from scratch, which adds significant startup latency. A higher TTL (e.g., 60 minutes) would keep the cluster warm for subsequent executions, reducing cold-start delays.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The core count of 8 is insufficient for the data flow.
Why it's wrong here
8 cores are typical.
- ✓
The TTL setting is too low, causing the cluster to be recreated frequently.
Why this is correct
Low TTL leads to frequent cluster teardown and startup delays.
- ✗
The AutoResolve location cannot be used for Managed IR.
Why it's wrong here
AutoResolve is a valid location.
- ✗
The integration runtime type should be 'Self-Hosted' for data flows.
Why it's wrong here
Managed IR supports data flows.
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