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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'), '/MyManagedVNetIR')]",
            "properties": {
                "type": "Managed",
                "typeProperties": {
                    "computeProperties": {
                        "location": "AutoResolve",
                        "dataFlowProperties": {
                            "computeType": "General",
                            "coreCount": 8,
                            "timeToLive": 10
                        }
                    }
                }
            }
        }
    ]
}

Refer to the exhibit. You are deploying an Azure Synapse Analytics workspace using an ARM template. The template defines a managed virtual network integration runtime. You need to ensure that the integration runtime can run mapping data flows with a time-to-live (TTL) of 10 minutes. What is the purpose of the 'timeToLive' property in this configuration?

⚠ Common exam trap

Many candidates confuse 'timeToLive' with activity timeout or concurrency limits, because all three involve time or capacity constraints, but TTL specifically governs cluster reuse after a data flow completes, not execution duration or parallelism.

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

✓

It defines how long the cluster will be kept alive after a data flow completes, allowing subsequent data flows to reuse the cluster.

The 'timeToLive' property in an Azure Synapse Analytics managed virtual network integration runtime controls how long the cluster remains alive after a mapping data flow completes. By setting a TTL of 10 minutes, subsequent data flows can reuse the same warm cluster, avoiding the 5–10 minute cold start time for new clusters. This optimizes performance and reduces latency for consecutive data flow executions.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✓
It defines how long the cluster will be kept alive after a data flow completes, allowing subsequent data flows to reuse the cluster.
Why this is correct
The timeToLive property sets the idle period before the data flow cluster shuts down. With 10 minutes configured, the cluster persists after a flow completes, letting subsequent mapping data flows reuse the warm cluster and avoid cold-start provisioning delays.
✗
It sets the timeout for the integration runtime to connect to the data sources.
Why it's wrong here
The property controls how long an idle data flow cluster stays alive before shutting down, not connection timeouts to sources. It is tempting because TTL sounds like a timeout, and a connection timeout setting would be correct when sources are unreachable within a defined window.
✗
It specifies the maximum duration a data flow activity can run before timing out.
Why it's wrong here
Activity timeout is a separate setting on the data flow activity; TTL instead keeps the cluster warm between runs. It is tempting because both involve durations, and an activity timeout would be correct when a single data flow must be stopped after a fixed period.
✗
It determines the maximum number of concurrent data flows that can run on the cluster.
Why it's wrong here
Concurrency is governed by cluster size and compute configuration, not TTL, which only governs idle cluster retention. It is tempting because both settings affect data flow execution, and a concurrency limit would be correct when parallel activity execution must be capped.

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DP-203 Develop data processing Practice Question

Exhibit

Why each option matters

Option-by-option breakdown

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