AZ-204 Practice Question: Durable Functions fan-out/fan-in for parallel…
A workflow must process 500 customer records in parallel and then aggregate all results into a single summary report. The team wants to use Azure Durable Functions so the orchestration state is durable and the solution can resume after a Function App restart. Which Durable Functions pattern matches this requirement?
⚠ Common exam trap
Test-takers frequently confuse the fan-out/fan-in pattern with the Async HTTP API pattern, thinking that the HTTP trigger and status polling are required for parallel processing, but the key distinction is that fan-out/fan-in handles the parallel execution and aggregation within the orchestrator itself, not via external polling.
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
✓
Fan-out/fan-in: start 500 activity functions in parallel with Task.WhenAll inside the orchestrator, then aggregate all returned results
The fan-out/fan-in pattern in Durable Functions is specifically designed to execute multiple activity functions in parallel using Task.WhenAll inside an orchestrator, then aggregate their results. This matches the requirement to process 500 customer records concurrently and produce a single summary report, while the orchestration state is durably persisted and can resume after a Function App restart.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Fan-out/fan-in: start 500 activity functions in parallel with Task.WhenAll inside the orchestrator, then aggregate all returned results
Why this is correct
Task.WhenAll fires all 500 activities simultaneously (constrained by the configured max concurrency). The orchestrator yields at the await statement, checkpointing its state. When all activities complete, the orchestrator resumes and aggregates results. Durable state management handles host restarts transparently.
- ✗
Function chaining: call each activity function sequentially, collecting each result before starting the next
Why it's wrong here
Function chaining executes activity functions sequentially, where the output of one activity typically becomes the input for the next. For a scenario involving 500 independent records, this pattern would process each record one at a time, completely serializing the workload. This approach offers no parallelism, making it highly inefficient and significantly extending the total execution time compared to concurrent processing methods. It is best suited for workflows where tasks have strict dependencies and must run in a specific order.
- ✗
Async HTTP API: start the workflow with an HTTP trigger, return a 202 with a status URL, and have the client poll for completion
Why it's wrong here
The Async HTTP API pattern is an interaction model for client-facing orchestrations. It describes how a client tracks workflow progress via polling — it is not a parallelism pattern. The fan-out/fan-in pattern describes how the orchestration itself processes records.
- ✗
Monitor: use a Durable timer loop that checks a status table every 60 seconds until all records are marked processed
Why it's wrong here
The Monitor pattern polls external state at intervals — it's suitable for tracking external processes. It does not initiate parallel processing. Using it to wait for 500 individually triggered activities to complete would be architecturally inverted and inefficient.
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