DP-203 Develop data processing Practice Question
You have a streaming pipeline using Azure Stream Analytics that ingests data from Event Hubs and outputs to Azure Synapse Analytics. The job has a high watermark delay and is falling behind. You need to reduce the latency. Which action should you take?
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
✓
Increase the number of Streaming Units (SUs) for the Stream Analytics job.
Increasing the number of Streaming Units (SUs) for the Stream Analytics job allocates more compute resources, reducing latency. Adding more Event Hubs partitions may improve throughput but not directly reduce latency if the job is already bottlenecked. Switching to reference data input does not help. Using Azure Functions for output may add overhead.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Add more partitions to the Event Hubs.
Why it's wrong here
Event Hubs partitions are fixed at creation; adding partitions requires recreating the namespace or event hub, and partition count governs consumer parallelism, not the Stream Analytics job's own processing latency. Partition scaling suits provisioning a new hub for higher ingest throughput.
- ✓
Increase the number of Streaming Units (SUs) for the Stream Analytics job.
Why this is correct
Streaming Units provision compute and throughput for the job; insufficient SUs cause the watermark delay to grow as the job falls behind. Increasing SUs adds parallel processing capacity, directly reducing latency, whereas partitioning or query changes alone may not resolve resource starvation.
- ✗
Replace the output with Azure Functions for each event.
Why it's wrong here
Per-event Azure Functions invocation adds per-call overhead and cannot match Stream Analytics throughput, worsening the watermark delay rather than reducing it. Functions suit event-driven processing or orchestration, not high-volume streaming sinks. Increasing streaming units or partitioning the output restores throughput.
- ✗
Change the input to a reference data input.
Why it's wrong here
Reference data is static lookup data joined to the stream, so switching the Event Hubs input to reference data removes the streaming source entirely and cannot lower watermark delay. Reference inputs suit enriching events with slowly changing lookup tables, not high-throughput ingestion.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
Go deeper
Related to this question
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Develop Stream Processing Solutions
Key term
Azure Stream Analytics
Azure Stream Analytics is a fully managed, real-time data processing service that analyzes and transforms high volumes of streaming data from various sources to deliver low-latency insights and trigger actions.
Key term
Azure Synapse Analytics
Azure Synapse Analytics is a cloud-based data integration, warehousing, and analytics service that brings together big data and data warehouse capabilities under one platform.
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