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
This may improve parallelism but not necessarily reduce latency if the job is under-provisioned.
- ✓
Increase the number of Streaming Units (SUs) for the Stream Analytics job.
Why this is correct
More SUs provide more compute power to process events faster.
- ✗
Replace the output with Azure Functions for each event.
Why it's wrong here
Azure Functions may add latency per call.
- ✗
Change the input to a reference data input.
Why it's wrong here
Reference data is static and not suitable for streaming 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
Learn chapter
Introduction to Azure Data Engineering
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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