DP-203 Practice Question: Secure, monitor, and optimize data storage and data processing
A company uses Azure Stream Analytics to process real-time data from IoT devices. They need to ensure that the output to Azure Synapse Analytics is optimized for high throughput and low latency. What should they configure in the Stream Analytics job?
⚠ Common exam trap
DP-203 often tests the misconception that disabling batching reduces latency, but in reality, it increases overhead and reduces throughput; batching is crucial for efficient bulk writes.
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
✓
Partition the output by a key and use a columnstore index in the target table.
Partitioning the output by a key and using a columnstore index in the target Synapse table optimizes for high throughput and low latency. Partitioning distributes the write load across multiple nodes, while columnstore indexes provide high compression and fast query performance for analytical workloads. This combination is recommended for Stream Analytics to Synapse ingestion at scale.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Azure SQL Database output instead of Azure Synapse Analytics.
Why it's wrong here
Azure SQL Database cannot ingest the streaming volumes Synapse dedicated SQL pools handle, and the stem mandates Synapse as the target, so swapping sinks abandons the requirement rather than optimising it. It tempts when the workload is small-scale transactional writes, where Azure SQL Database is genuinely the right sink.
- ✓
Partition the output by a key and use a columnstore index in the target table.
Why this is correct
Columnstore compression plus partitioning the output by a key lets Stream Analytics write in parallel batches, raising throughput and cutting latency into Synapse. This directly satisfies the stem's high-throughput, low-latency constraint by avoiding row-by-row inserts and single-writer bottlenecks.
- ✗
Use a single partition for the output to simplify processing.
Why it's wrong here
A single partition serialises writes and caps parallelism, so Synapse cannot absorb the stream at high throughput. Partitioning spreads load across compute nodes; one partition suits tiny, ordered workloads where strict sequence matters more than scale.
- ✗
Disable batching to reduce latency.
Why it's wrong here
Disabling batching forces row-by-row writes, cutting throughput and inflating transaction overhead against Synapse, directly contradicting the high-throughput requirement. Batching exists precisely to amortise commit costs; it would be correct only when each event must land individually with minimal delay and volume is low.
Go deeper
Related to this question
Learn chapter
Implement Azure Synapse Analytics
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.
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.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Microsoft exam blueprint
This DP-203 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DP-203 exam.