Secure, monitor, and optimize data storage and data processing →mediumMultiple ChoiceObjective-mapped
DP-203 Practice Question: Secure, monitor, and optimize data storage and data processing
Your team has deployed an Azure Stream Analytics job that reads from an Event Hubs input and writes to Azure Synapse Analytics. The job is falling behind, causing a growing backlog in Event Hubs. You have already scaled the Stream Analytics job to maximum streaming units. What should you do to improve throughput?
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 input Event Hubs and the output Synapse table, and adjust the Stream Analytics query to use PARTITION BY
Partitioning the input Event Hubs and the output Synapse table, and using PARTITION BY in the query, increases parallelism and allows the Stream Analytics job to process more data simultaneously. Option A is incorrect because the job is already at maximum streaming units. Option B is incorrect because a late arrival window handles out-of-order events, not throughput issues. Option C is incorrect because while increasing Event Hubs throughput units could help if Event Hubs is the bottleneck, the most likely bottleneck is the output sink (Azure Synapse Analytics), and partitioning the output is a more direct solution.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the streaming units further
Why it's wrong here
The job is already at maximum streaming units, cannot scale further.
- ✗
Configure a late arrival window to drop late events
Why it's wrong here
Late arrival window handles out-of-order events but does not increase throughput.
- ✗
Increase the throughput units of the Event Hubs namespace
Why it's wrong here
Event Hubs may not be the bottleneck; the job's processing capacity is the issue.
- ✓
Partition the input Event Hubs and the output Synapse table, and adjust the Stream Analytics query to use PARTITION BY
Why this is correct
Partitioning allows Stream Analytics to process data in parallel, increasing throughput.
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Related to this question
Learn chapter
Introduction to Azure Data Engineering
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 by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.