The answer is that the job is under-provisioned in terms of Streaming Units (SUs). This is the most likely cause because a rising watermark delay directly indicates backpressure: the Stream Analytics job cannot process incoming events as fast as they arrive from Event Hubs, especially with 10 partitions feeding a 5-minute hopping window that recomputes every minute. When SUs are insufficient, the engine struggles to keep up with the compute load, causing the watermark—which tracks the latest processed event time—to fall further behind real time. On the DP-203 exam, this scenario tests your understanding of Stream Analytics throughput and scaling; a common trap is to blame partitioning or window size, but the key clue is the *increasing* delay over time, not a static lag. Remember the memory tip: “Rising watermark? Check your SU count first.”
DP-203 Practice Question: Monitor and optimize data storage and processing
This DP-203 practice question tests your understanding of monitor and optimize data storage and processing. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A data engineering team uses Azure Stream Analytics to process real-time IoT data. They notice that the job's watermark delay is increasing over time, and the output is falling behind. The input is from Event Hubs with 10 partitions. The job uses a 5-minute hopping window with a 1-minute hop. What is the most likely cause?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The job is under-provisioned in terms of Streaming Units (SUs).
The increasing watermark delay and falling behind output indicate that the Stream Analytics job cannot keep up with the input throughput. With a 5-minute hopping window (1-minute hop) processing 10 Event Hubs partitions, the job requires sufficient Streaming Units (SUs) to handle the compute load. Under-provisioned SUs cause backpressure, leading to rising watermark delay as the job struggles to process events within the window boundaries.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
The hopping window size is too large.
Why it's wrong here
Larger windows reduce processing frequency, not cause delay.
✗
The late arrival tolerance is set too high.
Why it's wrong here
Late arrival tolerance does not affect throughput.
✓
The job is under-provisioned in terms of Streaming Units (SUs).
Why this is correct
Low SUs cause backpressure, increasing watermark delay.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
The Event Hubs partition count does not match the Stream Analytics job's parallelism.
Why it's wrong here
Partition count mismatch can cause issues, but the job can still handle input with enough SUs.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse watermark delay with configuration issues like window size or late arrival tolerance, but the progressive nature of the delay points directly to resource starvation (SU under-provisioning) rather than a static configuration problem.
Detailed technical explanation
How to think about this question
Under the hood, Stream Analytics uses a distributed processing engine where each SU (1 SU = 1 MB/s throughput) handles a subset of partitions. When SUs are under-provisioned, the job's internal checkpointing and watermark progression stall because the engine cannot commit offsets fast enough. In real-world scenarios, a common mistake is to assume that 10 partitions require only 1 SU, but each SU can only process about 1 MB/s, so high-throughput IoT data (e.g., 10 MB/s) would need at least 10 SUs to avoid backpressure.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this DP-203 question in full detail.
Monitor and optimize data storage and processing — This question tests Monitor and optimize data storage and processing — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The job is under-provisioned in terms of Streaming Units (SUs). — The increasing watermark delay and falling behind output indicate that the Stream Analytics job cannot keep up with the input throughput. With a 5-minute hopping window (1-minute hop) processing 10 Event Hubs partitions, the job requires sufficient Streaming Units (SUs) to handle the compute load. Under-provisioned SUs cause backpressure, leading to rising watermark delay as the job struggles to process events within the window boundaries.
What should I do if I get this DP-203 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data engineer monitors an Azure Stream Analytics job that processes real-time data. The job is falling behind, and the SU utilization is at 100%. Which action should be taken to improve performance?
easy
✓ A.Increase the number of Streaming Units (SU).
B.Reduce the number of Streaming Units.
C.Change the query compatibility level to 1.0.
D.Deploy a second Stream Analytics job and split the input.
Why A: When SU utilization reaches 100%, the job is fully saturated and cannot process incoming data fast enough. Increasing the number of Streaming Units (SU) allocates more compute resources (CPU and memory) to the job, allowing it to handle higher throughput and reduce backlog. This is the direct and recommended action for resolving performance bottlenecks caused by insufficient SU capacity.
Last reviewed: Jun 11, 2026
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