The answer is that the Azure SQL Database target table lacks proper indexes, which is the most likely cause of increasing output latency and eventual timeout failures in Stream Analytics. Without indexes, each batch of output forces full table scans for inserts or updates, creating a growing backlog that exceeds the job’s default five-minute output timeout. This scenario tests your understanding of how downstream database performance directly impacts streaming pipeline throughput on the DP-203 exam, often appearing as a trap where candidates mistakenly blame the Stream Analytics job configuration or SQL DTU limits. A key insight is that Stream Analytics writes in micro-batches, so even a small indexing deficiency compounds over time. Remember the memory tip: “No index, no speed—your SQL will bleed.”
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 company runs a streaming pipeline using Azure Stream Analytics to ingest IoT data and output to Azure SQL Database. They notice that the output latency increases over time and eventually the job fails with a timeout error. 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 Azure SQL Database target table lacks proper indexes.
The most likely cause is that the Azure SQL Database target table lacks proper indexes. Without indexes, each batch of output from Stream Analytics triggers full table scans for inserts or updates, causing cumulative latency. Over time, the backlog exceeds the job's timeout threshold (default 5 minutes for output), leading to failure.
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 Stream Analytics job has a high late arrival tolerance.
Why it's wrong here
Late arrival tolerance affects event ordering but does not cause output failures.
✗
The event hub is not partitioned correctly.
Why it's wrong here
Incorrect partitioning may affect ordering but not cause output latency or timeout.
✗
The event hub consumer group is misconfigured.
Why it's wrong here
Misconfigured consumer groups can cause data loss but not output latency.
✓
The Azure SQL Database target table lacks proper indexes.
Why this is correct
Missing indexes slow down write operations, causing backpressure and eventual timeout.
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often attribute output latency to input-side issues like partitioning or consumer groups, but the symptom of increasing latency over time points to a downstream bottleneck, specifically missing indexes on the SQL target table.
Trap categories for this question
Command / output trap
Late arrival tolerance affects event ordering but does not cause output failures.
Detailed technical explanation
How to think about this question
Stream Analytics uses batch writes to SQL Database, and without indexes, each batch triggers table scans that degrade exponentially as table size grows. The default batch size is 10,000 rows or 1 MB, and the timeout is 5 minutes; once the write duration exceeds this, the job fails. In production, adding a clustered index on the target table reduces write latency by over 90% and prevents this failure pattern.
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
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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 Azure SQL Database target table lacks proper indexes. — The most likely cause is that the Azure SQL Database target table lacks proper indexes. Without indexes, each batch of output from Stream Analytics triggers full table scans for inserts or updates, causing cumulative latency. Over time, the backlog exceeds the job's timeout threshold (default 5 minutes for output), leading to failure.
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.
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Question Discussion
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