The answer is to switch the output to Azure Cosmos DB with sufficient RU/s and use change feed to sync to SQL Database. This is correct because Azure SQL Database’s row-based storage and limited write throughput create a bottleneck when handling high-velocity IoT data from Stream Analytics, causing throttling and increased latency. By routing output to Cosmos DB, which offers scalable write throughput via Request Units per second (RU/s), the streaming job avoids throttling entirely, while the change feed asynchronously syncs data to SQL Database for reporting, effectively decoupling the write path. On the DP-900 exam, this scenario tests your understanding of output throttling reduction and the trade-offs between SQL Database’s transactional consistency and Cosmos DB’s high-throughput ingestion. A common trap is assuming increasing SQL Database DTUs alone will solve the issue, but the real bottleneck is row-level write contention. Remember the memory tip: “Cosmos for speed, SQL for reporting—change feed bridges the gap.”
DP-900 Describe an analytics workload on Azure Practice Question
This DP-900 practice question tests your understanding of describe an analytics workload on azure. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 uses Azure Stream Analytics to process IoT data from thousands of devices. The output is written to Azure SQL Database for reporting. Recently, the job latency increased significantly. The company suspects that the SQL Database is throttling writes. Which action should the company take to reduce latency?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Switch the output to Azure Cosmos DB with sufficient RU/s and use change feed to sync to SQL Database.
The correct answer is B because the latency is caused by Azure SQL Database throttling writes due to its row-based storage and limited write throughput. By switching the output to Azure Cosmos DB with sufficient Request Units per second (RU/s), the Stream Analytics job can write at high speed without throttling, and the change feed can then asynchronously sync data to Azure SQL Database for reporting, decoupling the write bottleneck.
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.
✗
Change the input serialization from JSON to Avro.
Why it's wrong here
Serialization format does not impact output write performance.
✓
Switch the output to Azure Cosmos DB with sufficient RU/s and use change feed to sync to SQL Database.
Why this is correct
Cosmos DB offers higher write throughput; change feed can asynchronously sync to SQL.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the batch size of writes to Azure SQL Database.
Why it's wrong here
Larger batches can increase lock contention and throttling.
✗
Increase the number of Streaming Units for the Stream Analytics job.
Why it's wrong here
More SU increases processing power but does not solve output throttling.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume increasing compute resources (Streaming Units) or batch sizes will fix any performance issue, but the real bottleneck is the output sink's write throttling, which requires a decoupled architecture like Cosmos DB with change feed.
Trap categories for this question
Command / output trap
Serialization format does not impact output write performance.
Detailed technical explanation
How to think about this question
Azure SQL Database uses a rowstore architecture and enforces resource governance based on DTU or vCore limits, which throttle write operations when the transaction log or IO exceeds thresholds. Azure Cosmos DB, in contrast, uses a distributed, schema-agnostic write-optimized engine that can absorb high-velocity writes with provisioned RU/s, and its change feed provides a reliable, ordered stream of inserts/updates that can be consumed by Azure Functions or Azure Data Factory to sync to SQL Database without impacting the real-time pipeline. In practice, this pattern is common for IoT scenarios where ingestion rates exceed 10,000 writes per second, as Cosmos DB can handle millions of RU/s while SQL Database typically caps at a few thousand writes per second per tier.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this DP-900 question in full detail.
Describe an analytics workload on Azure — This question tests Describe an analytics workload on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Switch the output to Azure Cosmos DB with sufficient RU/s and use change feed to sync to SQL Database. — The correct answer is B because the latency is caused by Azure SQL Database throttling writes due to its row-based storage and limited write throughput. By switching the output to Azure Cosmos DB with sufficient Request Units per second (RU/s), the Stream Analytics job can write at high speed without throttling, and the change feed can then asynchronously sync data to Azure SQL Database for reporting, decoupling the write bottleneck.
What should I do if I get this DP-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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