- A
In the function code, increase the number of retries on failure
Why wrong: Wrong: Retries do not affect scaling.
- B
In host.json, increase the batchSize and increase the newBatchThreshold
Correct: Larger batchSize means each function instance grabs more messages, and higher newBatchThreshold triggers scale-out sooner.
- C
In host.json, increase the batchSize and decrease the newBatchThreshold
Why wrong: Wrong: Decreasing newBatchThreshold would cause scaling to happen sooner, but the combination with increasing batchSize may not be optimal; actually increasing newBatchThreshold delays scale-out.
- D
Switch from Consumption plan to Premium plan
Why wrong: Wrong: Premium plan may help, but the question asks for a configuration change; switching plans is more drastic and not directly addressing the scaling threshold.
Quick Answer
The answer is to increase both the batchSize and newBatchThreshold in the host.json file. This is correct because the batchSize controls how many messages the Azure Functions runtime retrieves from the queue in a single polling cycle, while the newBatchThreshold determines the minimum number of messages that must be processed before the runtime spins up additional parallel function instances. By raising both values, you reduce the latency between message arrival and processing, allowing the function to scale out faster under high load—directly addressing the scaling bottleneck revealed in your load tests. On the AZ-204 exam, this scenario tests your understanding of Azure Storage queue trigger scaling behavior, often appearing as a distractor where candidates mistakenly adjust the function app’s plan or concurrency settings instead. A common trap is thinking you should decrease batchSize to reduce load, but that actually slows scaling. Memory tip: “Bigger batches, bigger threshold—faster scaling, no threshold.”
AZ-204 Develop Azure compute solutions Practice Question
This AZ-204 practice question tests your understanding of develop azure compute solutions. 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.
You have an Azure Function app that processes orders. The function uses a queue trigger from Azure Storage. Recent load tests show that the function is not scaling out fast enough under high load. What should you do to improve scaling?
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
In host.json, increase the batchSize and increase the newBatchThreshold
Option B is correct because increasing the batchSize and newBatchThreshold in host.json allows the Azure Functions runtime to fetch more messages per polling cycle and trigger more parallel function instances sooner. This directly improves scaling velocity under high load by reducing the latency between message arrival and processing, which is the core issue identified in the load test.
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.
- ✗
In the function code, increase the number of retries on failure
Why it's wrong here
Wrong: Retries do not affect scaling.
- ✓
In host.json, increase the batchSize and increase the newBatchThreshold
Why this is correct
Correct: Larger batchSize means each function instance grabs more messages, and higher newBatchThreshold triggers scale-out sooner.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
In host.json, increase the batchSize and decrease the newBatchThreshold
Why it's wrong here
Wrong: Decreasing newBatchThreshold would cause scaling to happen sooner, but the combination with increasing batchSize may not be optimal; actually increasing newBatchThreshold delays scale-out.
- ✗
Switch from Consumption plan to Premium plan
Why it's wrong here
Wrong: Premium plan may help, but the question asks for a configuration change; switching plans is more drastic and not directly addressing the scaling threshold.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse scaling improvements with plan upgrades (Premium vs. Consumption) or retry logic, when the real solution lies in tuning the queue trigger's batch processing parameters in host.json.
Detailed technical explanation
How to think about this question
The queue trigger uses a polling loop that checks the queue for messages. The batchSize controls how many messages are dequeued in a single poll, while newBatchThreshold determines the minimum number of messages that must remain in the current batch before a new batch is fetched. Increasing both values allows the runtime to process more messages concurrently and reduces idle polling cycles, which is critical for bursty workloads. In real-world scenarios, misconfigured batchSize and newBatchThreshold are common causes of poor scaling in queue-triggered functions, especially when messages arrive in large spikes.
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.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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Develop Azure compute solutions — study guide chapter
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FAQ
Questions learners often ask
What does this AZ-204 question test?
Develop Azure compute solutions — This question tests Develop Azure compute solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: In host.json, increase the batchSize and increase the newBatchThreshold — Option B is correct because increasing the batchSize and newBatchThreshold in host.json allows the Azure Functions runtime to fetch more messages per polling cycle and trigger more parallel function instances sooner. This directly improves scaling velocity under high load by reducing the latency between message arrival and processing, which is the core issue identified in the load test.
What should I do if I get this AZ-204 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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Last reviewed: Jun 24, 2026
This AZ-204 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 AZ-204 exam.
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