The correct answer is to configure scale-out when CPU exceeds 80% and scale-in when CPU drops below 30%. This directly satisfies the requirement for aggressive scaling above 80% to rapidly add instances under heavy load, and conservative scaling below 30% to avoid premature removal of instances, which prevents the thrashing that occurs when scale-in thresholds are set too close to scale-out thresholds. On the Microsoft Azure Developer Associate AZ-204 exam, this scenario tests your understanding of VMSS autoscale rules and CPU thresholds, specifically how to balance responsiveness with stability. A common trap is selecting symmetric thresholds like 70/30 or 80/40, which can cause frequent scaling oscillations; the key is that aggressive scale-out demands a high upper threshold, while conservative scale-in requires a low lower threshold. Remember the memory tip: “80 to grow, 30 to go”—high CPU triggers growth, low CPU triggers a slow exit.
AZ-204 Develop Azure compute solutions Practice Question
This AZ-204 practice question tests your understanding of develop azure compute solutions. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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.
Your company has a multi-tier application running on Azure Virtual Machines. The application experiences high CPU usage during peak hours. You need to implement autoscaling for the virtual machine scale set based on CPU usage. The scaling should be aggressive when CPU exceeds 80% and conservative when CPU drops below 30%. Which scaling rule configuration should you use?
Why wrong: Scale-out threshold is too low, causing unnecessary scaling. Scale-in threshold is low but not conservative enough.
B
Scale out when CPU > 90%, scale in when CPU < 10%
Why wrong: Scale-out threshold is too high, causing performance issues before scaling; scale-in threshold is too low, keeping resources running unnecessarily.
C
Scale out when CPU > 70%, scale in when CPU < 70%
Why wrong: Scale-in threshold is too high, causing aggressive scale-in.
D
Scale out when CPU > 80%, scale in when CPU < 30%
High scale-out threshold ensures scaling only when truly needed; low scale-in threshold prevents premature scale-in.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Scale out when CPU > 80%, scale in when CPU < 30%
Option D is correct because the question explicitly requires aggressive scaling when CPU exceeds 80% and conservative scaling when CPU drops below 30%. The scale-out threshold of 80% triggers rapid addition of instances to handle high load, while the scale-in threshold of 30% ensures instances are removed only when utilization is consistently low, preventing premature scale-in and thrashing. This matches the exact thresholds specified in the requirement.
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.
✗
Scale out when CPU > 50%, scale in when CPU < 20%
Why it's wrong here
Scale-out threshold is too low, causing unnecessary scaling. Scale-in threshold is low but not conservative enough.
✗
Scale out when CPU > 90%, scale in when CPU < 10%
Why it's wrong here
Scale-out threshold is too high, causing performance issues before scaling; scale-in threshold is too low, keeping resources running unnecessarily.
✗
Scale out when CPU > 70%, scale in when CPU < 70%
Why it's wrong here
Scale-in threshold is too high, causing aggressive scale-in.
✓
Scale out when CPU > 80%, scale in when CPU < 30%
Why this is correct
High scale-out threshold ensures scaling only when truly needed; low scale-in threshold prevents premature scale-in.
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 may choose Option C because it seems 'balanced' with a single threshold, not realizing that identical scale-out and scale-in thresholds cause autoscale flapping, and that the question explicitly demands distinct aggressive (80%) and conservative (30%) values.
Detailed technical explanation
How to think about this question
Azure Virtual Machine Scale Sets use autoscale rules based on metrics like CPU percentage, with a default cooldown period of 5 minutes between scaling events to prevent flapping. The scale-out and scale-in thresholds should be separated by a sufficient margin (e.g., 50 percentage points in this case) to create a 'dead zone' that stabilizes the number of instances. In real-world scenarios, setting thresholds too close together (as in Option C) causes the autoscale engine to repeatedly add and remove instances, leading to increased costs and potential service disruption.
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 AZ-204 question in full detail.
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: Scale out when CPU > 80%, scale in when CPU < 30% — Option D is correct because the question explicitly requires aggressive scaling when CPU exceeds 80% and conservative scaling when CPU drops below 30%. The scale-out threshold of 80% triggers rapid addition of instances to handle high load, while the scale-in threshold of 30% ensures instances are removed only when utilization is consistently low, preventing premature scale-in and thrashing. This matches the exact thresholds specified in the requirement.
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.
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 →
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.