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Stakeholders were unhappy with previous performance test results, which indicated that the adjustments were not made fast enough and engine stall was probable. The developers have now made changes that they believe will significantly improve the performance of the adjustments.
What should you do to ensure the performance test objectives have been properly defined before you run the next set of performance tests?
Before running the next set of performance tests, it is crucial to understand the differences (delta) between the previous test results and the expected improvements. This ensures that:
The changes made by developers are properly validated.
Performance objectives are aligned with measurable improvements.
Option B (Adjust stakeholder expectations) is incorrect, as performance testing should focus on real data rather than adjusting expectations.
Option C and D (Realign objectives for milestones or time-to-market goals) are incorrect because test objectives should focus on technical accuracy rather than schedule constraints.
What is the primary purpose of a load generator?
A load generator is responsible for simulating virtual users and applying workloads to a system as defined by an operational profile. This allows testers to analyze how the system behaves under different load conditions.
Option A (Background load) is incorrect because load generators create simulated user interactions, not just background noise.
Option C (Record and analyze behavior) is the role of monitoring tools, not a load generator.
Option D (Support root cause analysis) is incorrect because root cause analysis is done after the load test, using monitoring tools.
Which type of performance test measures the system's ability to handle increasing levels of load?
Load testing assesses a system's ability to handle gradually increasing levels of load and ensures that it meets performance expectations under normal and peak conditions.
Option B (Elevation testing) is incorrect because this term does not exist in ISTQB performance testing terminology.
Option C (Spike testing) focuses on sudden increases in load rather than gradual scaling.
Option D (Endurance testing) examines how a system performs over an extended period, rather than gradual load increases.
You have run a load test. When examining the metrics, you see that the virtual users experienced many timeouts and excessive wait times. The system throughput metrics exceeded expected results, even during peak times.
Based on your analysis, what conclusion should you draw?
If timeouts and excessive wait times occur, but throughput is high, this suggests that the system is unable to process requests fast enough. This points to slow processing speed due to CPU bottlenecks, memory limitations, or inefficient database queries.
Option A (Network infrastructure investigation) would be relevant if throughput was low or variable.
Option C (Impatient virtual users) is irrelevant; virtual users follow scripted behaviors.
Option D (New test data) does not address the core issue.
What challenge must be considered when using crowds to emulate load generation?
Crowd-based load generation involves using distributed users (e.g., cloud-based testers or real users). The main challenge is that the load generation can be difficult to reproduce consistently due to variations in network conditions, device types, and user behavior.
Option B is incorrect because load generation methods should adapt to system changes.
Option C is incorrect since mainframe applications typically do not rely on crowd-based testing.
Option D is incorrect because crowd-sourced load testing is generally less precise than scripted load testing.
