CT-PT Exam Dumps - ISTQB Software Testing & Quality Questions and Answers

Aggregating test resultshelps stakeholders understand performance trendsbycombining data points from multiple test runs. This makes iteasier to draw conclusionsabout system behavior.

Option A is incorrectbecause aggregation may reduce detailed visibility, not increase it.

Option B is incorrectbecause aggregation does notexpedite testing itself.

Option D is incorrectbecause aggregated results do not indicatetest completion.

When system resources (e.g.,CPU, RAM, I/O) are oversaturated, the system becomes overloaded, leading toincreased response timesandpoor application performance.

Option A (Gradual improvement of response time)is incorrect becauseperformance degrades under heavy load.

Option C (Error logging decreases)is unlikely; in most cases, errorsincreasewhen systems reach their limits.

Option D (Database size decreases)is unrelated to performance saturation.

Acloud-based environmentis themost cost-effectivesolution for performance testing. It offersscalability, pay-per-use models, and flexibility, allowing testers to simulate large workloadswithout expensive infrastructure investments.

Option A (Isolated lab environment)is costly due tohardware maintenance and infrastructure costs.

Option C (Mainframe environment)isexpensive and impracticalfor performance testing.

Option D (No environment)is not a viable option.

ODBC (Open Database Connectivity)is astandard database protocolthat enables applications toconnect to and interact with databasesregardless of vendor specifications.

Option B (JUNIT)is aunit testing framework, not a database protocol.

Option C (SMP - Symmetric Multiprocessing)relates toCPU architecture, not databases.

Option D (MMS - Multimedia Messaging Service)is related tomobile messaging, not database communication.

One of the mostcritical test acceptance criteriain performance testing is toensure that the hardware in the test environment is comparable to production. Differences inCPU, memory, disk I/O, or network infrastructurecandistort performance results.

Option A (Convincing stakeholders to set goals)is a planning activity, not an acceptance criterion.

Option B (Describing the system under test)is important but does not directly affect test acceptance.

Option D (Comparing baselined metrics)is useful, butwithout a comparable test environment, baseline metrics may be misleading.

Aload generatoris responsible forsimulating virtual usersand applyingworkloads to a systemas defined by an operational profile. This allows testers to analyze how the system behaves under different load conditions.

Option A (Background load)is incorrect becauseload generators create simulated user interactions, not just background noise.

Option C (Record and analyze behavior)is the role ofmonitoring tools, not a load generator.

Option D (Support root cause analysis)is incorrect becauseroot cause analysis is done after the load test, using monitoring tools.

Business stakeholders aremost concerned with risks that affect deployment and production stability. The primary risk in performance testing is that thetest environment may differ from production, leading to misleading test results.

Option A (HTTP response goals)is too technical for abusiness stakeholder audience.

Option B (Hardware specifications)is relevant for technical teams, not business stakeholders.

Option C (Support staff details)is alogistical aspect, not a key performance testing risk for business decision-makers.

Crowd-based load generationinvolves using distributed users (e.g., cloud-based testers or real users). The mainchallengeis thatthe load generation can be difficult to reproduce consistentlydue to variations innetwork conditions, device types, and user behavior.

Option B is incorrectbecause load generation methods should adapt to system changes.

Option C is incorrectsince mainframe applications typically do not rely on crowd-based testing.

Option D is incorrectbecause crowd-sourced load testing is generallyless precisethan scripted load testing.

Crowd-based load generationrelies on real users from different locations, makingtest conditions highly variable. This makes itdifficult to reproduce the same test scenario multiple timesunder identical conditions.

Option B (Less sensitive to changes)is incorrect becausecrowd-based load is highly sensitive to external conditions(network, device types, etc.).

Option C (Suitable for mainframe applications)is incorrect becausecrowd-based load testing is typically used for web and cloud applications, not mainframes.

Option D (More precise than other methods)is incorrect becausecrowd-based load testing lacks precision due to real-world inconsistencies.

Before running the next set of performance tests, it is crucial tounderstand 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 becausetest objectives should focus on technical accuracy rather than schedule constraints.