ISTQB CT-PT - ISTQB Certified Tester - Performance Testing

When system resources areoversaturated(e.g., CPU, RAM, or network bandwidth),responsetimes will degradebecause the systemstruggles to process requests efficiently. This is akey performance bottleneck indicator.

Option A is incorrectbecause response times do not improve under heavy load; they typically worsen.

Option C is incorrectbecauseerror logging may actually increaserather than decrease under stress.

Option D is incorrectbecause database size is independent of system saturation unless records are actively purged.

Performance monitoring mustbegin simultaneously with test executiontocapture system behavior in real-time. This ensures thatbottlenecks and performance issues are identified immediately.

Option A (After testing is concluded)is incorrect becausehistorical monitoring does not help in real-time analysis.

Option B (Before execution)is incorrect because monitoring needsactive system loadto measure performance.

Option C (Immediately after execution)is incorrect because itmisses runtime metrics.

Aload management consoleis a tool that helps testersanalyze and visualize test results, providingperformance reports, graphs, and insights. This aids in understandingbottlenecks, response times, and throughput trends.

Option A (Simulating user behavior)is incorrect;load generatorshandle simulation.

Option B (Senior management monitoring)is not the primary role of atest console.

Option D (Emulating virtual users)is incorrect; that is the job ofload injectors.

Nested transactionsallow performance tests to capture bothindividualandaggregatedelements bygrouping multiple related transactionsand measuring their cumulative impact.

Option A (Aggregated metrics)provides summaries but lacks insight into individual elements.

Option B (Process optimization tracking)focuses onbusiness process improvement, not test metrics.

Option C (Measuring underlying transactions)focuses only onindividualtransactions.

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.

Protocol-level scriptsmeasure theactual response times of network requests, making them ideal foranalyzing network latency, bandwidth issues, and server response times.

Option A (GUI scripts)measure user interactions, not network timing.

Option B (API scripts)measure API interactions but do not providedetailed network response time analysis.

Option C (HTTP scripts)measure web requests but lack low-level network insights.

Forsafety-critical software, performance goalsmust be identified as early as the requirements phase. This ensures thatperformance constraints are well understood and accounted for throughout development.

Option A (After test readiness but before system testing)istoo late, as requirements should be definedbefore coding begins.

Option C (During the concept stage)istoo early, as details may not be concrete.

Option D (During software design)is useful butshould be preceded by a proper definition during the requirements phase.

Mobile and browser-based applicationsoften rely on APIsfordata retrieval and processing.Unexpected API loadcan causelatency issues, bottlenecks, and system crashes.

Option A (Hard disk speed)is not a major issue incloud-based or mobile systems.

Option B (Misconfigured host machine)affectson-premise systems, not mobile/browser-based applications.

Option D (Internet connectivity issues)is auser-side issue, not a system performance risk.

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.