iSQI CTAL-TAE Exam With Confidence Using Practice Dumps

The primary problem is execution time; correctness and independence are already strong. TAE recommends improving feedback time for long-running regression suites by parallelizing execution when tests are independent and the infrastructure supports it. Because the tests are explicitly independent, they are well-suited to parallel execution across multiple environments (or multiple nodes within an environment), reducing overall wall-clock duration without changing test intent. Option B addresses crash recovery, but the scenario says executions complete successfully; crash recovery does not solve the current bottleneck. Option A changes the modeling pattern; it may or may not reduce LOC, but it introduces risk and rework without directly addressing runtime. Also, flow model and façade-expanded page objects are already architectural choices aimed at maintainability and reuse; replacing them is not the most direct solution for speed. Option D (improving SUT testability) can help in general, but it is invasive, expensive, and not targeted to the stated issue when tests already yield correct results. Therefore, the best improvement is to split the suite and run parts concurrently on different environments to reduce total execution time, consistent with TAE guidance on scaling automation execution.

Given the explicit risk that the web app may crash during execution, the highest-priority tool capability is resilience: the ability to recover, continue, and provide usable results from unattended nightly runs. TAE emphasizes that automation must be reliable as a process, not just at the single-test level. If one crash aborts the entire suite, the organization loses feedback for many tests, reduces confidence in the pipeline, and increases triage cost. Therefore, capabilities such as automatic restart of the browser/app, test isolation, robust teardown, failure handling, skipping/marking affected tests, and resuming execution with proper reporting are critical evaluation criteria. Option A (descriptive meta-language) can help readability or non-coder authoring but is not the most urgent need based on the scenario. Option C (mock server) is useful for isolating dependencies in some test levels, but the scenario is UI tests against the most recent build; nothing indicates an API dependency problem that drives tool selection here. Option D (licensing feature sets) affects procurement, but it does not directly mitigate the stated operational risk. Hence, recovery and continuation support is the most important concern.