Practice Free CTAL-TAE ISTQB Certified Tester Advanced Level, Test Automation Engineering Exam Questions Answers With Explanation

TAE guidance emphasizes that Agile/iterative delivery drives frequent change and frequent regression risk, which often leads teams to expand automated regression suites over time. As suites grow, they can become slower, costlier to maintain, and harder to keep stable—especially if the suite is concentrated too heavily at the UI level. For this reason, TAE stresses investing in automation across multiple test levels (unit/component, API/service, and selected UI), aligning with principles behind balanced automation strategies (often illustrated by the “test pyramid”). This directly supports option A. Option B is not generally true: in Waterfall/V-model, testing activities (including automation design and implementation) are planned and may start early, but execution and refinement occur across phases aligned with integration and system readiness—not “usually only during the last phase.” Option C is too absolute: the test pyramid is a common heuristic, but TAE does not mandate it “regardless of context”; constraints like legacy systems, risk, architecture, and tooling can change the optimal distribution. Option D is incorrect because unit testing is typically a developer responsibility in both Agile and V-model contexts; testers may support, review, or contribute but do not “write automated unit tests” as a defining V-model rule. Therefore, A best matches documented lifecycle realities and maintenance concerns.

In TAE guidance, pipeline configuration items used to identify a specific test environment (and its associated test data) are those that uniquely define where the SUT is running and how automation connects to the deployed system and its dependent services and data stores. That typically includes the base URL of the deployed web application, endpoints/URLs for backend services used in that environment, and connection details to environment-specific databases (or references to secrets/credentials that enable those connections). These items allow the same automated tests to be executed against different environments by switching configuration rather than changing test code. By contrast, “the number and type of automated tests to execute” is a test selection/execution configuration decision (what to run), not an environment identification configuration (where to run). You can run different subsets of tests in the same environment without changing the environment identity. TAE distinguishes environment configuration (addresses, endpoints, credentials, data sources) from orchestration configuration (suite selection, tags, parallelism). Therefore, option A does not describe a configuration item that identifies the test environment and its specific test data.

TAE stresses that when diagnosing intermittent aborts with unclear root cause, the first priority is ensuring sufficient, consistent observability from the automation side to reconstruct what happened immediately before termination. In this scenario, the suite aborts in different tests across runs, and the final HTML report currently contains full detail only for failing keywords, while successful keywords have reduced logging. If the run aborts due to an exception in the SUT, the “last executed successful keywords” and their full context may be essential to correlate actions with the SUT failure point. The fastest, most direct improvement is to include complete keyword-level logging for successful steps as well, at least until the issue is understood. This aligns with TAE guidance to temporarily increase logging verbosity during investigation to capture the sequence of actions, inputs, timings, and states leading up to failure. Option A could be helpful, but it changes SUT-side logging and may require additional access or instrumentation; also, it does not guarantee visibility into the exact automation step sequence. Options B and D improve presentation/performance of logs but do not add diagnostic content. Therefore, first increase the completeness of the final execution logs for all keywords to maximize evidence for root cause analysis.

TAE separates two verification scopes: (1) verifying the automation environment and TAS components (infrastructure, connectivity, toolchain readiness), and (2) verifying the correctness and trustworthiness of a specific automated test suite (test completeness, determinism, result validity). The scenario explicitly states that the environment and all TAS components have already been verified as working as expected. Connectivity between the TAS and internal/external systems is an environment-level readiness check and therefore belongs primarily to the first scope. For the second scope—verifying the behavior of the automated test suite—TAE emphasizes ensuring tests are complete (including correct expected results and data), are repeatable/deterministic across runs, and that the approach/tool intrusion level is understood so stakeholders can interpret confidence in results. That maps to options B, C, and D as suite-focused considerations. Option A repeats an environment connectivity check that should have been addressed in the prior phase and is not a core part of verifying the suite’s behavior once environment readiness has been established. Therefore, option A should NOT be part of the suite-behavior verification in this stated situation.

To implement automated API tests, TAE emphasizes that testers need precise, actionable interface specifications: what endpoints exist, what inputs they accept, how to authenticate/authorize requests, and what outputs are returned (status codes, headers, response body schemas/formats). Options B, C, and D directly support test design and implementation: parameter details enable valid/invalid request construction and boundary coverage; authentication mechanisms are required to execute any protected calls and to test auth-related behaviors; response formats enable robust assertions (including schema validation). Release notes and change logs are valuable for understanding evolution, migration, and backward compatibility considerations, but they are not typically required to implement the tests for the current API behavior when the current specification is available. They may help explain why something changed or guide test updates over time, yet they are less directly relevant to writing the core automated checks compared with endpoint inputs, auth, and response structure. Therefore, among the options, past release notes/change logs are the least relevant for implementing automated tests on the API.

TAE recommends controlled change management to isolate causes when multiple changes are introduced. When you apply more than one change at once, diagnosing failures becomes harder because you cannot easily attribute effects to a specific change. The best practice is to implement changes incrementally, validating automation and system behavior after each change using a representative subset of tests (e.g., smoke/build verification or targeted regression) to quickly detect issues. Because the system is business-critical, risk mitigation is stronger: you want early detection and clear attribution. After each change is validated with a subset, you then execute the full updated regression suite to ensure overall coverage and confidence. Options A and C apply two changes before running tests, which reduces diagnostic clarity and increases the risk of late discovery. Option D describes incremental changes with subset testing but omits the final full-suite run, which TAE would recommend to ensure broad coverage after all changes have been applied. Therefore, the best sequence is: change one item, run a subset, repeat for the next change, then run all updated scripts.

TAE recognizes a range of supporting capabilities offered by test tools beyond pure scripted execution, including reporting, evidence capture, and run artifacts that help stakeholders understand what was tested. Video recording of UI test sessions is a common feature in several UI automation ecosystems and cloud device/browser platforms, used to provide visual evidence of steps performed, failures observed, and the application’s look-and-feel during execution. This supports debugging and communication with non-technical stakeholders. Option A overstates what test automation tools do: autonomously designing intuitive UIs and evaluating UX is largely outside typical test automation tool scope and requires human-centered design methods. Option C is also overstated: exploratory testing is inherently human-driven; tools can assist (session notes, heuristics support, telemetry) but do not truly conduct exploratory testing autonomously based on charters in the general TAE framing. Option B touches on advanced analytics and AI/ML-assisted quality insights; while some platforms offer risk prediction features, the phrasing implies broad predictive defect capability, which is not a standard, dependable tool function emphasized in TAE compared with concrete capabilities like artifact capture. Therefore, the clearly true, commonly supported capability is making video recordings of UI testing sessions.

In TAE, “design for testability” includes attributes that make it easier to create, execute, and maintain automated tests across levels (component, integration, system, UI). The need to define precisely which interfaces are available at different test levels—e.g., public APIs, service endpoints, message queues, UI automation hooks, test seams, logs, and internal test interfaces—maps most directly toarchitecture transparency. Architecture transparency concerns how clearly the system’s structure, layers, and accessible interfaces are documented and exposed so test automation can reliably connect to the right interaction points. This includes understanding which interfaces are stable, supported, and appropriate for each level of testing, and avoiding “guesswork” that increases brittleness. Controllability is about the ability to set inputs, states, and preconditions (e.g., reset data, seed databases, drive system state). Observability is about the ability to see outputs, internal states, and logs to assess outcomes. Autonomy concerns whether tests can run independently without external dependencies or manual intervention (e.g., isolated environments, stable test data). While controllability/observability/autonomy are critical for automation, the specific emphasis on “precisely defining which interfaces are available” is fundamentally an architectural transparency issue: clear interface availability and documentation enable correct, maintainable automation connections across test levels.

TAE promotes selecting automation interfaces that maximize speed, robustness, and functional coverage while minimizing unnecessary UI traversal. For a thin client architecture, most business logic and system functionality resides on the server. To cover functionality efficiently, tests should interact as close as possible to where the logic is implemented—typically via server-side interfaces (e.g., APIs/services, backend endpoints, message interfaces). This reduces GUI overhead and accelerates execution while improving reliability. For a fat client, substantial logic resides on the client side; server-side automation alone may miss critical client behavior, validations, local processing, and UI-driven flows that embody much of the functionality. In such cases, client-side automation (often UI automation or client-level interfaces) is more directly aligned to achieving high functional coverage. TAE also highlights that the “best” interface depends on where behavior is implemented and which interface yields the most stable, fastest checks for the targeted risks. Therefore, the optimal combination is server-side automation for SUT1 (thin client) and client-side automation for SUT2 (fat client), which best meets the goal of broad coverage with minimal execution time.

TAE strongly discourages replacing robust, app-aware synchronization with manual waits. Automatic synchronization based on application readiness signals (e.g., no pending async requests) reduces flakiness and unnecessary delays. Hard-coded waits (A) are brittle and slow; polling waits (C) can be better than fixed sleeps but are still generally inferior to event/readiness-based synchronization already in place. The improvement opportunity described is that the same initialization steps are repeated in every test as explicit test steps, which increases test script length, duplication, and maintenance effort. TAE recommends centralizing common setup logic using framework setup/teardown mechanisms to enforce consistency and reduce duplication. Since the initialization tasks are needed to set preconditions for each test (so each test starts from a known state and remains independent), they belong in the Test Setup, which runs before each test case. Putting them in Suite Setup (D) would run them only once, risking that later tests inherit polluted state, making tests interdependent and more brittle. Therefore, moving shared per-test initialization tasks into the Test Setup is the best recommendation.