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| Section | Weight | Objectives |
|---|---|---|
| Continuous Improvement | 19% | - Streamlining and maintaining test assets - Adapting to new technologies and requirements - Refactoring and optimizing automation - Upgrading tools and frameworks |
| Implementing Test Automation | 11% | - Planning and running pilot projects - Handling synchronization, stability and reliability - Managing technical debt - Developing and maintaining automation components |
| Preparing for Test Automation | 14% | - Evaluating and selecting test tools - Assessing system testability and architecture - Cost, effort and ROI analysis - Identifying automation opportunities and constraints |
| Implementation and Deployment Strategies | 12% | - Integration with CI/CD pipelines - Test execution strategies and environment management - Configuration management and version control - Test data management approaches |
| Test Automation Architecture | 15% | - Design principles and patterns for automation - Interoperability and integration concepts - Layered frameworks and separation of concerns - Generic Test Automation Architecture (gTAA) |
| Verifying the Test Automation Solution | 12% | - Validating test suite correctness - Assessing quality and reliability of automation - Verifying automation code and infrastructure |
| Reporting and Metrics | 12% | - Visualization and dashboards - Collecting and analyzing data - Reporting to stakeholders - Defining relevant automation metrics |
| Introduction and Objectives for Test Automation | 5% | - Test automation in software development lifecycle models - Purpose, benefits and limitations of test automation - Roles and responsibilities of a Test Automation Engineer |
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NEW QUESTION # 72
An automated test case that should always pass sometimes passes and sometimes fails intermittently (non- deterministic behavior) when executed in the same test environment, even if no code (i.e., SUT code or the test automation code) has been changed. Which of the following statements about the root cause of this non- deterministic behavior is TRUE?
Answer: A
Explanation:
TAE treats non-deterministic (flaky) test behavior as a symptom that can originate from multiple sources:
timing and synchronization issues, race conditions, concurrency, environmental variability (resource contention, network latency), unstable test data, third-party dependencies, or hidden state leakage between tests. Because these causes often span boundaries-application code, infrastructure, deployment configuration, test tooling, and data pipelines-finding the true root cause frequently requires collaboration beyond the TAE role. Developers may need to inspect application logs, thread behavior, and recent architectural assumptions; system engineers may need to analyze resource saturation, container orchestration events, network anomalies, or environment drift. Option A is too specific and assertive: the root cause is not necessarily a race condition, and logs may not be sufficient to identify it. Option C is incorrect because no code change does not imply the environment is the only cause; flaky behavior can stem from hidden nondeterminism in the system or tests that is always present but only sometimes triggers. Option D is also incorrect; intermittent failures are often harder to diagnose than consistent deterministic failures because evidence is less reproducible. Therefore, the true statement is that determining the root cause may require support from developers and system engineers in addition to the TAE.
NEW QUESTION # 73
A release candidate of a SUT, after being fully integrated with all other necessary systems, has successfully passed all required functional tests (90% were automated tests and 10% were manual tests). Now, it is necessary to perform reliability tests aimed at evaluating whether, under certain conditions, that release will be able to guarantee an MTBF (Mean Time Between Failures) in the production environment higher than a certain threshold (expressed in CPU time). Which of the following test environments is BEST suited to perform these reliability tests?
Answer: C
Explanation:
Reliability testing (e.g., long-duration runs, endurance/soak, stability measurements, MTBF assessment) requires an environment that closely resembles production in terms of configuration, resource allocation, deployment topology, integrations, and operational characteristics. TAE guidance emphasizes that measurements like MTBF are highly sensitive to environmental differences such as CPU quotas, background load, database sizing, network topology, virtualization settings, and monitoring agents. A local development environment is unsuitable because it is not representative, is often unstable, and typically lacks full system integration. A build environment focuses on building/packaging and fast verification, not production-like reliability evaluation. An integration environment can validate that systems work together, but it is frequently shared, changes often, and may not match production sizing and operational constraints; it is also commonly disrupted by other teams' deployments. Preproduction (often called staging) is designed to be the closest safe approximation to production while still allowing controlled testing, including reliability and performance- related evaluations, without risking real users or live data. Therefore, preproduction is the best-suited environment to run reliability tests intended to predict production MTBF behavior with credible confidence.
NEW QUESTION # 74
As a TA-E, you have successfully verified that a test automation environment and all other components of the TAS are working as expected. Now your goal is to verify the correct behavior for a given automated test suite that will be run by the TAS. Which of the following should NOT be part of the verifications aimed at achieving your goal?
Answer: D
Explanation:
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.
NEW QUESTION # 75
You are a TAE responsible for a TAS that is used on several products, all of which have complex UIs. A substantial proportion of the automated tests break on a regular basis, usually for reasons associated with the UI. The investigation and maintenance this requires is significantly reducing the productivity of the teams and the return on investment.
You are looking for ways to improve the test cases in this TAS. Which of the following approaches would be MOST appropriate?
a) Artificial intelligence
b) Test histograms
c) Reliability growth modelling
d) Schema validation
e) Operational profiles
Answer: B
Explanation:
Both artificial intelligence and test histograms directly address unstable UI automation. CTAL-TAE explains that test histograms visualize test-result and test-data trends and help TAEs identify fragile test cases that should be refactored or redesigned. This is particularly useful when a substantial proportion of tests repeatedly break. AI and machine-learning techniques provide an additional UI-specific improvement: tools can detect changed locator values, identify replacement selectors through ML or image-recognition techniques, and apply self-healing mechanisms that reduce locator-related maintenance. Schema validation is primarily applicable to API and database data structures rather than complex UI instability. Reliability growth modelling and operational profiles address software reliability concerns rather than recurring automated UI maintenance. Therefore, combining test histograms for identifying fragile tests with AI-based self-healing for UI locator changes gives the strongest solution.
(https://www.istqb.org/wp-content/uploads/2024/11/ISTQB_CTAL-TAE_Syllabus_v2.0.pdf)
NEW QUESTION # 76
A TAS is used to run on a test environment a suite of automated regression tests, written at the UI level, on different releases of a web app: all executions complete successfully, always providing correct results (i.e., producing neither false positives nor false negatives). The tests, all independent of each other, consist of executable test scripts based on the flow model pattern which has been implemented in a three-layer TAF (test scripts, business logic, core libraries) by expanding the page object model via the facade pattern. Currently the suite takes too long to run, and the test scripts are considered too long in terms of LOC (Lines of Code).
Which of the following recommendations would you provide for improving the TAS (assuming it is possible to perform all of them)?
Answer: B
Explanation:
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 facade-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.
NEW QUESTION # 77
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