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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Preparation for Test Automation | 15% | - Design for Testability and Automation - SUT Analysis for Test Automation - Rollout and Deployment Considerations - Test Automation Architecture Design - Test Automation Tool Evaluation and Selection |
| Topic 2: Test Automation Strategy and Roadmap | 15% | - Test Automation Planning - Test Automation Maintenance - Test Automation Governance - Test Automation Integration with CI/CD |
| Topic 3: Execution and Implementation of Test Automation | 35% | - Logging and Reporting Strategies - Implementing Test Automation Solutions - Developing Test Automation Solutions - Verifying Test Automation Solutions - Building Robust Automated Test Suites - Designing Test Automation Solutions |
| Topic 4: Test Automation Validation | 15% | - Test Automation Assessment Metrics - Root Cause Analysis - Test Automation Verification |
| Topic 5: Transitioning from Manual to Automated Testing | 15% | - Criteria for Manual vs. Automated Testing - Automation Migration Strategies - Maintaining Automation Consistency |
| Topic 6: Introduction and Objectives for Test Automation | 5% | - Risks and Benefits of Test Automation - Purpose of Test Automation - Success Factors for Test Automation |
>> CTAL-TAE_V2 Exam Objectives <<
The ISQI CTAL-TAE_V2 exam offers a great opportunity for beginner and experienced to validate their expertise in a short time period. To do this they just need to pass the ISTQB Certified Tester Advanced Level - Test Automation Engineering CTAL-TAE (Syllabus v2.0) CTAL-TAE_V2 Certification Exam which is not an easy task. And Actual4Cert offfers latest CTAL-TAE_V2 exam practice, exam pattern and practice exam online.
NEW QUESTION # 78
A suite of automated test cases was run multiple times on the same release of the SUT in the same test environment. Consider analyzing a test histogram that shows the distribution of test results (pass, fail, etc.) for each test case across these runs. Which of the following potential issues is MOST likely to be identified as a result of such an analysis?
Answer: A
Explanation:
TAE recommends monitoring test results over repeated executions to detect non-determinism and flakiness. A histogram showing pass/fail distributions per test across multiple runs in the same environment and on the same SUT version is especially useful for identifying tests whose outcomes vary without corresponding changes. If a test sometimes passes and sometimes fails under equivalent conditions, the distribution reveals instability: repeated failures for the same test, intermittent patterns, or inconsistent outcomes compared with other tests that remain stable. This is a classic indicator of flaky tests or unstable test design (e.g., synchronization issues, hidden dependencies, data leakage, timing sensitivity) and is a key maintainability
/reliability concern in automation programs. While execution time outliers (A) require time-series or duration metrics rather than pass/fail distributions, a result histogram primarily focuses on outcome variability, not performance. Security vulnerabilities (B) are not identifiable from outcome distributions; they require static analysis, code review, or security testing methods. Maintainability issues (D) are generally inferred from code structure metrics (complexity, duplication), change frequency, or effort trends, not from pass/fail distributions across runs. Therefore, the most likely issue identified by analyzing such a histogram is unstable automated test cases.
NEW QUESTION # 79
You are working as a TAE for a small start-up insurance broker and your next project will be a new system for generating motor insurance quotes. The project will follow an Agile methodology. The stakeholders are keen to get it into production, but are realistic about the need to allow enough time for thorough testing and support automation of tests where appropriate.
Which approach for automating the tests would be MOST suitable for this project?
Answer: B
Explanation:
Structured scripting is the most appropriate starting approach for this relatively small new system. CTAL- TAE explains that structured scripting introduces reusable test libraries containing elements, test steps, or user journeys. Its main advantages are that automated tests become easier to maintain, scale, port, adapt, and evolve, while business logic can be separated from the scripts. Linear scripting is quicker initially but becomes difficult to maintain and evolve, making it less appropriate for an Agile system expected to change.
Keyword-driven testing introduces substantially more framework effort and the syllabus specifically notes that it can create excessive effort for smaller systems. Data-driven testing is valuable when substantial execution with different datasets is required, but that need is not stated here. Therefore, structured scripting provides the most balanced solution.
(https://www.istqb.org/wp-content/uploads/2024/11/ISTQB_CTAL-TAE_Syllabus_v2.0.pdf)
NEW QUESTION # 80
Some automated regression test scripts run by a TAS in a given test environment make calls to private APIs that require authentication for all requests (the authentication method is the same for all APIs). The SUT is a business-critical system. The following two changes are planned: a change in the authentication method of all APIs and a minor upgrade of the OS (Operating System) in the test environment. You have updated the test scripts to cope with the change in the API authentication method. Which of the following sequences of activities is BEST to ensure that the test scripts are not adversely affected by these changes?
Answer: B
Explanation:
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.
NEW QUESTION # 81
Which of the following statements about contract testing is TRUE?
Answer: D
Explanation:
TAE describes contract testing as verifying that two parties (e.g., consumer and provider services) adhere to an agreed interface contract, enabling earlier, more targeted detection of integration mismatches without requiring full end-to-end integration in every test run. A key distinction in approaches is indeed who defines
/publishes the contract. In provider-driven contracts, the provider defines the contract describing what it offers; consumers validate compatibility against it. In consumer-driven contract testing, consumers define expectations (often per consumer), and providers verify they satisfy those expectations. Option A is false because stubs/mocks (or simulated counterparts) are frequently used to allow each side to test independently and deterministically, which is one of contract testing's practical strengths. Option B is too narrow: contract testing can apply beyond REST (e.g., GraphQL, gRPC, messaging/event contracts). Option D is also too restrictive: it can apply to asynchronous interactions (events/messages) as well as synchronous calls.
Therefore, the accurate statement is option C.
NEW QUESTION # 82
The last few runs for a suite of automated keyword-driven tests on a SUT were never completed. The test where the run was aborted was not the same between runs. Currently, it is not possible to identify the root cause of these aborts, but only determine that test execution aborted when exceptions (e.g., NullPointerException, OutOfMemoryError) occurred on the SUT by analyzing its log files. Test execution log files are currently generated, in HTML format, by the TAS as follows: all expected logging data is logged for each keyword in intermediate log files. This data is then inserted into the final log file only for keywords that fail, while only a configurable subset of that data is logged for keywords that execute successfully. Which of the following actions (assuming it is possible to perform all of them) would you take FIRST to help find the root cause of the aborts?
Answer: A
Explanation:
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.
NEW QUESTION # 83
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