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| Section | Objectives |
|---|---|
| Implementation of Test Automation | - SUT-specific adaptors - Integration of test automation with CI/CD pipelines - Test data management - Configuration management in test automation - Test environment configuration (URLs, Credentials, Test data, Common core library) - Test automation solution (TAS) implementation |
| Continuous Improvement | - Upgrading tools and libraries - Analyzing and adapting the test automation strategy over time - Refactoring test code - Streamlining test suites (removing obsolete tests) - Maintaining automated testing assets |
| The Generic Test Automation Architecture (gTAA) | - Design principles and design patterns in test automation (Abstraction, Modularity, Separation of data from scripts) - Approaches for automating test cases - Layering of test automation frameworks (Test Generation, Test Definition, Test Execution, Test Adaptation layers) - Designing a test automation solution - Tailoring gTAA to create a specific Test Automation Architecture (TAA) - Capabilities in a test automation architecture |
| Preparing for Test Automation | - Evaluating automation tools and technologies - SUT Testability (Observability and Controllability) - Design for Testability - Tool Selection and evaluation for specific technology stacks |
| Verifying the Test Automation Solution (TAS) | - Checking the test environment setup - Automated test suite verification - Verifying that the automation code itself is correct |
| Test Automation Reporting and Metrics | - Logging: detailed logs for debugging vs. high-level summaries - Visualizing results for different audiences (technical vs. management) - Proving value to stakeholders - Selecting appropriate metrics (e.g., percent automated, defect detection rate) |
| Introduction and Objectives for Test Automation | - Test automation within different software development lifecycle models - How SUT (System Under Test) architecture impacts test tool suitability - Benefits and limitations of test automation |
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NEW QUESTION # 67
Which of the following is the BEST example of how static analysis tools can help improve the test automation code quality in terms of security?
Answer: A
Explanation:
TAE highlights that test automation code can introduce security risks, particularly when it handles secrets (API keys, passwords, tokens), test accounts, and connections to production-like systems. Static analysis tools can scan source code for insecure patterns and policy violations without executing the code. A common, high- impact security issue in automation is hard-coded credentials or secrets embedded in scripts, configuration files committed to version control, or test utilities. Detecting these is a direct security-quality improvement: it reduces exposure risk and supports compliance. Option A is incorrect because static analysis can produce false positives; detection heuristics are not perfect. Option B is useful for maintainability (duplication), but it is not specifically a security improvement example. Option D overclaims: static analysis cannot guarantee the absence of security vulnerabilities; it can only detect certain classes of issues. Therefore, the best security- focused example is that static analysis can identify hard-coded credentials and other sensitive data exposure in test automation code.
NEW QUESTION # 68
Consider A TAS for testing a desktop application via its GUI. All the test cases of the automated test suite contain the same identical sequences of steps at the beginning (to create the necessary objects when doing a preliminary configuration of the test environment and at the end (to remove everything created -specifically for the test itself during the preliminary configuration of the test environment). All automated test cases use the same set of assertion functions from a shared library, for verifying the values in the GUI fields (e.g text boxes).
What is the BEST recommendation for improving the TAS?
Answer: B
NEW QUESTION # 69
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 # 70
Consider a TAS that uses a keyword-driven framework. The SUT is a web application and there is a large set of keywords available for writing the automated tests that relate to highly specific user actions linked directly to the GUI of the SUT. The automated test written with the keywords are statically analyzed by a custom tool which highlight's repeated instances of identical sequence of keywords. The waiting mechanism implemented by the TAS for a webpage load is based on a synchronous sampling within a given timeout. The TAS allows checking a webpage load every seconds until a timeout value
Answer: D
NEW QUESTION # 71
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: C
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 # 72
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