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| Section | Objectives |
|---|---|
| Integration and Deployment (CI/CD) | - Toolchain integration - Integration into CI/CD pipelines - Execution strategies in continuous testing |
| Test Automation Architecture | - Maintainability and scalability considerations - Test automation frameworks - Design principles for automation architecture |
| Test Automation Maintenance and Evolution | - Maintaining automated test assets - Refactoring automation solutions - Handling application changes |
| Introduction to Test Automation Engineering | - Goals and value of test automation - Success factors for test automation - Test automation within the software lifecycle |
| Test Automation Reporting and Metrics | - Automation reporting approaches - Key metrics for test automation effectiveness |
| Transition and Deployment | - Scaling test automation adoption - Introducing automation into organizations |
| Test Automation Solution Development | - Handling test environments - Implementation of automated test solutions - Test data management |
| Preparing for Test Automation | - Risk analysis for automation scope - Selecting test cases for automation - Feasibility assessment for automation |
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質問 # 39
(Which of the following statements about how test automation is applied across different software development lifecycle models is TRUE?)
正解：C
解説：
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.
質問 # 40
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?
正解：D
解説：
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.
質問 # 41
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?
正解：A
解説：
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.
質問 # 42
An automated test script makes a well-formed request to a REST API in the backend of a web app to add a single item for a product (with ID = 710) to the cart and expects a response confirming that the product is successfully added. The status line of the API response is HTTP/1.1 200 OK, while the response body indicates that the product is out of stock. The API response is correct, the test script fails but completes, and the message to log is: The product with ID = 710 is out of stock. Cart not updated. When this occurs, you are already aware that both the failed test and the API are behaving correctly and that the problem is in the test data. The TAS supports the following test logging levels: FATAL, ERROR, WARN, INFO, DEBUG. Which of the following is the MOST appropriate test logging level to use to log the specified message?
正解：D
解説：
TAE logging guidance focuses on making logs actionable while reflecting severity and intent. Here, the test failed due to an expected, non-system fault condition: the product is out of stock, which is a valid business- state response and confirms the API behaved correctly. The issue is that the test data (product availability) did not satisfy the test's precondition. This is not a fatal condition (FATAL) because execution continues and the overall system is not unusable. It is not best treated as ERROR either (not offered as an option here) because an error-level message usually indicates a defect, malfunction, or unexpected failure needing immediate engineering attention. INFO would be too low because it may be lost among normal run messages and does not adequately flag that the test outcome is affected by a precondition violation requiring action (e.g., reseeding data, choosing a different product ID). DEBUG is typically reserved for highly detailed diagnostic traces intended for deeper troubleshooting, not for highlighting a test-data problem affecting test validity.
WARN is intended for abnormal or noteworthy conditions that do not indicate a product defect but may require attention to maintain test reliability. Therefore, WARN is the most appropriate level.
質問 # 43
(Which of the following aspects of "design for testability" is MOST directly associated with the need to define precisely which interfaces are available in the SUT for test automation at different test levels?)
正解：B
解説：
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 to architecture 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.
質問 # 44
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