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| Section | Objectives |
|---|---|
| The Generic Test Automation Architecture (gTAA) | - Tailoring gTAA to create a specific Test Automation Architecture (TAA) - Layering of test automation frameworks (Test Generation, Test Definition, Test Execution, Test Adaptation layers) - Capabilities in a test automation architecture - Designing a test automation solution - Design principles and design patterns in test automation (Abstraction, Modularity, Separation of data from scripts) - Approaches for automating test cases |
| 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 |
| Preparing for Test Automation | - Design for Testability - SUT Testability (Observability and Controllability) - Tool Selection and evaluation for specific technology stacks - Evaluating automation tools and technologies |
| Test Automation Reporting and Metrics | - Visualizing results for different audiences (technical vs. management) - Selecting appropriate metrics (e.g., percent automated, defect detection rate) - Proving value to stakeholders - Logging: detailed logs for debugging vs. high-level summaries |
| Continuous Improvement | - Maintaining automated testing assets - Refactoring test code - Streamlining test suites (removing obsolete tests) - Upgrading tools and libraries - Analyzing and adapting the test automation strategy over time |
| Implementation of Test Automation | - Integration of test automation with CI/CD pipelines - Configuration management in test automation - Test automation solution (TAS) implementation - Test data management - Test environment configuration (URLs, Credentials, Test data, Common core library) - SUT-specific adaptors |
| Verifying the Test Automation Solution (TAS) | - Automated test suite verification - Verifying that the automation code itself is correct - Checking the test environment setup |
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NEW QUESTION # 42
You are currently conducting a Proof of Concept (PoC) aimed at selecting a tool that will be used for the development of a TAS. This TAS will exclusively be used by one team within your organization to implement automated UI-level test scripts for two web apps. The two tools selected for the PoC use JavaScript
/TypeScript to implement the automated test scripts and offer capture and playback capabilities. Three test cases for each of the two web apps were selected to be automated during the PoC. The PoC will compare these two tools in terms of their effectiveness in recognizing and interacting with UI widgets exercised by the test cases, to quickly determine whether test automation is possible and which tool is better. Which of the following TAFs is BEST suited for conducting the PoC?
Answer: B
Explanation:
For a PoC whose primary goal is rapid feasibility assessment and tool comparison (especially around object recognition and interaction), TAE recommends minimizing framework complexity and upfront engineering.
In a PoC, you want the shortest path to executing representative tests so you can observe tool behavior, stability, locator robustness, synchronization support, and ease of driving the UI widgets in scope. A one-layer approach-simple test scripts with minimal abstraction-reduces the time spent building reusable libraries, enforcing architecture, or creating business layers that are not necessary for answering the PoC question.
Multi-layer frameworks (two-layer and beyond) are more appropriate when you are establishing maintainability, reuse, and scaling for long-term automation. Those benefits matter in the full TAS implementation, but they can distort PoC outcomes by introducing additional design decisions, patterns, and glue code that hide or compensate for tool limitations. Since only six test cases are being automated and the objective is to quickly determine whether UI automation is possible and which tool performs better at widget interaction, the simplest structure (one-layer TAF) is best aligned with TAE PoC guidance: rapid learning, minimal overhead, and clear attribution of outcomes to the tool rather than to framework design.
NEW QUESTION # 43
A defect in a SUT has been resolved and validated by an automated defect re-test in the current release of the software. This retest has now been added to the automated regression test suite.
Which statement BEST describes a reason why this defect could re-occur in future releases?
Answer: D
NEW QUESTION # 44
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?
Answer: D
Explanation:
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.
NEW QUESTION # 45
You are reviewing the testability of your SUT.
Which of the following BEST refers to the characteristic of OBSERVABILITY?
Answer: B
NEW QUESTION # 46
Which of the following metrics could suggest, under certain condition that an automated regression test suite has NOT been updated for new functionalities added to the SUT?
Answer: A
NEW QUESTION # 47
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