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| Section | Objectives |
|---|---|
| Continuous Improvement | - Streamlining test suites (removing obsolete tests) - Analyzing and adapting the test automation strategy over time - Maintaining automated testing assets - Upgrading tools and libraries - Refactoring test code |
| Test Automation Reporting and Metrics | - Proving value to stakeholders - Visualizing results for different audiences (technical vs. management) - Logging: detailed logs for debugging vs. high-level summaries - Selecting appropriate metrics (e.g., percent automated, defect detection rate) |
| Verifying the Test Automation Solution (TAS) | - Automated test suite verification - Verifying that the automation code itself is correct - Checking the test environment setup |
| Introduction and Objectives for Test Automation | - Benefits and limitations of test automation - How SUT (System Under Test) architecture impacts test tool suitability - Test automation within different software development lifecycle models |
| Implementation of Test Automation | - Integration of test automation with CI/CD pipelines - Test automation solution (TAS) implementation - Configuration management in test automation - SUT-specific adaptors - Test environment configuration (URLs, Credentials, Test data, Common core library) - Test data management |
| Preparing for Test Automation | - SUT Testability (Observability and Controllability) - Design for Testability - Evaluating automation tools and technologies - Tool Selection and evaluation for specific technology stacks |
| The Generic Test Automation Architecture (gTAA) | - Design principles and design patterns in test automation (Abstraction, Modularity, Separation of data from scripts) - Tailoring gTAA to create a specific Test Automation Architecture (TAA) - Layering of test automation frameworks (Test Generation, Test Definition, Test Execution, Test Adaptation layers) - Designing a test automation solution - Approaches for automating test cases - Capabilities in a test automation architecture |
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NEW QUESTION # 33
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 # 34
As a TAE you are evaluating a functional test automation tool that will be for several projects within your organization. The projects require that tool to work effectively and efficiently with SUT's in distributed environments. The test automated tool also needs to interface with other existing test tools (test management tool and defect tracking tool.) The existing test tools subject to planned updates and their interface to the test automated tool may not work property after these updates.
Which of the following are the two LEAST important concerns related to the evaluation of the test automation in this scenario?
* Is the test automation tool able to launch processors and execute test cases on multiple machines in different environments?
* Does the test automation tool support a licensing scheme thatallows accessing different sets?
* Does the test automation tool have a large featureset, but only part of the features will be sets?
* Do the release notes for the planned updates on existing specify the impacts on their interfaces to other tools?
Does the test automation tool need to install specific libraries that could impact the SUT?
Answer: D
NEW QUESTION # 35
Which of the following statements about the reuse of TAS artefacts is TRUE?
Answer: B
NEW QUESTION # 36
The GUI of a Customer Relationship Management (CRM) application has been delivered through internet Explorer with proprietary Active X and Java controls. This implementation enables rich client capabilities, but specific commercial automation tools are necessary to automate test cases at GUI of functional test cases. This is to demonstrate whether a small set of the commercial are able to properly recognize actions taken by a tester when interacting with GUI of the CRM application.
Which of the following scripting techniques would be MOST suitable in this scenario?
Answer: C
NEW QUESTION # 37
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: C
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 # 38
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