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| Section | Objectives |
|---|---|
| Continuous Improvement | - Refactoring test code - Maintaining automated testing assets - Streamlining test suites (removing obsolete tests) - Analyzing and adapting the test automation strategy over time - Upgrading tools and libraries |
| Introduction and Objectives for Test Automation | - How SUT (System Under Test) architecture impacts test tool suitability - Benefits and limitations of test automation - Test automation within different software development lifecycle models |
| Implementation of Test Automation | - Test data management - Configuration management in test automation - Integration of test automation with CI/CD pipelines - SUT-specific adaptors - Test environment configuration (URLs, Credentials, Test data, Common core library) - Test automation solution (TAS) implementation |
| Preparing for Test Automation | - Design for Testability - SUT Testability (Observability and Controllability) - Evaluating automation tools and technologies - Tool Selection and evaluation for specific technology stacks |
| Test Automation Reporting and Metrics | - Selecting appropriate metrics (e.g., percent automated, defect detection rate) - Logging: detailed logs for debugging vs. high-level summaries - Proving value to stakeholders - Visualizing results for different audiences (technical vs. management) |
| 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 - Approaches for automating test cases - Design principles and design patterns in test automation (Abstraction, Modularity, Separation of data from scripts) - Designing a test automation solution |
| Verifying the Test Automation Solution (TAS) | - Verifying that the automation code itself is correct - Checking the test environment setup - Automated test suite verification |
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NEW QUESTION # 67
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: B
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 # 68
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 # 69
(In User Acceptance Testing (UAT) for a new SUT, in addition to the manual tests performed by the end- users, automated tests are performed that focus on the execution of repetitive and routine test scenarios. In which of the following environments are all these tests typically performed?)
Answer: C
Explanation:
TAE distinguishes test environments by purpose and risk. User Acceptance Testing is typically performed in an environment that is as production-like as feasible (configuration, data shape, integrations) but still controlled and safe for testing activities. This is commonly referred to aspreproduction(often "staging"): it supports realistic end-to-end flows, allows business users to validate that the SUT meets acceptance criteria, and enables running routine/repetitive automated checks without risking live operations. A build environment is focused on compiling/packaging and basic verification, not business acceptance. An integration environment is used to validate interactions among components/systems, but may not reflect full production- like configuration, and it's often shared and volatile-less suitable for formal acceptance activities involving end users. Production is generally avoided for UAT because acceptance testing can alter live data, disrupt users, and introduce unacceptable business risk; production testing is typically limited to tightly controlled smoke checks, monitoring, or specific "in-production" validation patterns with strong safeguards. Therefore, the environment in which both end-user manual UAT and supporting automated routine scenarios are typically executed is thepreproduction environment, aligning with TAE's guidance on balancing realism with risk containment.
NEW QUESTION # 70
You are reviewing the testability of your SUT.
Which of the following BEST refers to the characteristic of OBSERVABILITY?
Answer: D
NEW QUESTION # 71
Which of the following is NOT an advantage of test automation?
Answer: B
NEW QUESTION # 72
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