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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Preparing for Test Automation | 14% | - Cost, effort and ROI analysis - Identifying automation opportunities and constraints - Assessing system testability and architecture - Evaluating and selecting test tools |
| Topic 2: Implementation and Deployment Strategies | 12% | - Configuration management and version control - Test execution strategies and environment management - Integration with CI/CD pipelines - Test data management approaches |
| Topic 3: Test Automation Architecture | 15% | - Interoperability and integration concepts - Generic Test Automation Architecture (gTAA) - Layered frameworks and separation of concerns - Design principles and patterns for automation |
| Topic 4: Continuous Improvement | 19% | - Adapting to new technologies and requirements - Streamlining and maintaining test assets - Refactoring and optimizing automation - Upgrading tools and frameworks |
| Topic 5: Reporting and Metrics | 12% | - Visualization and dashboards - Defining relevant automation metrics - Reporting to stakeholders - Collecting and analyzing data |
| Topic 6: Verifying the Test Automation Solution | 12% | - Assessing quality and reliability of automation - Verifying automation code and infrastructure - Validating test suite correctness |
| Topic 7: Introduction and Objectives for Test Automation | 5% | - Roles and responsibilities of a Test Automation Engineer - Purpose, benefits and limitations of test automation - Test automation in software development lifecycle models |
| Topic 8: Implementing Test Automation | 11% | - Developing and maintaining automation components - Managing technical debt - Handling synchronization, stability and reliability - Planning and running pilot projects |
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17. Frage
You are a TAE responsible for a TAS that is used on several products, all of which have complex UIs. A substantial proportion of the automated tests break on a regular basis, usually for reasons associated with the UI. The investigation and maintenance this requires is significantly reducing the productivity of the teams and the return on investment.
You are looking for ways to improve the test cases in this TAS. Which of the following approaches would be MOST appropriate?
a) Artificial intelligence
b) Test histograms
c) Reliability growth modelling
d) Schema validation
e) Operational profiles
Antwort: A
Begründung:
Both artificial intelligence and test histograms directly address unstable UI automation. CTAL-TAE explains that test histograms visualize test-result and test-data trends and help TAEs identify fragile test cases that should be refactored or redesigned. This is particularly useful when a substantial proportion of tests repeatedly break. AI and machine-learning techniques provide an additional UI-specific improvement: tools can detect changed locator values, identify replacement selectors through ML or image-recognition techniques, and apply self-healing mechanisms that reduce locator-related maintenance. Schema validation is primarily applicable to API and database data structures rather than complex UI instability. Reliability growth modelling and operational profiles address software reliability concerns rather than recurring automated UI maintenance. Therefore, combining test histograms for identifying fragile tests with AI-based self-healing for UI locator changes gives the strongest solution.
(https://www.istqb.org/wp-content/uploads/2024/11/ISTQB_CTAL-TAE_Syllabus_v2.0.pdf)
18. Frage
An API's response to a request made to the corresponding endpoint should return some specific data about a payment transaction in JSON format. In particular, your goal is to write the test automation code, keeping it as short as possible, aimed at determining whether that response includes certain properties (transaction_id, amount, status, timestamp) with the data types and formats expected. Assuming that the TAF provides all the necessary support to validate the specified API response, how would you BEST achieve your goal?
Antwort: B
Begründung:
TAE encourages using the highest-leverage validation mechanisms available in the framework/tooling to keep tests concise, expressive, and maintainable. When validating JSON responses for presence of fields plus correct data types and formats, schema-based validation (e.g., JSON Schema or an equivalent contract/schema mechanism provided by the TAF) is typically the most efficient approach. It allows you to declare the expected structure once (required properties, types, constraints such as regex/date-time format, numeric ranges) and then validate the whole response in a single operation. This minimizes code and reduces repetitive assertions while producing clearer diagnostics when validation fails. Option B can work but usually results in more lines of code and repeated checks, and it is easier to miss constraints (e.g., timestamp format). Option D increases code volume and duplication by re-implementing parsing and validation logic that the TAF already provides, increasing maintenance burden. Option C is irrelevant to the goal of validating response properties
/types/formats. Therefore, specifying an expected schema and validating the response against it is the best way to keep code short and aligned with TAE maintainability recommendations.
19. Frage
Which statement about layering in a test automation framework is TRUE?
Antwort: D
Begründung:
TAF layers establish boundaries between classes or components that perform similar purposes, such as test execution, reporting, logging, encryption, test harness functions, or test scripts. Layering can provide useful separation of concerns and abstraction; however, the CTAL-TAE syllabus explicitly warns that introducing a separate layer for every individual purpose can make the framework design unnecessarily complicated.
Consequently, it recommends keeping the number of TAF layers low. The objective is not specifically to optimize execution efficiency, as stated in option B, but to control architectural complexity and avoid excessive abstraction. Options A and C advocate creating a layer for every different purpose, which is precisely the design approach the syllabus cautions against. Therefore, maintaining only a limited number of meaningful layers to minimize framework complexity is the correct principle.
(https://istqb.org/wp-content/uploads/2024/11/ISTQB_CTAL-TAE_Syllabus_v2.0.pdf)
20. Frage
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?
Antwort: C
Begründung:
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.
21. Frage
Which of the following statements about the relationship between TAA, TAS and TAF is true?
Antwort: A
Begründung:
In TAE terminology, the Test Automation Architecture (TAA) is the conceptual, high-level blueprint that describes how automation will be structured, what layers exist, how components interact, and how the automation connects to the SUT and supporting systems. The Test Automation Solution (TAS) is the concrete realization of that architecture in a specific context-tools, infrastructure, pipelines, conventions, and components assembled to deliver automated testing capability. The Test Automation Framework (TAF) is a structured set of reusable libraries, guidelines, and mechanisms that supports efficient development, execution, reporting, and maintenance of automated tests; it is commonly a key part used to build the TAS.
TAE documents commonly present this relationship as: TAA (design) # implemented as TAS (solution) # constructed using one or more TAFs (framework elements) plus tools and environment components. Options B, C, and D invert these relationships and misrepresent the concept that architecture is implemented by a solution, not the other way around. Therefore, the statement that a TAF can be used to implement a TAS, which is an implementation of a TAA, is the correct relationship.
22. Frage
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