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| Section | Objectives |
|---|---|
| Topic 1: Testing Process in the Test Analyst Role | - Test analysis and design
|
| Topic 2: Test Tools and Test Automation Support | - Tool support for test analysis and execution
|
| Topic 3: Testing Techniques for Test Analysis | - Experience-based techniques
|
| Topic 4: Testing of Software Quality Characteristics | - Non-functional quality aspects
|
| Topic 5: Reviews and Defect Management | - Static testing and review process
|
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NEW QUESTION # 45
Your project is developing an application to process claims against insurance policies. Which of the following is a good example of CRUD completeness testing?
Answer: C
Explanation:
The correct answer is C because CRUD completeness testing is a static activity. CTAL-TA v4.0 defines CRUD as create, read, update, and delete, and states that CRUD testing verifies the lifecycle of data entities processed by the test item. The syllabus distinguishes two parts: CRUD completeness testing , which statically verifies whether all possible CRUD operations occur for every entity, and CRUD consistency testing , which dynamically checks whether functions interact correctly when handling the entity.
For an insurance-claims application, the key entity is the claim . A technical review checking whether the architecture/specification includes the ability to create, read, update, and delete claim records directly matches CRUD completeness. It is concerned with whether the full claim lifecycle has been included, not whether the lifecycle behaves correctly during execution. Option A is a strong example of CRUD consistency testing because it executes a lifecycle sequence and checks post-delete behavior. Option B is also dynamic execution, so it is not the best match for completeness. Option D is requirements elicitation completeness, not specifically CRUD completeness for the implemented claim entity. Reference: CTAL-TA v4.0, Section 3.2.1 CRUD Testing .
NEW QUESTION # 46
You are working on a project to build a purchasing system. The main requirements for the system are that it shall:
* Allow users to enter details of items that they wish to have purchased and, from these details, create a purchase request.
* Take each purchase request through a workflow that will allow the requestor's line manager to approve the request, reject it or return it for clarification / modification.
* Forward approved requests to the Purchasing Department.
* Allow purchasers to find the best available supplier for each approved request and place the request on that supplier as a purchase order.
The solution is being developed according to the company's traditional V-model methodology. The requirements are clearly documented and include a business process model.
System testing has finished and the test manager has asked you, the senior TA, to identify any parts of the product that should be focused on during acceptance testing. The results of this may be used to adjust the acceptance test plan.
You are using defect cluster analysis, only counting defects with severity levels 1 to 3, ignoring low severity levels 4 and 5. Function point analysis was used for estimating, so you have used function points as the unit of size for the various functional areas of the product. No numeric estimates of expected defect quantities were made for them individually but, as a result of product risk analysis, these functional areas have been ranked according to the amount of product risk that was predicted in them, with risk ranking 1 being the highest and
5 the lowest; the thoroughness of the testing performed so far has been proportional to that. The results are presented in the table below.
Functional area
Total function points
Product risk ranking
Defects found
Purchase Request entry
2000
2
30
Purchase Request workflow
1500
4
15
Purchase Request authorisation
1000
5
25
Supplier identification
2000
1
30
Purchase Order creation
1000
3
30
Which functional areas would you recommend for more test focus in acceptance testing?
a) Purchase Request entry
b) Purchase Request workflow
c) Purchase Request authorization
d) Supplier Identification
e) Purchase Order creation
Answer: B
Explanation:
The correct answer is C because defect cluster analysis must not be based on raw defect counts alone. CTAL- TA v4.0 states that after testing, the Test Analyst can identify actual defect-prone areas and compare predicted versus actual defect clusters; where discrepancies appear, more rigorous testing may be needed in those areas.
Here, size must be normalized using function points. The defect densities are: Purchase Request entry 30
/2000 = 15 defects per 1000 FP , Purchase Request workflow 15/1500 = 10 defects per 1000 FP , Purchase Request authorization 25/1000 = 25 defects per 1000 FP , Supplier identification 30/2000 = 15 defects per
1000 FP , and Purchase Order creation 30/1000 = 30 defects per 1000 FP .
The strongest actual clusters are therefore Purchase Order creation and Purchase Request authorization .
This is even more significant because authorization had the lowest predicted product risk ranking, 5 , so it should not have produced such a high defect density if the original risk assessment and test focus were accurate. Purchase Order creation also shows the highest defect density despite only medium predicted risk, ranking 3. Supplier identification and Purchase Request entry both have 30 defects, but they are larger areas and have only 15 defects per 1000 function points; Supplier identification was also the highest predicted risk area, so more defects there are less surprising. Reference: CTAL-TA v4.0, Software Defect Prevention , test result analysis and predicted-versus-actual defect cluster analysis.
NEW QUESTION # 47
The following activity diagram, drawn in Business Process Modeling Notation (BPMN), shows the process of raising a Purchase Request (PR), getting it authorized and raising a Purchase Order from it.
What is the minimum quantity of test cases that will be needed to cover all scenarios?
Answer: B
Explanation:
The correct answer is B , because the BPMN model contains two executable end-to-end scenarios . CTAL- TA v4.0 classifies scenario-based testing as a behavior-based technique where the Test Analyst creates a scenario model from action sequences that form workflows through the test item. The syllabus specifically mentions activity diagrams and BPMN-style workflow models as scenario-model sources, and states that test cases are designed to cover the identified scenarios.
In the diagram, the process starts when something is needed and the merchandiser enters the details of goods wanted. The manager then decides whether to authorize the PR. From that decision, there are only two real scenario outcomes. The first is the authorized scenario : the PR is authorized, the supplier is identified, the PR is updated and a Purchase Order is created, and the Purchase Order is sent. The second is the rejected scenario : the PR is rejected, the PR is cancelled, and the merchandiser considers the consequence of rejection.
The notification/result-of-authorization flow does not create separate scenarios; it is part of the same authorized or rejected process path. The note saying the merchandiser may try again is not a modeled loop because no BPMN flow returns to the start. CTAL-TA states that when the scenario model has no loops, each scenario can be tested with a separate test case. Therefore, the minimum quantity is 2 test cases .
NEW QUESTION # 48
In which of the following circumstances is the transition from a high-level test case to a low-level test case MOST LIKELY to be deferred from test design to test implementation?
Answer: B
Explanation:
The correct answer is D . CTAL-TA v4.0 distinguishes between high-level test cases and low-level test cases
. A high-level test case describes the circumstances under which the test object is examined and indicates the test conditions covered, but it does not provide concrete preconditions, input data, expected outputs, or postconditions. A low-level test case is the detailed refinement of that high-level test case and specifies concrete test data, tester actions, expected results, and postconditions.
The syllabus explicitly states that the transition from high-level to low-level test cases is often deferred from test design to test implementation, especially if specific test data is needed . This is because concrete test data may depend on available databases, valid account records, configured environments, external interfaces, data privacy restrictions, or generated identifiers. Until those implementation details are known, a high-level test case may remain abstract.
Option A is incorrect because the execution schedule affects sequencing, not necessarily the abstraction level of a test case. Option B argues for making the test case more understandable, not deferring refinement. Option C is wrong because product risk analysis should guide test prioritization and coverage, but it is not the direct reason for delaying low-level test-case derivation. Reference: CTAL-TA v4.0, Section 1.3.1 High-Level Test Cases and Low-Level Test Cases .
NEW QUESTION # 49
A written specification describes the rules for granting membership of your company's sports facility, whether it must be paid for if granted and whether free transport to and from the facility will be provided.
These actions depend upon employment status (permanent, temporary or probationary), the result of a medical exam (passed, failed or not taken) and whether the person is classed as a local resident. The best decision table that could be created from this specification is shown below.
Which of the following conclusions is correct?
Answer: C
Explanation:
The correct answer is A because the decision table has the correct structural completeness, but it exposes missing rules in the written specification. There are three condition dimensions: employment status has three values, medical exam has three values, and local residency has two values. Therefore, the complete decision table requires 3 × 3 × 2 = 18 rule columns , and the table contains exactly 18 rules. The unresolved entries appear in rules 17 and 18 , where the person is probationary , the medical exam is not taken , and local residency is either yes or no . Since all action cells for these two combinations are marked with question marks, the model has revealed that the specification does not define the required behavior for those cases.
CTAL-TA v4.0 places decision table testing under rule-based test techniques and states that it is used when behavior is determined by combinations of conditions, such as business rules. It is especially useful for finding omissions, inconsistencies, ambiguities, and gaps in rule specifications. Option B is wrong because the table has the correct number of columns. Option C is wrong because models are deliberately used to clarify text-based specifications. Option D is wrong because a decision table is the correct model type for condition-action business rules. Reference: CTAL-TA v4.0, Section 3.3.1 Decision Table Testing .
NEW QUESTION # 50
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