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| Section | Objectives |
|---|---|
| Topic 1: Testing Techniques for Test Analysis | - Specification-based techniques
|
| Topic 2: Test Tools and Test Automation Support | - Tool support for test analysis and execution
|
| Topic 3: Testing Process in the Test Analyst Role | - Test analysis and design
|
| Topic 4: Reviews and Defect Management | - Static testing and review process
|
| Topic 5: Testing of Software Quality Characteristics | - Functional suitability testing
|
>> CTAL-TA_V4.0 PDF Question <<
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NEW QUESTION # 28
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 would like to purchase 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 system is being developed following an Agile SDLC. Risk-based testing is practiced informally, discussion about what testing each user story will need being done in a risk-driven way during story refinement, but there is no central product risk register. Testing is exploratory, except for the acceptance tests for each story's acceptance tests. Operational profiles have been created to guide performance testing.
Regression test automation started late. The team want to automate all acceptance tests for every user story, but the test automation engineers are still playing catch up. They started with the tests that were easiest to automate, so the functional and non-functional coverage of the regression test pack is incomplete and random.
The current iteration is delivering a major change to the approval workflow and you, as a Test Analyst, have been tasked with defining the regression testing.
Which techniques would be MOST effective for selecting regression tests?
a) History-based, driven by the results of the regression tests run so far b) Risk-based, driven by the product risks and their traceability to the tests c) Operational profile-based, driven by the likely patterns of use of the relevant transactions d) Coverage-based, driven by the coverage obtained from use of relevant test techniques e) Impact analysis, driven by consultation with technical stakeholders to identify what might be affected.
Answer: A
Explanation:
The correct answer is B because the most effective regression-selection combination in this situation is operational profile-based selection plus impact analysis . CTAL-TA v4.0 states that regression testing often cannot execute every regression test because of time, budget, environment, or data constraints, so appropriate regression tests must be selected and the scope must be reviewed every cycle. The syllabus identifies impact analysis as a regression selection technique that focuses tests on areas most likely to be affected by changed configuration items, and also recognizes operational-profile-based testing as useful for selecting tests according to likely usage patterns and critical business processes.
Here, the current change is a major approval workflow change , so impact analysis with technical stakeholders is essential to identify direct and indirect effects. Operational-profile-based selection is also appropriate because approval, rejection, clarification, forwarding, and purchase-order creation are transaction flows with realistic usage patterns. Option A is weak because history-based selection is unreliable when the existing regression pack is incomplete and randomly biased toward easy-to-automate tests. Option C is invalid because formal risk-based selection depends on product risks and traceability to tests, but the scenario explicitly says there is no central product risk register. Option D also includes the invalid risk-based pair.
Reference: CTAL-TA v4.0, Section 2.2 Risk Control / Regression Testing .
NEW QUESTION # 29
Your project is developing an application to generate quotes for motor insurance. The calculation requires a wide range of input data and a reliable model of it has been built in a spreadsheet. There will not be enough time to get adequate functional correctness coverage from the base choice or pairwise techniques, so you have decided to do some random testing based on a probability distribution of variable values that is weighted towards their business criticality.
Which limitation of random testing should you be MOST concerned about?
Answer: C
Explanation:
The correct answer is A because the major risk in this scenario is that randomly generated input combinations may fail to represent semantically meaningful insurance situations . CTAL-TA v4.0 defines random testing as selecting test data from the input domain according to a specified probability distribution, and explicitly lists its limitations as including "neglecting data semantics" and "potentially missing defects related to data meaning." In a motor insurance quote engine, defects often arise not merely from raw value combinations, but from the business meaning of combinations: driver age, vehicle type, claims history, postcode risk, policy excess, occupation, and coverage options may interact in ways that are legally or commercially significant.
Random testing weighted by business criticality can improve sampling relevance, but it still may not deliberately target these meaningful relationships unless they are explicitly modeled.
Option B is a known limitation, but redundancy is less critical here because the distribution can be controlled and the main concern is business-rule correctness. Option C is weakened because a reliable spreadsheet model already exists and can act as an oracle. Option D is incorrect because the issue is not simply validation versus verification; CTAL-TA notes that random testing distributions can support either purpose depending on how they are chosen. Reference: CTAL-TA v4.0, Section 3.1.3 Random Testing , under Test Analysis and Test Design .
NEW QUESTION # 30
A system will be developed to support the administration of table-tennis tournaments. The following requirements for the enrolment of entrants have been provided:
R1. High. The administrator shall be able to enter the entrant's name, address, phone number/s (mobile and/or home), email and social media handles. Validation rules for these data items are provided separately.
R2. High. The administrator shall be able to enter the entrant's current world ranking (up to 250) and their relationship with the tournament sponsors (i.e. clothing / equipment / endorsement).
R3. High. The administrator shall be able to enter the entrant's attendance history at previous events (i.e.
finalist / played / withdrew during event / withdrew before event).
R4. Medium. From the entrant's address the system shall derive whether or not the entrant lives locally and, on the basis of that combined with other rules that are provided separately, shall suggest how much of the tournament's overall prize fund should be offered to the entrant as their individual attendance fee.
R5. Low. The administrator should be able to change the suggested attendance fee and the system should then deduct this from the remaining prize fund balance accordingly.
You have been asked to review the requirements using perspective-based reading, from your viewpoint as the TA who will be writing functional correctness system test cases. Assume that the other rules referred to by requirements R1 and R4 have been reviewed and are satisfactorily testable. Which conclusion would you reach?
Answer: C
Explanation:
The correct answer is A . Using perspective-based reading, the TA must review the requirements from the viewpoint of someone who must derive functional correctness test cases. CTAL-TA v4.0 states that perspective-based reading requires reviewers to attempt to use the test basis to generate the work product they would derive from it; for a tester, that means attempting to create tests and checking whether all necessary information is present.
R1 is testable because the data-entry items are listed and the validation rules are explicitly provided separately and assumed testable. R4 is also testable because the derivation and attendance-fee rules are separately provided and assumed testable. R5 is testable as a functional rule: change the suggested attendance fee, then verify that the remaining prize fund balance is reduced accordingly.
The weak requirements are R2 and R3 . R2 does not fully define the valid model for "relationship with the tournament sponsors": it is unclear whether clothing, equipment, and endorsement are mutually exclusive, whether multiple relationships may apply, or whether "no relationship" is valid. R3 is also incomplete because "attendance history at previous events" is not fully defined: it is unclear whether this is one value, multiple event records, or a history per event, and some values overlap conceptually, such as finalist and played. These gaps prevent complete functional correctness test design. Reference: CTAL-TA v4.0, Sections
5.2.2 Applying Review Techniques and 1.2.1 Test Analysis .
NEW QUESTION # 31
During a sprint retrospective it was noted that some test cases were interpreted differently between the Product Owner and the Test Analysts. Which test case quality criteria may need some attention?
Answer: C
Explanation:
The correct answer is C . The phrase "interpreted differently" is the key. CTAL-TA v4.0 defines precision as the quality criterion that ensures there is only one interpretation of a test case. The syllabus states: "There should be only one interpretation of a test case to avoid false-negative and false-positive test results," and warns against ambiguous terms such as "suitable," "as needed," or "several." This is not mainly an understandability issue. A test case may be understandable in plain language but still imprecise if different stakeholders infer different exact expected outcomes, input values, business rules, or acceptance thresholds. In this scenario, both the Product Owner and Test Analysts can read the test cases, but they are deriving different meanings from them. That is ambiguity, and ambiguity is a precision failure.
Option A , understandability, would be more appropriate if stakeholders could not understand the language or structure of the test case at all. CTAL-TA says test cases should be understandable to all stakeholders and may need simplification if complex. Option B , consistency, concerns uniform language, formatting, and structure. Option D , completeness, concerns whether necessary attributes, test data, and expected results are present. Here the defect symptom is specifically multiple interpretations, so precision is the exact quality criterion being violated. Reference: CTAL-TA v4.0, Section 1.3.2 Quality Criteria for Test Cases .
NEW QUESTION # 32
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: A
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 # 33
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