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| Section | Objectives |
|---|---|
| Testing Techniques for Test Analysis | - Specification-based techniques
|
| Reviews and Defect Management | - Defect lifecycle and reporting
|
| Testing Process in the Test Analyst Role | - Test planning, monitoring, and control in advanced testing contexts
|
| Testing of Software Quality Characteristics | - Non-functional quality aspects
|
| Test Tools and Test Automation Support | - Tool support for test analysis and execution
|
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問題 #21
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?
答案：B
解題說明：
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 .
問題 #22
In your role as a Test Analyst for the development of a business application, you asked the Product Owner to help you understand how the various product risks would affect the users.
Which product risk management activity was this?
答案：A
問題 #23
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?
答案：C
解題說明：
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 .
問題 #24
You are working on a project to replace an old sales and merchandising system with a new system that will be based on modern platforms. The old system is still in production but is poorly documented and expensive to maintain. The new version will be developed using modern technology. The main requirements for the system are that it shall:
* Load sales data, sent electronically from the stores each evening, into a central database.
* Produce actual sales reports for the merchandisers, whose job is to manage stock levels in stores.
* Produce sales forecast reports, based on a combination of sales history and forecasting parameters. The parameters will be entered by the merchandisers into a browser-based front end and the reports will be produced overnight. The users will be able to see the reports next day, on screen or in print, by requesting this through the browser.
Requirements 1 and 2 are satisfied by the existing system and will be rewritten with no significant functional changes. Requirement 3 is new and has been clearly specified. Because of the poor test basis you have decided to perform experience-based functional correctness testing for requirements 1 and 2 with the support of checklists. Which would be the BEST items to include in the checklist for testing Requirement 2?
a) Does the sales quantity in the database for any one product in a particular store, equal the sales data input for that product / store?
b) Does the total sales value in the report for all products in a particular store, equal the sales data input for that store?
c) Does the sales quantity in the report for any one product in a particular store, equal the sales data input for that product / store?
d) Does successful login allow access to the report parameter entry features?
e) Does the total sales value in the database for all products in a particular store, equal the sales data input for that store?
答案：B
解題說明：
The correct answer is C because Requirement 2 is specifically about producing actual sales reports for merchandisers. CTAL-TA v4.0 states that checklist-based testing helps the Test Analyst cover known essential aspects of a test item, prevents overlooking critical areas, and supports experience-based testing when documentation is weak or changing. It also states that checklist items should be clear, specific, relevant, actionable, measurable, and answerable with yes, no, or not applicable.
Checklist item b is relevant because it verifies that the report total sales value for a store matches the sales input for that store. Checklist item c is also relevant because it verifies that the report shows the correct sales quantity for a specific product/store combination. These two checks directly test the functional correctness of the actual sales reports .
Item a is not the best fit because it checks data in the database , which belongs more directly to Requirement
1, loading sales data into the central database. Item d belongs to Requirement 3, because report parameter entry is part of the new sales forecast reporting feature. Item e again checks database content rather than report output. Reference: CTAL-TA v4.0, Section 3.4.2 Checklists Supporting Experience-Based Test Techniques and Section 4.1 Functional Testing .
問題 #25
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
答案：B
解題說明：
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.
問題 #26
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