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| Section | Objectives |
|---|---|
| Topic 1: Test Analysis and Test Design | - Experience-based Testing - Data-Based Test Techniques - Rule-Based Test Techniques - Behavior-Based Test Techniques |
| Topic 2: The Tasks of the Test Analyst in the Test Process | - Involvement in Test Activities - Tasks Related to Work Products - Testing in the Software Development Lifecycle |
| Topic 3: Testing Quality Characteristics | - Usability Testing - Functional Testing - Flexibility Testing - Compatibility Testing |
| Topic 4: The Tasks of the Test Analyst in Risk-Based Testing | - Risk Analysis - Risk Control |
| Topic 5: Software Defect Prevention | - Defect Prevention Practices - Supporting Phase Containment - Mitigating Recurrence of Defects |
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質問 # 22
As an experienced Test Analyst, you have been asked to collaborate in usability test sessions for a new shopping cart application. In which way would you BEST contribute to this activity?
正解：A
解説：
The correct answer is C because the Test Analyst's strongest contribution to usability test sessions is to help design realistic, representative scenarios that users will attempt during the session. CTAL-TA v4.0 states that usability test sessions involve future users or representatives attempting predefined tasks to determine whether those tasks can be completed effectively, efficiently, and satisfactorily. It further specifies that the Test Analyst may contribute by designing scenarios for usability sessions according to personas, user groups, or operational profiles.
For a shopping cart application, appropriate scenarios could include finding a product, adding multiple items, applying a discount code, changing quantities, selecting delivery options, abandoning and resuming a cart, and completing checkout. These scenarios expose usability barriers in the user journey rather than merely validating functional output.
Option A is too narrow: WAMMI is a questionnaire used to measure user satisfaction, and the Test Analyst may help design or evaluate questionnaires, but simply filling one out is not the best contribution. Option B incorrectly shifts the activity from user-centered usability evaluation to tester execution, and accessibility is only one aspect of usability. Option D is functional correctness testing, not usability testing. Reference:
CTAL-TA v4.0, Section 4.2 Usability Testing .
質問 # 23
The following decision table shows the rules for sending a package. Same day delivery will not be provided for big packages. All combinations of same day delivery and international destination have been removed as they are infeasible.
How many columns will the table have after all possible minimization has been performed?
正解：B
解説：
The correct answer is C because the fully minimized decision table has 8 columns . CTAL-TA v4.0 states that decision-table minimization derives an equivalent table with fewer rules by merging action-equivalent rules using the don't-care operator "-"; action-equivalent rules that differ in only one condition and cover all possible values of that condition may be merged. It also recommends disregarding or removing infeasible rules before merging.
For Small packages, columns 1 and 3 are action-equivalent: both charge 0% , provide insurance, and deliver same day. They differ only in High value , so they merge into one rule. Columns 2 and 4 cannot merge with them because same-day delivery and/or insurance differ. Therefore Small reduces from 4 rules to 3 . For Average packages, the same structure applies: columns 5 and 7 merge, while 6 and 8 remain separate.
Average also reduces from 4 rules to 3 . For Big packages, same-day is N/A , so columns 9 and 10 are action- equivalent and merge; columns 11 and 12 also merge. Big reduces from 4 rules to 2 .
Total minimized columns = 3 + 3 + 2 = 8 . Option A misses the valid merges for same-day insured Small and Average packages. Option D over-minimizes by combining rules that are not action-equivalent. Reference:
CTAL-TA v4.0, Section 3.3.1 Decision Table Testing .
質問 # 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?
正解：D
解説：
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 .
質問 # 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
正解：A
解説：
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.
質問 # 26
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?
正解：A
解説：
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 .
質問 # 27
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