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| Section | Objectives |
|---|---|
| Topic 1: Testing Techniques for Test Analysis | - Experience-based techniques
|
| Topic 2: Testing of Software Quality Characteristics | - Functional suitability testing
|
| Topic 3: Testing Process in the Test Analyst Role | - Test planning, monitoring, and control in advanced testing contexts
|
| Topic 4: Reviews and Defect Management | - Defect lifecycle and reporting
|
| Topic 5: Test Tools and Test Automation Support | - Tool support for test analysis and execution
|
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NEW QUESTION # 42
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 changes.
Requirement 3 is new and has been clearly specified.
The entry of forecasting parameters and of requests to see the reports will be low volume, but the Merchandising Manager is concerned about usability because the forecasting parameters are complex.
Which test charter would be BEST for the initial exploratory testing of requirement 3?
Answer: A
Explanation:
The correct answer is C . CTAL-TA v4.0 describes a session-based exploratory test charter as a focused mission, commonly structured as Explore [target] With [resources] To discover [information] . The charter should focus the tester's limited exploratory effort on the highest-value area, using suitable resources and a clear discovery objective. The syllabus includes test charters supporting session-based testing under test analysis and design.
Here, the relevant new functionality is Requirement 3 , especially the entry of complex forecasting parameters through a browser-based front end. The Merchandising Manager's explicit concern is usability , not functional correctness of the existing actual sales reports and not performance. The scenario also states that entry of forecasting parameters and report requests will be low volume , which weakens performance as the initial exploratory objective.
Option A targets report request entry, but the problem statement says the complexity and concern are around forecasting parameters , not the act of requesting a report. Option B targets Requirement 2, the existing actual sales report production, rather than the new Requirement 3 functionality. Option D uses the right target but the wrong objective: performance is not the main stated risk for this initial exploratory session. Reference:
CTAL-TA v4.0, Section 3.4.1 Test Charters Supporting Session-Based Testing and Section 4.2 Usability Testing .
NEW QUESTION # 43
You are assigned to an Agile team as a Test Analyst. In most ways, the team seems to be working well.
Iteration planning goes smoothly because the user stories are good quality. Most defects that are found by testing are fixed in the same iteration and those that do go live are not causing significant dissatisfaction amongst stakeholders. However, the team is unhappy because the Defect Removal Efficiency rate, measured for each iteration, is less than 80%.
What would be the MOST effective contribution by you as a TA, to improve the situation?
Answer: A
Explanation:
The correct answer is D . The problem described is not obviously poor requirements quality, poor Agile discipline, or severe stakeholder dissatisfaction. The visible issue is a defect metric : Defect Removal Efficiency is below 80% for each iteration. The most effective Test Analyst contribution is therefore to improve the collection, classification, interpretation, and evaluation of defect data before the team acts on the metric. CTAL-TA v4.0 states that test results provide feedback that helps the TA improve defect detection effectiveness, and that measures such as defect detection percentage require careful boundaries, escaped- defect counting rules, and exclusion criteria. A low value indicates ineffective defect detection, so the TA should analyze the reasons and propose focused improvements.
This scenario strongly suggests that the team may be reacting to a headline metric without understanding what is being counted. For example, escaped defects may be minor, duplicates may be counted inconsistently, defects may belong to earlier work, or the iteration boundary may distort the figure. CTAL-TA also warns that defect metrics are not always easy to read because failed tests and defects can have many-to-many relationships and severities may differ.
Option B , root cause analysis, is useful after credible defect classification exists, but it is premature as the primary action. Options A and C are too generic. Reference: CTAL-TA v4.0, Sections 5.3.1 Analyzing Test Results to Improve Defect Detection and 5.3.2 Supporting Root Cause Analysis with Defect Classification .
NEW QUESTION # 44
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 # 45
A simple DVD player is described in the following state transition diagram:
States shown in the diagram:
All round trips are feasible, even those that do not start at OFF. How many test cases will be needed for complete round-trip coverage?
Answer: B
Explanation:
The correct answer is B , because complete round-trip coverage requires every feasible round trip in the state model to be covered, not only those that start from the initial OFF state. CTAL-TA v4.0 explicitly includes state transition testing with N-switch and round-trip coverage as the focus of its state transition topic. A round trip is a path that starts and ends in the same state, normally without repeating an intermediate state.
The feasible round trips are:
* OFF # PLAY # OFF
* PLAY # OFF # PLAY
* OFF # REC # OFF
* REC # OFF # REC
* REC # REC PAUSE # REC
* REC PAUSE # REC # REC PAUSE
* PLAY # PLAY PAUSE # PLAY
* PLAY PAUSE # PLAY # PLAY PAUSE
* OFF # REC # REC PAUSE # OFF
* REC # REC PAUSE # OFF # REC
* REC PAUSE # OFF # REC # REC PAUSE
That gives 11 round trips. The warning in the question is important: if only OFF-starting round trips were counted, the answer would be too low. Option A overcounts by adding a non-modeled or duplicate round trip.
Options C and D miss feasible non-OFF-starting round trips. Reference: CTAL-TA v4.0, Section 3.2.2 State Transition Testing , round-trip coverage.
NEW QUESTION # 46
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?
Answer: B
Explanation:
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 .
NEW QUESTION # 47
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