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| Section | Objectives |
|---|---|
| Test Tools and Test Automation Support | - Tool support for test analysis and execution
|
| Testing Techniques for Test Analysis | - Specification-based techniques
|
| 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
|
| Reviews and Defect Management | - Static testing and review process
|
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NEW QUESTION # 13
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: B
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 # 14
You are developing test cases for an application which performs complex calculations and have decided to model these calculations on a spreadsheet, to determine expected results from inputs.
Which solution to the test oracle problem BEST describes the approach you are using?
Answer: C
Explanation:
The correct answer is D . A spreadsheet model used to calculate expected results is best classified as a pseudo- oracle when it has been developed independently from the test object and is used to produce expected outcomes for comparison with the system under test. CTAL-TA v4.0 explains that a test oracle is needed to determine expected results in dynamic testing, and that the test oracle problem occurs when a cost-effective oracle is not readily available because of factors such as data-related complexity, non-determinism, probabilistic behavior, or ambiguous requirements. The syllabus identifies pseudo-oracles as independently developed systems that fulfill the same specification as the test object, including simplified versions of the test object.
Here, the application performs complex calculations, and the spreadsheet is being used as an independent calculation model to determine expected results from the same inputs. That is the classic pseudo-oracle pattern. Option A is wrong because property-based testing verifies general input/output properties rather than exact expected calculation results. Option B is wrong because metamorphic testing checks relations between source and follow-up test cases, not a full expected result from a spreadsheet. Option C is not the named CTAL-TA oracle solution being used here. Reference: CTAL-TA v4.0, Section 1.3.4 Determining Test Oracles .
NEW QUESTION # 15
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.
The main release has now been live for three months and you are working on the first maintenance release.
The test manager has asked you, the senior TA, to identify those parts of the test process which were most and least effective at finding defects in the main release.
You have collected statistics for the independent test activities, i.e. excluding developers' testing, including only the 178 defects in the three highest of your five defect severity levels. The results are presented in the table below:
Assume that all defects not found at the first opportunity could have been, e.g. that all defects that escaped into acceptance testing were within the scope and objectives of system testing. By applying Defect detection percentage (DDP) analysis, which test activity was LEAST effective?
Answer: A
Explanation:
The correct answer is B . CTAL-TA v4.0 explains that Defect Detection Percentage is used to evaluate how effectively a test level or activity detects defects, and a low DDP indicates a high percentage of escaped defects, meaning the activity was ineffective and should be analyzed for improvement. The official CTAL-TA sample-answer material gives the formula as DDP = D / (D + E) , where D is defects detected in that phase and E is defects that escaped that phase and were detected later.
For test analysis including requirements review , only requirements defects are in scope at the first opportunity. It found 13 requirements defects, while 52 requirements defects escaped and were found later: 12 in test design, 20 in system testing, 10 in acceptance testing, and 10 in live operation. Therefore, DDP = 13 / (13 + 52) = 13 / 65 = 20% .
For comparison, test design found 40 of the 100 defects it could have found, so DDP = 40% . System testing found 75 of 125 possible remaining defects, so DDP = 60% . Acceptance testing found 25 of 50 possible remaining defects, so DDP = 50% . The lowest DDP is therefore test analysis, including requirements review . Reference: CTAL-TA v4.0, Section 5.3.1 Analyzing Test Results to Improve Defect Detection .
NEW QUESTION # 16
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: C
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 # 17
You are testing a program which should calculate the area of a trapezium and have decided to use metamorphic testing. The formula Area = (((a + b) / 2) * h) where a and b are the lengths of the two parallel sides, and h is the height when the parallel sides are horizontal.
Which ONE of the following would be an appropriate metamorphic relation?
Answer: A
Explanation:
The correct answer is B , but the question is slightly under-specified as written. CTAL-TA v4.0 defines a metamorphic relation as a property that describes how a change in test input is reflected in the expected result.
It is used to create follow-up test cases from a source test case and then evaluate the source and follow-up results jointly.
For the trapezium formula, if b changes while a and h remain unchanged, the area must change, not the height. Therefore, options C and D are structurally wrong because changing an input side length does not imply a change in h . Option A is also weak because it mixes an absolute increase in b with a fixed percentage increase in area, without considering the original values of a , b , and h . Option B is the only option that correctly expresses the expected-result relationship: a percentage increase in b causes a predictable percentage increase in area . Strictly, the exact percentage depends on the contribution of b to a + b ; the shown 2/3 relation is valid when b / (a + b) = 2/3 . Reference: CTAL-TA v4.0, Section 3.3.2 Metamorphic Testing .
NEW QUESTION # 18
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