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| Section | Weight | Objectives |
|---|---|---|
| Test Analysis and Test Design | 51% | - Experience-based and defect prevention techniques
|
| Test Implementation and Execution | 10% | - Defect logging and root cause analysis support - Designing test procedures and test cases - Test data preparation and management - Executing tests and documenting results |
| The Test Analyst Role in the Software Development Lifecycle | 15% | - Contribution to test strategy and planning - Test analyst tasks across SDLC models - Work products and stakeholder communication |
| Test Analyst Tasks in Risk-Based Testing | 15% | - Risk-based test prioritization and scope definition - Product risk analysis and assessment - Regression testing scope based on change impact |
| Evaluating Test Results and Reporting | 9% | - Reporting test progress and quality status - Assessing requirement and risk coverage - Analyzing test results and coverage - Improving test processes and defect detection |
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NEW QUESTION # 28
You are your employer's most experienced TA. The organisation performs root cause analysis (RCA) thoroughly, but infrequently and only on individual defects that went live at the highest of the five Severity levels.
After six months of live operation for one system, particular concern has been expressed about the volume of usability and security defects and there is a strong desire to reduce their quantities. You have recommended adding product quality characteristics, per ISO 25010, to the defect report template.
How might this recommendation support RCA?
Answer: A
Explanation:
The correct answer is B . The organization already performs RCA, but only infrequently and only for individual live defects at the highest severity level. That approach misses recurring lower-severity or medium- severity defect patterns, especially where many defects share the same quality concern. CTAL-TA v4.0 includes supporting root cause analysis with defect classification and aligns the syllabus with the ISO/IEC
25010:2023 product quality model, including quality characteristics such as functional suitability, usability, security, compatibility, and others.
Adding Quality Characteristic to the defect report template enables defects to be grouped by the quality attribute they affect. Combined with Severity , this allows the team to isolate clusters such as high-volume usability defects or serious security defects and then perform RCA on a meaningful defect group rather than only on isolated live failures. That directly supports the stated goal: reducing the quantities of usability and security defects.
Option A is partly useful because Pareto analysis can identify high-volume categories, but by itself it is defect counting, not the strongest RCA support. Option C concerns defect detection percentage, which is a different analysis objective. Option D is weak because "how the defect was fixed" may help investigation, but it does not provide the systematic classification needed to target recurring root causes. Reference: CTAL-TA v4.0, Sections 5.3.2 Supporting Root Cause Analysis with Defect Classification and Appendix F Software Quality Model .
NEW QUESTION # 29
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 # 30
Your project is developing an application to process claims against insurance policies. Which of the following is a good example of CRUD completeness testing?
Answer: A
Explanation:
The correct answer is C because CRUD completeness testing is a static activity. CTAL-TA v4.0 defines CRUD as create, read, update, and delete, and states that CRUD testing verifies the lifecycle of data entities processed by the test item. The syllabus distinguishes two parts: CRUD completeness testing , which statically verifies whether all possible CRUD operations occur for every entity, and CRUD consistency testing , which dynamically checks whether functions interact correctly when handling the entity.
For an insurance-claims application, the key entity is the claim . A technical review checking whether the architecture/specification includes the ability to create, read, update, and delete claim records directly matches CRUD completeness. It is concerned with whether the full claim lifecycle has been included, not whether the lifecycle behaves correctly during execution. Option A is a strong example of CRUD consistency testing because it executes a lifecycle sequence and checks post-delete behavior. Option B is also dynamic execution, so it is not the best match for completeness. Option D is requirements elicitation completeness, not specifically CRUD completeness for the implemented claim entity. Reference: CTAL-TA v4.0, Section 3.2.1 CRUD Testing .
NEW QUESTION # 31
A pricing system for determining the postage cost of a company's letters examines two input variables, representing the length L of the envelope in centimeters and the weight W of the letter in grams. Envelopes can only have three lengths: 15 cm Small, 20 cm Standard, and 25 cm Large.
For standard length envelopes, 20 cm, a premium postage rate is applied if the weight is equal to or greater than 50 grams. This is represented as:
IF L = 20 AND W > = 50 THEN RETURN Cost "Premium rate"
Which set of test points, each represented as variables L, W , achieves 100% simplified domain coverage for the Standard letter postage cost domain?
Answer: B
Explanation:
The correct answer is B . Simplified domain coverage requires boundary-focused ON and OFF points. CTAL- TA v4.0 states that for borders defined by < , #, > , or #, simplified domain coverage requires one ON and one OFF point. For an equality border, it requires one ON point and two OFF points on different sides of the border, with OFF points as close as possible according to the given precision.
The premium Standard-letter domain is defined by L = 20 and W > = 50 . For the W > = 50 border, (20,50) is the ON point because 50 is included, and (20,49) is the closest OFF point. For the L = 20 equality border, (20,50) also serves as the ON point, while (15,60) and (25,60) are OFF points on the two available sides of the Standard envelope length, because envelope lengths are restricted to 15, 20, and 25 cm.
Option A includes unnecessary extra points and does not represent the minimal required simplified coverage set. Option C uses 19 and 21, which are invalid envelope lengths. Option D misses the closest OFF point for the inclusive weight boundary because it uses 51 instead of 49. Reference: CTAL-TA v4.0, Section 3.1.1 Domain Testing , simplified domain coverage.
NEW QUESTION # 32
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: A
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 # 33
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