ISQI CTFL_SYLL_4.0 VALID EXAM PAPERS | RELIABLE CTFL_SYLL_4.0 TEST SAMPLE

ISQI CTFL_Syll_4.0 Valid Exam Papers | Reliable CTFL_Syll_4.0 Test Sample

ISQI CTFL_Syll_4.0 Valid Exam Papers | Reliable CTFL_Syll_4.0 Test Sample

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ISQI ISTQB Certified Tester Foundation Level (CTFL) v4.0 Sample Questions (Q156-Q161):

NEW QUESTION # 156
Which of the following statements is true?

  • A. Both in Agile software development and in sequential development models, such as the V-model, test levels tend to overlap since they do not usually have defined entry and exit criteria
  • B. In Agile software development, work product documentation tends to be lightweight and manual tests tend to be often unscripted as they are often produced using experience-based test techniques
  • C. In Agile software development, the first iterations are exclusively dedicated to testing activities, as testing will be used to drive development, which will be performed in the subsequent iterations
  • D. Sequential development models impose the use of systematic test techniques and do not allow the use of experience-based test techniques

Answer: B

Explanation:
This answer is correct because in Agile software development, work product documentation, such as user stories, acceptance criteria, or test cases, tends to be lightweight and concise, as the focus is on working software and frequent communication rather than comprehensive documentation. Manual tests tend to be often unscripted, as they are often produced using experience-based test techniques, such as error guessing or exploratory testing, which rely on the tester's skills, knowledge, and creativity to find defects and provide feedback. Reference: ISTQB Foundation Level Syllabus v4.0, Section 3.1.1.2, Section 3.2.1.2


NEW QUESTION # 157
The whole-team approach:

  • A. is a consensus-based approach that engages the whole team in estimating the user stories
  • B. is mostly adopted in projects aimed at developing safety-critical systems, as it ensures the highest level of testing independence
  • C. promotes the idea that all team members should be responsible for the quality of the product
  • D. promotes the idea that all team members should have a thorough understanding of test techniques

Answer: C

Explanation:
This answer is correct because the whole-team approach is a way of working in agile projects where all team members share the responsibility for the quality of the product, and collaborate on delivering value to the customer. The whole-team approach involves testers, developers, business analysts, product owners, and other stakeholders in planning, designing, developing, testing, and delivering the product. The whole-team approach fosters communication, feedback, learning, and continuous improvement within the team. References: ISTQB Glossary of Testing Terms v4.0, ISTQB Foundation Level Syllabus v4.0, Section
3.1.1.1


NEW QUESTION # 158
Which of the following statements about impact of DevOps on testing is CORRECT?

  • A. DevOps helps eliminate manual testing by having focus on continuous automated testing
  • B. DevOps helps shift focus of operations people to functional testing
  • C. DevOps helps shift focus of testing people to perform operations testing
  • D. DevOps helps bring focus on testing of non-functional characteristics

Answer: D

Explanation:
DevOps practices emphasize the importance of testing non-functional characteristics such as performance, security, and reliability. This focus ensures that the system not only meets functional requirements but also performs well under various conditions and is secure. DevOps promotes a continuous testing approach, which includes both functional and non-functional testing integrated into the development and deployment pipelines.
References:
* ISTQB CTFL Syllabus V4.0, Section 2.1.4 on DevOps and testing, which highlights the role of DevOps in emphasizing non-functional characteristics.


NEW QUESTION # 159
During component testing of a program if 100% decision coverage is achieved, which of the following coverage criteria is also guaranteed to be 100%?

  • A. 100% Statement coverage
  • B. 100% Stale transition coverage
  • C. 100% Boundary value coverage
  • D. 100% Equivalence class coverage

Answer: A

Explanation:
Statement coverage is a structural coverage metric that measures the percentage of executable statements in the source code that are executed by a test suite1. Decision coverage is another structural coverage metric that measures the percentage of decision outcomes (such as branches or conditions) in the source code that are executed by a test suite1. Decision coverage is a stronger metric than statement coverage, because it requires that every possible outcome of each decision is tested, while statement coverage only requires that every statement is executed at least once2. Therefore, if a test suite achieves 100% decision coverage, it also implies that it achieves 100% statement coverage, because every statement in every branch or condition must have been executed. However, the converse is not true: 100% statement coverage does not guarantee 100% decision coverage, because some branches or conditions may have multiple outcomes that are not tested by the test suite2. For example, consider the following pseudocode:
if (x > 0) then print("Positive") else print("Non-positive") end if
A test suite that executes this code with x = 1 and x = -1 will achieve 100% statement coverage, because both print statements are executed. However, it will not achieve 100% decision coverage, because the condition x >
0 has only been tested with two outcomes: true and false. The third possible outcome, x = 0, has not been tested by the test suite. Therefore, the test suite may miss a potential bug or error in the condition or the branch.
The other options, such as stale transition coverage, equivalence class coverage, and boundary value coverage, are not guaranteed to be 100% by achieving 100% decision coverage. Stale transition coverage is a structural coverage metric that measures the percentage of transitions between states in a state machine that are executed by a test suite3. Equivalence class coverage is a functional coverage metric that measures the percentage of equivalence classes (or partitions) of input or output values that are tested by a test suite4. Boundary value coverage is another functional coverage metric that measures the percentage of boundary values (or extreme values) of input or output ranges that are tested by a test suite4. These metrics are independent of decision coverage, because they are based on different aspects of the system under test, such as its behavior, functionality, or specification. Therefore, achieving 100% decision coverage does not imply achieving 100% of any of these metrics, and vice versa. References = ISTQB Certified Tester Foundation Level Syllabus v4.0, Test Coverage in Software Testing - Guru99, Structural Coverage Metrics - MATLAB & Simulink - MathWorks India, Test Design Coverage in Software Testing - GeeksforGeeks.


NEW QUESTION # 160
Which of the following statements about how different types of test tools support testers is true?

  • A. The support offered by a test data preparation tool is often leveraged by testers to run automated regression test suites
  • B. The support offered by a performance testing tool is often leveraged by testers to run load tests
  • C. The support offered by a continuous integration tool is often leveraged by testers to automatically generate test cases from a model
  • D. The support offered by a bug prediction tool is often used by testers to track the bugs they found

Answer: B

Explanation:
The support offered by a performance testing tool is often leveraged by testers to run load tests, which are tests that simulate a large number of concurrent users or transactions on the system under test, in order to measure its performance, reliability, and scalability. Performance testing tools can help testers to generate realistic workloads, monitor system behavior, collect and analyze performance metrics, and identify performance bottlenecks. The other statements are false, because:
A test data preparation tool is a tool that helps testers to create, manage, and manipulate test data, which are the inputs and outputs of test cases. Test data preparation tools are not directly related to running automated regression test suites, which are test suites that verify that the system still works as expected after changes or modifications. Regression test suites are usually executed by test execution tools, which are tools that can automatically run test cases and compare actual results with expected results.
A bug prediction tool is a tool that uses machine learning or statistical techniques to predict the likelihood of defects in a software system, based on various factors such as code complexity, code churn, code coverage, code smells, etc. Bug prediction tools are not used by testers to track the bugs they found, which are the actual defects that have been detected and reported during testing. Bugs are usually tracked by defect management tools, which are tools that help testers to record, monitor, analyze, and resolve defects.
A continuous integration tool is a tool that enables the integration of code changes from multiple developers into a shared repository, and the execution of automated builds and tests, in order to ensure the quality and consistency of the software system. Continuous integration tools are not used by testers to automatically generate test cases from a model, which are test cases that are derived from a representation of the system under test, such as a state diagram, a decision table, a use case, etc. Test cases can be automatically generated by test design tools, which are tools that support the implementation and maintenance of test cases, based on test design specifications or test models. Reference: ISTQB Certified Tester Foundation Level (CTFL) v4.0 sources and documents:
ISTQB® Certified Tester Foundation Level Syllabus v4.0, Chapter 3.4.1, Types of Test Tools ISTQB® Glossary of Testing Terms v4.0, Performance Testing Tool, Test Data Preparation Tool, Bug Prediction Tool, Continuous Integration Tool, Test Execution Tool, Defect Management Tool, Test Design Tool


NEW QUESTION # 161
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