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| Certification Vendor: | ISTQB |
|---|---|
| Exam Name: | ISTQB Certified Tester AI Testing (CT-AI) Exam |
| Exam Number: | CT-AI |
| Real Exam Qty: | 40 |
| Related Certifications: | ISTQB Certified Tester Foundation Level (CTFL) |
| Passing Score: | 65% |
| Exam Format: | Multiple Choice |
| Certificate Validity Period: | Lifetime |
| Exam Duration: | 60 minutes |
| Available Languages: | English |
| Recommended Training: | ISTQB Accredited Training Providers |
| Exam Registration: | ISTQB Official Website |
| Sample Questions: | ISTQB CT-AI Sample Questions |
| Exam Way: | Online and onsite proctored exam via accredited ISTQB examination providers |
| Pre Condition: | Recommended prior knowledge of ISTQB Foundation Level (CTFL) and basic understanding of software testing concepts and machine learning fundamentals. |
| Official Syllabus URL: | https://www.istqb.org |
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NEW QUESTION # 20
Which statement describes factors related to test data that make testing AI-based systems difficult?
Choose ONE option (1 out of 4)
Answer: D
Explanation:
Section2.2 - Data Preparationand4.1 - Challenges in Testing AI-Based Systemsdescribe difficulties in obtaining and managing large, representative datasets. AI-based systems requirerealistic, diverse, and representativedata reflecting real-world variations. The syllabus emphasizes that assembling such datasets is time-consuming, resource-intensive, and often constrained by availability, privacy, or domain complexity.
Option B directly corresponds to these documented challenges.
Option A is incorrect: using the same implementation risksdefect masking, not preventing it; the syllabus warns against this practice. Option C is incorrect because real-world data naturally evolves, and the syllabus notes thatdriftis normal; expecting stable input data contradicts operational reality. Option D is incorrect:
although data privacy is important, the syllabus does not claim that artificially generated data always requires legal approval, nor that sanitization/encryption is mandatory for synthetic data.
Thus,Option Baccurately reflects syllabus-defined difficulties in producing representative test data.
NEW QUESTION # 21
Which ONE of the following would be the LEAST effective input to an AI based test optimization process?
Answer: B
Explanation:
The correct answer is C. Test environment downtime . AI-based regression test optimization is intended to select, prioritize, and sometimes augment test cases so that the regression suite is more effective and efficient.
The CT-AI syllabus states that an AI-based tool can optimize a regression test suite by analyzing information such as previous test results , associated defects , and latest changes , including frequently broken features and tests that exercise recently impacted code.
Option A is useful because previously failing tests are part of historical test-result information and may indicate high-risk functionality. Option B is useful because associated defects help the tool learn which tests reveal real faults. Option D is useful because source-control data indicates recent code changes, impacted files, commit patterns, and potentially riskier components. Option C is the least effective because test environment downtime reflects infrastructure availability rather than product risk, code change impact, defect- prone functionality, or historical failure detection. It may affect scheduling, but it is weak as an input for optimizing which tests should be prioritized or selected.
References/topics: CT-AI Syllabus Chapter 11, Section 11.4 "Using AI for the Optimization of Regression Test Suites."
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NEW QUESTION # 22
Which of the following statements about reinforcement learning is correct?
Choose ONE option (1 out of 4)
Answer: C
Explanation:
Section1.6.3 - Reinforcement Learningof the ISTQB CT-AI syllabus states that reinforcement learning (RL) is based on anagent interacting with an environment, performing actions, and receivingrewards or penalties. The core concept is thereward function, which guides the agent's learning process. The syllabus emphasizes that training in RL isdriven by rewards, and the agent aims to maximize cumulative reward over time. Therefore, OptionCdirectly reflects the correct description: the agent learns by being rewarded for successful actions .
Option A is incorrect because RL doesnotuse labeled data; that applies to supervised learning. Option B contradicts the syllabus definition: RL fundamentallyrequiresinteraction with the environment. Option D is incorrect because the reward function isdefined by humans, not learned by the agent; the agent learns apolicy
, not the reward function itself.
Thus, OptionCis the only statement consistent with RL as defined in the syllabus.
NEW QUESTION # 23
Which ONE of the following statements is true about dynamic testing for inappropriate bias?
Answer: C
Explanation:
Dynamic testing for inappropriate bias often requires obtaining additional attributes about the data being processed. This is because bias can emerge based on hidden or unexamined features that influence the system's behavior. By collecting and analyzing more attributes, testers can better understand potential biases in the data and how they affect the AI system's decisions.
NEW QUESTION # 24
An e-commerce developer built an application for automatic classification of online products in order to allow customers to select products faster. The goal is to provide more relevant products to the user based on prior purchases.
Which of the following factors is necessary for a supervised machine learning algorithm to be successful?
Answer: B
Explanation:
Supervised machine learning requires correctly labeled data to train an effective model. The learning process relies on input-output mappings where each training example consists of an input (features) and a correctly labeled output (target variable). Incorrect labeling can significantly degrade model performance.
* Supervised Learning Process
* The algorithm learns from labeled data, mapping inputs to correct outputs during training.
* If labels are incorrect, the model will learn incorrect relationships and produce unreliable predictions.
* Quality of Training Data
* The accuracy of any supervised ML model ishighly dependent on the quality of labels.
* Poorly labeled data leads to mislabeled training sets, resulting inbiased or underperforming models.
* Error Minimization and Model Accuracy
* Incorrectly labeled data affects theconfusion matrix, reducing precision, recall, and accuracy.
* It leads to overfitting or underfitting, which decreases the model's ability to generalize.
* Industry Standard Practices
* Many AI development teams spend a significant amount of time ondata annotation and quality controlto ensure high-quality labeled datasets.
* (B) Minimizing the amount of time spent training the algorithm#(Incorrect)
* While reducing training time is important for efficiency, the quality of training is more critical. A well-trained model takes time to process large datasets and optimize its parameters.
* (C) Selecting the correct data pipeline for the ML training#(Incorrect)
* A good data pipeline helps, butit does not directly impact learning successas much as labeling does.Even a well-optimized pipeline cannot fix incorrect labels.
* (D) Grouping similar products together before feeding them into the algorithm#(Incorrect)
* This describesclustering, which is anunsupervised learning technique. Supervised learningrequires labeled examples, not just grouping of data.
* Labeled data is necessary for supervised learning."For supervised learning, it is necessary to have properly labeled data."
* Data labeling errors can impact performance."Supervised learning assumes that the data is correctly labeled by the data annotators.However, it is rare in practice for all items in a dataset to be labeled correctly." Why Labeling is Critical?Why Other Options are Incorrect?References from ISTQB Certified Tester AI Testing Study GuideThus,option A is the correct answer, ascorrectly labeled data is essential for supervised machine learning success.
NEW QUESTION # 25
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