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| Section | Weight | Objectives |
|---|---|---|
| Input Data Testing for Machine Learning Systems | 20% | - Validation of data pipelines and preprocessing - Label verification and ground truth assessment - Testing for bias, representativeness, and completeness - Data quality attributes |
| Machine Learning Development Testing | 10% | - Testing MLOps and deployment pipelines - Testing ML development workflows - Regression testing for retrained models |
| Test Approaches and Techniques for AI Systems | 10% | - A/B testing and back-to-back testing - Risk-based testing - Test environment and tool considerations - Exploratory testing and red teaming |
| Testing Generative AI and Large Language Models | 15% | - Specific risks and test approaches for LLMs - Evaluation of generative AI outputs - Generative AI fundamentals |
| Test Levels and Machine Learning Systems | 15% | - Testing across ML lifecycle stages - Unit, integration, system, and acceptance testing for ML - Test strategies for AI projects |
| Model Testing for Machine Learning Systems | 20% | - Adversarial and robustness testing - Explainability and transparency testing - Metamorphic and statistical testing - Drift detection and monitoring - Model performance and accuracy testing |
| Introduction to Testing AI-Based Systems | 10% | - AI-specific quality characteristics - Challenges in testing AI-based systems - Characteristics of AI and ML systems |
>> New Study CT-AI Questions <<
The ISTQB CT-AI certification exam is one of the top-rated and valuable credentials in the ISTQB world. This Certified Tester AI Testing Exam CT-AI exam questions is designed to validate the candidate's skills and knowledge. With ISTQB CT-AI exam dumps everyone can upgrade their expertise and knowledge level. By doing this the successful ISTQB CT-AI Exam candidates can gain several personal and professional benefits in their career and achieve their professional career objectives in a short time period.
NEW QUESTION # 155
Which of the following statements regarding experience-based testing for AI-based systems is correct?
Choose ONE option (1 out of 4)
Answer: D
Explanation:
The ISTQB CT-AI syllabus explains inSection 4.4 - Experience-Based Testing for AI Systemsthat AI- based systems frequently suffer frominsufficient specifications, unpredictable model behavior, andtest oracle problems, especially when outputs depend on probabilistic or learned patterns. The syllabus explicitly states thatexploratory testingis especially valuable in such contexts because it allows testers to investigate the system interactively, observe unexpected behavior, and evaluate system responses that cannot be fully predicted beforehand. Thus, OptionCaccurately reflects the role and justification of exploratory testing for AI systems.
Option A describes data analysis rather than intuitive test design. Option B is incorrect because checklist- based testing does not dynamically adapt test cases; instead, it follows predetermined checklists. Option D incorrectly defines "tour-based testing"; tours refer to structured exploratory approaches, not biased datasets.
Therefore,Option Cis the syllabus-aligned correct statement.
NEW QUESTION # 156
ln the near future, technology will have evolved, and Al will be able to learn multiple tasks by itself without needing to be retrained, allowing it to operate even in new environments. The cognitive abilities of Al are similar to a child of 1-2 years.' In the above quote, which ONE of the following options is the correct name of this type of Al?
SELECT ONE OPTION
Answer: B
Explanation:
* A. Technological singularity
Technological singularity refers to a hypothetical point in the future when AI surpasses human intelligence and can continuously improve itself without human intervention. This scenario involves capabilities far beyond those described in the question.
* B. Narrow AI
Narrow AI, also known as weak AI, is designed to perform a specific task or a narrow range of tasks. It does not have general cognitive abilities and cannot learn multiple tasks by itself without retraining.
* C. Super AI
Super AI refers to an AI that surpasses human intelligence and capabilities across all fields. This is an advanced concept and not aligned with the description of having cognitive abilities similar to a young child.
* D. General AI
General AI, or strong AI, has the ability to understand, learn, and apply knowledge across a wide range of tasks, similar to human cognitive abilities. It aligns with the description of AI that can learn multiple tasks and operate in new environments without needing retraining.
NEW QUESTION # 157
Which of the following is an example of a clustering problem that can be resolved by unsupervised learning?
Answer: B
Explanation:
Clustering is a form ofunsupervised learning, which groups data points based onsimilarities without predefined labels. According toISTQB CT-AI Syllabus, clustering is used in scenarios where:
* The objective is to find natural groupings in data.
* The dataset does not have labeled outputs.
* Patterns and structures need to be identified automatically.
Analyzing the answer choices:
* A. Associating shoppers with their shopping tendencies # Correct
* Shoppers can be grouped based on purchasing behaviors(e.g., luxury shoppers vs. budget- conscious shoppers), which is a typical clustering application in market segmentation.
* B. Grouping individual fish together based on their types of fins # Incorrect
* If thetypes of fins are labeled, it becomes aclassification problem, which requires supervised learning.
* C. Classifying muffin purchases based on packaging attractiveness # Incorrect
* Classification, not clustering, because attractiveness scores or labels must be predefined.
* D. Estimating the expected purchase of cat food after an ad campaign # Incorrect
* This is a prediction task, best suited forregression models, which are part of supervised learning.
Thus,Option A is the best answer, asclusteringis used togroup shoppers based on tendencies without predefined labels.
Certified Tester AI Testing Study Guide References:
* ISTQB CT-AI Syllabus v1.0, Section 3.1.2 (Unsupervised Learning - Clustering and Association)
* ISTQB CT-AI Syllabus v1.0, Section 3.3 (Selecting a Form of ML - Clustering).
NEW QUESTION # 158
A company is using a spam filter to attempt to identify which emails should be marked as spam.
Detection rules are created by the filter that causes a message to be classified as spam. An attacker wishes to have all messages internal to the company be classified as spam. So, the attacker sends messages with obvious red flags in the body of the email and modifies the from portion of the email to make it appear that the emails have been sent by company members. The testers plan to use exploratory data analysis (EDA) to detect the attack and use this information to prevent future adversarial attacks. How could EDA be used to detect this attack?
Answer: A
Explanation:
The syllabus explains that EDA can be used to analyze data to identify outliers and unusual patterns, which can indicate adversarial attacks like data poisoning:
"Testing to detect data poisoning is possible using EDA, as poisoned data may show up as outliers."
NEW QUESTION # 159
An Al based website will recommend holiday rental accommodation for a user based on their previous bookings, and inputs about their requirements. Metamorphic testing is being used by a test engineer, who has built up a history of previous bookings. The first test case they use (T1) is as follows:
The outputs from the test ate the following accommodations:
* Villa Fianca
* Villa Palma
* Apartments Mirasol
* Villa Paris
* Apartments Miramar
Which ONE of the below options are the MOST likely outputs from the test:
Answer: B
Explanation:
The correct answer is D . Metamorphic testing derives follow-up test cases from a source test case by applying a metamorphic relation, where a controlled change in input should produce a predictable relationship in the output. The syllabus states that a metamorphic relation describes how a change in test inputs is reflected in the expected results, and that follow-up expected results are relative to the source test case rather than absolute values.
Here, the source result set contains five recommendations. If the follow-up test tightens or partitions the pool requirement, the valid recommendations should be consistent subsets of the original recommendations. Option D gives a coherent partition: T2 returns the accommodations satisfying the pool-related requirement, while T3 returns the accommodations aligned with "pool not required." It does not introduce recommendations outside the original result set and it preserves the expected relation between changed preference criteria and the returned list. Options A and B are unnecessarily narrow, while C mixes the partition by including Villa Franca in both branches.
References/topics: CT-AI Syllabus Chapter 9, Section 9.5 "Metamorphic Testing"; Section 9.5.1 "Hands- On Exercise: Metamorphic Testing."
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NEW QUESTION # 160
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