Deal of The Day! Hurry Up, Grab the Special Discount - Save 25% - Ends In 00:00:00 Coupon code: SAVE25
Welcome to Pass4Success

- Free Preparation Discussions

iSQI CT-AI Exam Questions

Exam Name: Certified Tester AI Testing
Exam Code: CT-AI
Related Certification(s): iSQI ISTQB Certified Tester Certification
Certification Provider: iSQI
Number of CT-AI practice questions in our database: 80 (updated: Mar. 21, 2025)
Expected CT-AI Exam Topics, as suggested by iSQI :
  • Topic 1: Introduction to AI: This exam section covers topics such as the AI effect and how it influences the definition of AI. It covers how to distinguish between narrow AI, general AI, and super AI; moreover, the topics covered include describing how standards apply to AI-based systems.
  • Topic 2: Quality Characteristics for AI-Based Systems: This section covers topics covered how to explain the importance of flexibility and adaptability as characteristics of AI-based systems and describes the vitality of managing evolution for AI-based systems. It also covers how to recall the characteristics that make it difficult to use AI-based systems in safety-related applications.
  • Topic 3: Machine Learning ML: This section includes the classification and regression as part of supervised learning, explaining the factors involved in the selection of ML algorithms, and demonstrating underfitting and overfitting.
  • Topic 4: ML: Data: This section of the exam covers explaining the activities and challenges related to data preparation. It also covers how to test datasets create an ML model and recognize how poor data quality can cause problems with the resultant ML model.
  • Topic 5: ML Functional Performance Metrics: In this section, the topics covered include how to calculate the ML functional performance metrics from a given set of confusion matrices.
  • Topic 6: Neural Networks and Testing: This section of the exam covers defining the structure and function of a neural network including a DNN and the different coverage measures for neural networks.
  • Topic 7: Testing AI-Based Systems Overview: In this section, focus is given to how system specifications for AI-based systems can create challenges in testing and explain automation bias and how this affects testing.
  • Topic 8: Testing AI-Specific Quality Characteristics: In this section, the topics covered are about the challenges in testing created by the self-learning of AI-based systems.
  • Topic 9: Methods and Techniques for the Testing of AI-Based Systems: In this section, the focus is on explaining how the testing of ML systems can help prevent adversarial attacks and data poisoning.
  • Topic 10: Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based systems from those required for conventional systems.
  • Topic 11: Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Disscuss iSQI CT-AI Topics, Questions or Ask Anything Related

Rikki

6 days ago
Faced questions on AI deployment strategies. Review concepts like containerization and CI/CD for AI systems.
upvoted 0 times
...

Mila

8 days ago
Aced the iSQI exam! Pass4Success, thanks for making my study time count.
upvoted 0 times
...

Ezekiel

19 days ago
AI security was a key topic. Study potential vulnerabilities in AI systems and mitigation strategies.
upvoted 0 times
...

Kattie

1 months ago
Encountered questions on AI model validation. Understand cross-validation techniques and their importance.
upvoted 0 times
...

Lawrence

1 months ago
AI Testing certified! Pass4Success, your exam questions were worth every penny.
upvoted 0 times
...

Edelmira

2 months ago
The exam included questions on AI performance metrics. Know how to evaluate AI models using various metrics.
upvoted 0 times
...

Timothy

2 months ago
Computer vision topics appeared in the exam. Familiarize yourself with image classification and object detection concepts.
upvoted 0 times
...

Chantay

2 months ago
Success! iSQI AI Testing cert in the bag. Pass4Success made cramming actually effective.
upvoted 0 times
...

Martina

3 months ago
Faced questions on natural language processing. Study tokenization, sentiment analysis, and named entity recognition.
upvoted 0 times
...

Helene

3 months ago
Questions on AI explainability were tricky. Review techniques for interpreting AI model decisions.
upvoted 0 times
...

Devon

3 months ago
Passed the iSQI AI Testing exam today! Pass4Success, your questions were right on target.
upvoted 0 times
...

Merilyn

4 months ago
The exam tested knowledge of AI bias and fairness. Understand methods to detect and mitigate bias in AI systems.
upvoted 0 times
...

Margarita

4 months ago
Encountered scenario-based questions on AI testing strategies. Practice applying testing methods to real-world AI scenarios.
upvoted 0 times
...

Marvel

4 months ago
AI Testing cert acquired! Couldn't have done it without Pass4Success's relevant practice tests.
upvoted 0 times
...

An

5 months ago
AI ethics was a significant topic. Study ethical considerations in AI development and deployment.
upvoted 0 times
...

Jerry

5 months ago
Excited to announce that I passed the iSQI Certified Tester AI Testing exam. The Pass4Success practice questions were a great help. One question that puzzled me was about the different methods and techniques for testing AI-based systems, particularly the use of black-box testing versus white-box testing. It was a tough one!
upvoted 0 times
...

Temeka

5 months ago
Phew! Made it through the iSQI exam. Pass4Success, you're a gem for last-minute studying.
upvoted 0 times
...

Latrice

5 months ago
Faced challenges with data preprocessing questions. Focus on techniques like normalization and feature scaling.
upvoted 0 times
...

Nguyet

5 months ago
I passed the iSQI Certified Tester AI Testing exam, thanks to the practice questions from Pass4Success. There was a question about the role of test environments in AI-based systems, specifically how to simulate real-world conditions for testing. I had to think about various factors like data variability and system load.
upvoted 0 times
...

Catarina

6 months ago
The exam covered neural network architectures. Review perceptrons, CNNs, and RNNs.
upvoted 0 times
...

Lai

6 months ago
Happy to share that I passed the iSQI Certified Tester AI Testing exam. The practice questions from Pass4Success were spot on. One question I found challenging was related to the different quality characteristics specific to AI-based systems, like transparency and explainability. I wasn't entirely sure how to prioritize these characteristics in a testing scenario.
upvoted 0 times
...

Lashaunda

6 months ago
Nailed the AI Testing certification! Pass4Success materials were a lifesaver for quick prep.
upvoted 0 times
...

Gail

6 months ago
Encountered questions on machine learning algorithms. Make sure to understand supervised, unsupervised, and reinforcement learning.
upvoted 0 times
...

Cheryl

6 months ago
Just cleared the iSQI Certified Tester AI Testing exam! The Pass4Success practice questions were a lifesaver. There was a tricky question on the exam about the importance of data quality in machine learning models. It asked how missing data could affect model performance, and I had to think hard about the implications.
upvoted 0 times
...

Sharita

7 months ago
Just passed the iSQI Certified Tester AI Testing exam! Expect questions on AI fundamentals. Study different types of AI and their applications.
upvoted 0 times
...

Lynette

7 months ago
I recently passed the iSQI Certified Tester AI Testing exam, and I must say that the Pass4Success practice questions were incredibly helpful. One question that stumped me was about the different types of neural networks and their applications in testing. I wasn't sure if convolutional neural networks were best suited for image recognition tasks, but I managed to get through it.
upvoted 0 times
...

Janey

7 months ago
Just passed the iSQI Certified AI Testing exam! Thanks Pass4Success for the spot-on practice questions.
upvoted 0 times
...

Celeste

7 months ago
Thanks to Pass4Success practice questions, I passed the iSQI Certified Tester AI Testing exam with flying colors. The exam included topics such as standards for AI-based systems and characteristics that make it difficult to use AI-based systems in safety-related applications. One question that I remember struggling with was related to how standards apply to AI-based systems. Despite my initial confusion, I managed to pass the exam.
upvoted 0 times
...

Santos

8 months ago
My exam experience was great as I passed the iSQI Certified Tester AI Testing exam using Pass4Success practice questions. The exam covered topics like the importance of flexibility and adaptability in AI-based systems. One question that I found challenging was related to managing evolution for AI-based systems. Despite my initial uncertainty, I was able to pass the exam.
upvoted 0 times
...

Edmond

9 months ago
Passed the AI Testing exam on my first try! Pass4Success's questions were incredibly similar to the real thing. Thanks for the time-saving prep!
upvoted 0 times
...

Mariko

9 months ago
I successfully passed the iSQI Certified Tester AI Testing exam with the help of Pass4Success practice questions. The exam covered topics such as the AI effect and quality characteristics for AI-based systems. One question that stood out to me was related to distinguishing between narrow AI, general AI, and super AI. Although I was unsure of the answer at first, I managed to pass the exam.
upvoted 0 times
...

Rachael

10 months ago
Wow, the exam was challenging but I made it! Grateful for Pass4Success's relevant study materials. Couldn't have done it without them.
upvoted 0 times
...

Bernadine

10 months ago
Ethical considerations in AI testing are a key topic. You may encounter questions about bias detection and mitigation in AI systems. Familiarize yourself with fairness metrics and regulatory compliance in AI testing. Thanks to Pass4Success for providing relevant practice questions that helped me pass the exam in a short time!
upvoted 0 times
...

Dallas

10 months ago
Successfully cleared the AI Testing exam today! Pass4Success's materials were key to my quick preparation. Truly appreciate their help!
upvoted 0 times
...

Shanda

10 months ago
iSQI Certified Tester AI Testing - check! Pass4Success's practice exams were a lifesaver. Thank you for the accurate and efficient study resources!
upvoted 0 times
...

Vallie

11 months ago
Just passed the iSQI Certified Tester AI Testing exam! Pass4Success's practice questions were spot-on. Thanks for helping me prepare so quickly!
upvoted 0 times
...

Free iSQI CT-AI Exam Actual Questions

Note: Premium Questions for CT-AI were last updated On Mar. 21, 2025 (see below)

Question #1

A wildlife conservation group would like to use a neural network to classify images of different animals. The algorithm is going to be used on a social media platform to automatically pick out pictures of the chosen animal of the month. This month's animal is set to be a wolf. The test team has already observed that the algorithm could classify a picture of a dog as being a wolf because of the similar characteristics between dogs and wolves. To handle such instances, the team is planning to train the model with additional images of wolves and dogs so that the model is able to better differentiate between the two.

What test method should you use to verify that the model has improved after the additional training?

Reveal Solution Hide Solution
Correct Answer: D

Back-to-back testing is used to compare two different versions of an ML model, which is precisely what is needed in this scenario.

The model initially misclassified dogs as wolves due to feature similarities.

The test team retrains the model with additional images of dogs and wolves.

The best way to verify whether this additional training improved classification accuracy is to compare the original model's output with the newly trained model's output using the same test dataset.

Why Other Options Are Incorrect:

A (Metamorphic Testing): Metamorphic testing is useful for generating new test cases based on existing ones but does not directly compare different model versions.

B (Adversarial Testing): Adversarial testing is used to check how robust a model is against maliciously perturbed inputs, not to verify training effectiveness.

C (Pairwise Testing): Pairwise testing is a combinatorial technique for reducing the number of test cases by focusing on key variable interactions, not for validating model improvements.

Supporting Reference from ISTQB Certified Tester AI Testing Study Guide:

ISTQB CT-AI Syllabus (Section 9.3: Back-to-Back Testing)

'Back-to-back testing is used when an updated ML model needs to be compared against a previous version to confirm that it performs better or as expected'.

'The results of the newly trained model are compared with those of the prior version to ensure that changes did not negatively impact performance'.

Conclusion:

To verify that the model's performance improved after retraining, back-to-back testing is the most appropriate method as it compares both model versions. Hence, the correct answer is D.


Question #2

Which of the following is an example of a clustering problem that can be resolved by unsupervised learning?

Reveal Solution Hide Solution
Correct Answer: A

Clustering is a form of unsupervised learning, which groups data points based on similarities without predefined labels. According to ISTQB 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 the types of fins are labeled, it becomes a classification 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 for regression models, which are part of supervised learning.

Thus, Option A is the best answer, as clustering is used to group shoppers based on tendencies without predefined labels.

Certified Tester AI Testing Study Guide Reference:

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).


Question #3

Pairwise testing can be used in the context of self-driving cars for controlling an explosion in the number of combinations of parameters.

Which ONE of the following options is LEAST likely to be a reason for this incredible growth of parameters?

SELECT ONE OPTION

Reveal Solution Hide Solution
Correct Answer: C

Pairwise testing is used to handle the large number of combinations of parameters that can arise in complex systems like self-driving cars. The question asks which of the given options is least likely to be a reason for the explosion in the number of parameters.

Different Road Types (A): Self-driving cars must operate on various road types, such as highways, city streets, rural roads, etc. Each road type can have different characteristics, requiring the car's system to adapt and handle different scenarios. Thus, this is a significant factor contributing to the growth of parameters.

Different Weather Conditions (B): Weather conditions such as rain, snow, fog, and bright sunlight significantly affect the performance of self-driving cars. The car's sensors and algorithms must adapt to these varying conditions, which adds to the number of parameters that need to be considered.

ML Model Metrics to Evaluate Functional Performance (C): While evaluating machine learning (ML) model performance is crucial, it does not directly contribute to the explosion of parameter combinations in the same way that road types, weather conditions, and car features do. Metrics are used to measure and assess performance but are not themselves variable conditions that the system must handle.

Different Features like ADAS, Lane Change Assistance, etc. (D): Advanced Driver Assistance Systems (ADAS) and other features add complexity to self-driving cars. Each feature can have multiple settings and operational modes, contributing to the overall number of parameters.

Hence, the least likely reason for the incredible growth in the number of parameters is C. ML model metrics to evaluate the functional performance.


ISTQB CT-AI Syllabus Section 9.2 on Pairwise Testing discusses the application of this technique to manage the combinations of different variables in AI-based systems, including those used in self-driving cars.

Sample Exam Questions document, Question #29 provides context for the explosion in parameter combinations in self-driving cars and highlights the use of pairwise testing as a method to manage this complexity.

Question #4

Written requirements are given in text documents, which ONE of the following options is the BEST way to generate test cases from these requirements?

SELECT ONE OPTION

Reveal Solution Hide Solution
Correct Answer: A

When written requirements are given in text documents, the best way to generate test cases is by using Natural Language Processing (NLP). Here's why:

Natural Language Processing (NLP): NLP can analyze and understand human language. It can be used to process textual requirements to extract relevant information and generate test cases. This method is efficient in handling large volumes of textual data and identifying key elements necessary for testing.

Why Not Other Options:

Analyzing source code for generating test cases: This is more suitable for white-box testing where the code is available, but it doesn't apply to text-based requirements.

Machine learning on logs of execution: This approach is used for dynamic analysis based on system behavior during execution rather than static textual requirements.

GUI analysis by computer vision: This is used for testing graphical user interfaces and is not applicable to text-based requirements.


Question #5

Pairwise testing can be used in the context of self-driving cars for controlling an explosion in the number of combinations of parameters.

Which ONE of the following options is LEAST likely to be a reason for this incredible growth of parameters?

SELECT ONE OPTION

Reveal Solution Hide Solution
Correct Answer: C

Pairwise testing is used to handle the large number of combinations of parameters that can arise in complex systems like self-driving cars. The question asks which of the given options is least likely to be a reason for the explosion in the number of parameters.

Different Road Types (A): Self-driving cars must operate on various road types, such as highways, city streets, rural roads, etc. Each road type can have different characteristics, requiring the car's system to adapt and handle different scenarios. Thus, this is a significant factor contributing to the growth of parameters.

Different Weather Conditions (B): Weather conditions such as rain, snow, fog, and bright sunlight significantly affect the performance of self-driving cars. The car's sensors and algorithms must adapt to these varying conditions, which adds to the number of parameters that need to be considered.

ML Model Metrics to Evaluate Functional Performance (C): While evaluating machine learning (ML) model performance is crucial, it does not directly contribute to the explosion of parameter combinations in the same way that road types, weather conditions, and car features do. Metrics are used to measure and assess performance but are not themselves variable conditions that the system must handle.

Different Features like ADAS, Lane Change Assistance, etc. (D): Advanced Driver Assistance Systems (ADAS) and other features add complexity to self-driving cars. Each feature can have multiple settings and operational modes, contributing to the overall number of parameters.

Hence, the least likely reason for the incredible growth in the number of parameters is C. ML model metrics to evaluate the functional performance.


ISTQB CT-AI Syllabus Section 9.2 on Pairwise Testing discusses the application of this technique to manage the combinations of different variables in AI-based systems, including those used in self-driving cars.

Sample Exam Questions document, Question #29 provides context for the explosion in parameter combinations in self-driving cars and highlights the use of pairwise testing as a method to manage this complexity.


Unlock Premium CT-AI Exam Questions with Advanced Practice Test Features:
  • Select Question Types you want
  • Set your Desired Pass Percentage
  • Allocate Time (Hours : Minutes)
  • Create Multiple Practice tests with Limited Questions
  • Customer Support
Get Full Access Now

Save Cancel