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Exam Overview
Exam Code CT-AI
Exam Name Certified Tester AI Testing
Vendor iSQI
Total Questions 120
Last Updated 26-Oct, 2026
Free CT-AI Questions Get Premium Access
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Free CT-AI Practice Questions

Explore 5 free practice questions for the iSQI CT-AI exam and review the answers and explanations to support your preparation.

  1. Question 1

    Single answer

    Which of the following is a technique used in machine learning?

    • Option A:

      Decision trees

      Correct
    • Option B:

      Equivalence partitioning

      Correct
    • Option C:

      Boundary value analysis

      Correct
    • Option D:

      Decision tables

      Correct
    Show Answer & Explanation Hide Answer & Explanation

    Correct Answer: A

    Explanation

    Decision trees are a foundational algorithm used in supervised machine learning. The syllabus describes:

    "A decision tree is a tree-like ML model whose nodes represent decisions and whose branches represent possible outcomes."

    (Reference: ISTQB CT-AI Syllabus v1.0, Section 3.4)

  2. Question 2

    Single answer

    Which statement regarding data preparation in the ML workflow is correct?

    Choose ONE option (1 out of 4)

    • Option A:

      A key challenge in data transformation is the removal or correction of erroneous data.

      Correct
    • Option B:

      Since data preparation is time-consuming, all steps should be automated.

      Correct
    • Option C:

      One challenge of data gathering is obtaining high-quality data from multiple sources.


      Correct
    • Option D:

      Sampling is so well researched that it is no longer considered risky.

      Correct
    Show Answer & Explanation Hide Answer & Explanation

    Correct Answer: C

    Explanation

    The ISTQB CT-AI syllabus describes the ML data preparation workflow in Section 2.2 – Data Preparation . Data preparation consists of data gathering , cleaning , transformation , and sampling . The syllabus emphasizes that one significant challenge during data gathering is combining data from multiple heterogeneous sources , which often differ in structure, quality, and format. Ensuring the resulting dataset is accurate, complete, and representative can be complex, making this a critical challenge in the ML workflow. This aligns directly with Option C .


    Option A is incorrect because erroneous data correction is part of cleaning , not transformation. Option B contradicts the syllabus: while automation can help, not all steps should be automated due to the need for expert oversight, especially in detecting subtle data quality issues. Option D is incorrect because sampling continues to involve risk—particularly around representativeness—and the syllabus emphasizes caution, not complacency.


    Thus, Option C is the only statement that accurately reflects the syllabus.

  3. Question 3

    Single answer

    Which ONE of the following options is an example that BEST describes a system with Al-based autonomous functions?

    SELECT ONE OPTION

    • Option A:

      A system that utilizes human beings for all important decisions.

      Correct
    • Option B:

      A fully automated manufacturing plant that uses no software.



      Correct
    • Option C:

      A system that utilizes a tool like Selenium.

      Correct
    • Option D:

      A system that is fully able to respond to its environment.

      Correct
    Show Answer & Explanation Hide Answer & Explanation

    Correct Answer: D

    Explanation

    AI-Based Autonomous Functions: An AI-based autonomous system is one that can respond to its environment without human intervention.

    The other options either involve human decisions or do not use AI at all.

    Reference: ISTQB_CT-AI_Syllabus_v1.0, Sections on Autonomy and Testing Autonomous AI-Based Systems​​​​.

  4. Question 4

    Single answer

    Arihant Meditation is a startup using Al to aid people in deeper and better meditation based on analysis of various factors such as time and duration of the meditation, pulse and blood pressure, EEG patters etc. among others. Their model accuracy and other functional performance parameters have not yet reached their desired level.


    Which ONE of the following factors is NOT a factor affecting the ML functional performance?


    SELECT ONE OPTION

    • Option A:

      The data pipeline

      Correct
    • Option B:

      The quality of the labeling

      Correct
    • Option C:

      Biased data

      Correct
    • Option D:

      The number of classes

      Correct
    Show Answer & Explanation Hide Answer & Explanation

    Correct Answer: D

    Explanation

     Factors Affecting ML Functional Performance: The data pipeline, quality of the labeling, and biased data are all factors that significantly affect the performance of machine learning models. The number of classes, while relevant for the model structure, is not a direct factor affecting the performance metrics such as accuracy or bias.


     Reference: ISTQB_CT-AI_Syllabus_v1.0, Sections on Data Quality and its Effect on the ML Model and ML Functional Performance Metrics​​​​.

  5. Question 5

    Single answer

    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?

    • Option A:

      EDA can help detect the outlier emails from the real emails

      Correct
    • Option B:

      EDA can detect and remove the false emails

      Correct
    • Option C:

      EDA can restrict how many inputs can be provided by unique users

      Correct
    • Option D:

      EDA cannot be used to detect the attack

      Correct
    Show Answer & Explanation Hide Answer & Explanation

    Correct 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."


    (Reference: ISTQB CT-AI Syllabus v1.0, Section 9.1.2, page 67 of 99)