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ISTQB CT-AI Exam Syllabus Topics:

TopicDetails
Topic 1
  • systems from those required for conventional systems.
Topic 2
  • 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 3
  • Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 4
  • 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 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
  • 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 7
  • 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 8
  • 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.

ISTQB Certified Tester AI Testing Exam Sample Questions (Q111-Q116):

NEW QUESTION # 111
A company producing consumable goods wants to identify groups of people with similar tastes for the purpose of targeting different products for each group. You have to choose and apply an appropriate ML type for this problem.
Which ONE of the following options represents the BEST possible solution for this above-mentioned task?
SELECT ONE OPTION

Answer: B

Explanation:
A . Regression
Regression is used to predict a continuous value and is not suitable for grouping people based on similar tastes.
B . Association
Association is used to find relationships between variables in large datasets, often in the form of rules (e.g., market basket analysis). It does not directly group individuals but identifies patterns of co-occurrence.
C . Clustering
Clustering is an unsupervised learning method used to group similar data points based on their features. It is ideal for identifying groups of people with similar tastes without prior knowledge of the group labels. This technique will help the company segment its customer base effectively.
D . Classification
Classification is a supervised learning method used to categorize data points into predefined classes. It requires labeled data for training, which is not the case here as we want to identify groups without predefined labels.
Therefore, the correct answer is C because clustering is the most suitable method for grouping people with similar tastes for targeted product marketing.


NEW QUESTION # 112
Which ONE of the following combinations of Training, Validation, Testing data is used during the process of learning/creating the model?

Answer: D

Explanation:
The process of developing a machine learning model typically involves the use of three types of datasets:
Training Data:This is used to train the model, i.e., to learn the patterns and relationships in the data.
Validation Data:This is used to tune the model's hyperparameters and to prevent overfitting during the training process.
Test Data:This is used to evaluate the final model's performance and to estimate how it will perform on unseen data.
Let's analyze each option:
A). Training data - validation data - test data
This option correctly includes all three types of datasets used in the process of creating and validating a model. The training data is used for learning, validation data for tuning, and test data for final evaluation.
B). Training data - validation data
This option misses the test data, which is crucial for evaluating the model's performance on unseen data after the training and validation phases.
C). Training data - test data
This option misses the validation data, which is important for tuning the model and preventing overfitting during training.
D). Validation data - test data
This option misses the training data, which is essential for the initial learning phase of the model.
Therefore, the correct answer is A because it includes all necessary datasets used during the process of learning and creating the model: training, validation, and test data.


NEW QUESTION # 113
Data used for an object detection ML system was found to have been labelled incorrectly in many cases.
Which ONE of the following options is most likely the reason for this problem?

Answer: C

Explanation:
Accuracy Issues: The primary goal of labeling data in machine learning is to ensure that the model can accurately learn and make predictions based on the given labels. Incorrectly labeled data directly impacts the model's accuracy, leading to poor performance because the model learns incorrect patterns.


NEW QUESTION # 114
You are using a neural network to train a robot vacuum to navigate without bumping into objects.
You set up a reward scheme that encourages speed but discourages hitting the bumper sensors.
Instead of what you expected, the vacuum has now learned to drive backwards because there are no bumpers on the back. This is an example of what type of behavior?

Answer: D

Explanation:
The syllabus defines reward hacking as:
"Reward hacking can result from an AI-based system achieving a specified goal by using a
'clever' or 'easy' solution that perverts the spirit of the designer's intent." In this case, the vacuum found a loophole in the reward function--driving backwards to avoid bumper triggers while maximizing reward for speed.


NEW QUESTION # 115
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?

Answer: C

Explanation:
The syllabus defines back-to-back testing as a method to compare a modified AI system to the previous version, which is ideal in this scenario:
"Back-to-back testing is performed by comparing the outputs of two systems that are supposed to provide the same outputs, one being a known and trusted system and the other being the test system. This approach can be used to test ML systems after re-training to verify that improvements have not introduced regressions."


NEW QUESTION # 116
......

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