Practice CT-AI Questions & CT-AI Exam Topics Pdf

Wiki Article

DOWNLOAD the newest Dumps4PDF CT-AI PDF dumps from Cloud Storage for free: https://drive.google.com/open?id=1Ue-YauEhKpxq1-bLdKUu7llL7bWNJLdu

Our website just believe in offering cost-efficient and time-saving CT-AI exam braindumps to our customers that help them get high passing score easier. Our valid CT-AI test questions can be instantly downloaded and easy to understand with our 100% correct exam answers. One-year free update right will enable you get the latest CT-AI VCE Dumps anytime and you just need to check your mailbox.

ISTQB CT-AI Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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
  • 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 4
  • 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 5
  • 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 6
  • Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 7
  • systems from those required for conventional systems.
Topic 8
  • 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 9
  • Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based

>> Practice CT-AI Questions <<

CT-AI Exam Topics Pdf | Latest CT-AI Braindumps Sheet

The CT-AI study guide to good meet user demand, will be a little bit of knowledge to separate memory, every day we have lots of fragments of time, such as waiting in line to take when you eat, or time in buses commute on the way by subway every day, but when you add them together will be surprised to find a day we can make use of the time is so much debris. We have three version of our CT-AI Exam Questions which can let you study at every condition so that you can make full use of your time. And you will get the CT-AI certification for sure.

ISTQB Certified Tester AI Testing Exam Sample Questions (Q73-Q78):

NEW QUESTION # 73
Which of the following is a technique used in machine learning?

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."


NEW QUESTION # 74
A bank wants to use an algorithm to determine which applicants should be given a loan. The bank hires a data scientist to construct a logistic regression model to predict whether the applicant will repay the loan or not.
The bank has enough data on past customers to randomly split the data into a training dataset and a test
/validation dataset. A logistic regression model is constructed on the training dataset using the following independent variables:
* Gender
* Marital status
* Number of dependents
* Education
* Income
* Loan amount
* Loan term
* Credit score
The model reveals that those with higher credit scores and larger total incomes are more likely to repay their loans. The data scientist has suggested that there might be bias present in the model based on previous models created for other banks.
Given this information, what is the best test approach to check for potential bias in the model?

Answer: C

Explanation:
The syllabus mentions that experience-based testing and EDA are effective for detecting biases:
"Experience-based testing can be used to verify that the training dataset is operationally relevant and identify potential sources of bias. EDA is also useful for exploring the data and understanding any relationships that might lead to bias in the model." (Reference: ISTQB CT-AI Syllabus v1.0, Section 8.3, page 58 of 99)


NEW QUESTION # 75
"Splendid Healthcare" has started developing a cancer detection system based on ML. The type of cancer they plan on detecting has 2% prevalence rate in the population of a particular geography. It is required that the model performs well for both normal and cancer patients.
Which ONE of the following combinations requires MAXIMIZATION?
SELECT ONE OPTION

Answer: A

Explanation:
Prevalence Rate and Model Performance:
The cancer detection system being developed by "Splendid Healthcare" needs to account for the fact that the type of cancer has a 2% prevalence rate in the population. This indicates that the dataset is highly imbalanced with far fewer positive (cancer) cases compared to negative (normal) cases.
Importance of Recall:
Recall, also known as sensitivity or true positive rate, measures the proportion of actual positive cases that are correctly identified by the model. In medical diagnosis, especially cancer detection, recall is critical because missing a positive case (false negative) could have severe consequences for the patient. Therefore, maximizing recall ensures that most, if not all, cancer cases are detected.
Importance of Precision:
Precision measures the proportion of predicted positive cases that are actually positive. High precision reduces the number of false positives, meaning fewer people will be incorrectly diagnosed with cancer. This is also important to avoid unnecessary anxiety and further invasive testing for those who do not have the disease.
Balancing Recall and Precision:
In scenarios where both false negatives and false positives have significant consequences, it is crucial to balance recall and precision. This balance ensures that the model is not only good at detecting positive cases but also accurate in its predictions, reducing both types of errors.
Accuracy and Specificity:
While accuracy (the proportion of total correct predictions) is important, it can be misleading in imbalanced datasets. In this case, high accuracy could simply result from the model predicting the majority class (normal) correctly. Specificity (true negative rate) is also important, but for a cancer detection system, recall and precision take precedence to ensure positive cases are correctly and accurately identified.
Conclusion:
Therefore, for a cancer detection system with a low prevalence rate, maximizing both recall and precision is crucial to ensure effective and accurate detection of cancer cases.


NEW QUESTION # 76
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: D

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 # 77
A beer company is trying to understand how much recognition its logo has in the market. It plans to do that by monitoring images on various social media platforms using a pre-trained neural network for logo detection. This particular model has been trained by looking for words, as well as matching colors on social media images. The company logo has a big word across the middle with a bold blue and magenta border. Which associated risk is most likely to occur when using this pre-trained model?

Answer: B

Explanation:
According to the syllabus, pre-trained models often inherit biases and limitations from the data and processes used in their original training, which may not align with the new use case.
Specifically, the syllabus states:
"When using a pre-trained model, the training data and process cannot be fully controlled or known by the user of the model. As a result, the model can inherit biases or inaccuracies that were part of its original development and training process."


NEW QUESTION # 78
......

Whether you want to improve your skills, expertise or career growth, with Dumps4PDF's CT-AI training and CT-AI certification resources help you achieve your goals. Our exams files feature hands-on tasks and real-world scenarios; in just a matter of days, you'll be more productive and embracing new technology standards. Our online resources and events enable you to focus on learning just what you want on your timeframe. You get access to every exams files and there continuously update our study materials; these exam updates are supplied free of charge to our valued customers. Get the best CT-AI Exam Training; as you study from our exam-files.

CT-AI Exam Topics Pdf: https://www.dumps4pdf.com/CT-AI-valid-braindumps.html

P.S. Free & New CT-AI dumps are available on Google Drive shared by Dumps4PDF: https://drive.google.com/open?id=1Ue-YauEhKpxq1-bLdKUu7llL7bWNJLdu

Report this wiki page