CT-AI RELIABLE EXAM PRICE, RELIABLE CT-AI EXAM SIMULATIONS

CT-AI Reliable Exam Price, Reliable CT-AI Exam Simulations

CT-AI Reliable Exam Price, Reliable CT-AI Exam Simulations

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Tags: CT-AI Reliable Exam Price, Reliable CT-AI Exam Simulations, Reliable CT-AI Test Questions, New CT-AI Test Vce, Test Certification CT-AI Cost

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Reliable CT-AI Exam Simulations - Reliable CT-AI Test Questions

A good deal of researches has been made to figure out how to help different kinds of candidates to get CT-AI certification. We revise and update the CT-AI test torrent according to the changes of the syllabus and the latest developments in theory and practice. We base the CT-AI Certification Training on the test of recent years and the industry trends through rigorous analysis. Therefore, for your convenience, more choices are provided for you, we are pleased to suggest you to choose our CT-AI exam question for your exam.

ISTQB CT-AI Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 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
  • 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
  • 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 6
  • systems from those required for conventional systems.
Topic 7
  • 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.

ISTQB Certified Tester AI Testing Exam Sample Questions (Q64-Q69):

NEW QUESTION # 64
Which ONE of the following tests is MOST likely to describe a useful test to help detect different kinds of biases in ML pipeline?
SELECT ONE OPTION

  • A. Check the input test data for potential sample bias.
  • B. Testing the distribution shift in the training data for inappropriate bias.
  • C. Test the model during model evaluation for data bias.
  • D. Testing the data pipeline for any sources for algorithmic bias.

Answer: C

Explanation:
Detecting biases in the ML pipeline involves various tests to ensure fairness and accuracy throughout the ML process.
Testing the distribution shift in the training data for inappropriate bias (A): This involves checking if there is any shift in the data distribution that could lead to bias in the model. It is an important test but not the most direct method for detecting biases.
Test the model during model evaluation for data bias (B): This is a critical stage where the model is evaluated to detect any biases in the data it was trained on. It directly addresses potential data biases in the model.
Testing the data pipeline for any sources for algorithmic bias (C): This test is crucial as it helps identify biases that may originate from the data processing and transformation stages within the pipeline. Detecting sources of algorithmic bias ensures that the model does not inherit biases from these processes.
Check the input test data for potential sample bias (D): While this is an important step, it focuses more on the input data and less on the overall data pipeline.
Hence, the most likely useful test to help detect different kinds of biases in the ML pipeline is B. Test the model during model evaluation for data bias.
Reference:
ISTQB CT-AI Syllabus Section 8.3 on Testing for Algorithmic, Sample, and Inappropriate Bias discusses various tests that can be performed to detect biases at different stages of the ML pipeline.
Sample Exam Questions document, Question #32 highlights the importance of evaluating the model for biases.


NEW QUESTION # 65
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

  • A. Regression
  • B. Clustering
  • C. Classification
  • D. Association

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 # 66
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

  • A. GUI analysis by computer vision
  • B. Machine learning on logs of execution
  • C. Analyzing source code for generating test cases
  • D. Natural language processing on textual requirements

Answer: D

Explanation:
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.
References:This aligns with the methodology discussed in the syllabus under the section on using AI for generating test cases from textual requirements.


NEW QUESTION # 67
Which ONE of the following options is the MOST APPROPRIATE stage of the ML workflow to set model and algorithm hyperparameters?
SELECT ONE OPTION

  • A. Deploying the model
  • B. Tuning the model
  • C. Evaluating the model
  • D. Data testing

Answer: B

Explanation:
Setting model and algorithm hyperparameters is an essential step in the machine learning workflow, primarily occurring during the tuning phase.
Evaluating the model (A): This stage involves assessing the model's performance using metrics and does not typically include the setting of hyperparameters.
Deploying the model (B): Deployment is the stage where the model is put into production and used in real-world applications. Hyperparameters should already be set before this stage.
Tuning the model (C): This is the correct stage where hyperparameters are set. Tuning involves adjusting the hyperparameters to optimize the model's performance.
Data testing (D): Data testing involves ensuring the quality and integrity of the data used for training and testing the model. It does not include setting hyperparameters.
Hence, the most appropriate stage of the ML workflow to set model and algorithm hyperparameters is C. Tuning the model.
Reference:
ISTQB CT-AI Syllabus Section 3.2 on the ML Workflow outlines the different stages of the ML process, including the tuning phase where hyperparameters are set.
Sample Exam Questions document, Question #31 specifically addresses the stage in the ML workflow where hyperparameters are configured.


NEW QUESTION # 68
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 teamhas 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?

  • A. Metamorphic testing because the application domain is not clearly understood at this point.
  • B. Back-to-back testing using the version of the model before training and the new version of the model after being trained with additional images.
  • C. Pairwise testing using combinatorics to look at a long list of photo parameters.
  • D. Adversarial testing to verify that no incorrect images have been used in the training.

Answer: B

Explanation:
Back-to-back testing isused to compare two different versions of an ML model, which is precisely what is needed in this scenario.
* The model initiallymisclassified dogs as wolvesdue to feature similarities.
* Thetest team retrains the modelwith additional images of dogs and wolves.
* The best way to verify whether this additional trainingimproved classification accuracyis to compare theoriginal model's output with the newly trained model's output using the same test dataset.
* A (Metamorphic Testing):Metamorphic testing is useful forgenerating new test casesbased 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 againstmaliciously perturbed inputs, not to verify training effectiveness.
* C (Pairwise Testing):Pairwise testing is a combinatorial technique for reducing the number of test casesby focusing on key variable interactions, not for validating model improvements.
* 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".
Why Other Options Are Incorrect:Supporting References from ISTQB Certified Tester AI Testing Study Guide:Conclusion:To verify that the model's performance improved after retraining,back-to-back testing is the most appropriate methodas it compares both model versions. Hence, thecorrect answer is D.


NEW QUESTION # 69
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