LATEST CT-AI TEST COST & CT-AI EXAM REFERENCE

Latest CT-AI Test Cost & CT-AI Exam Reference

Latest CT-AI Test Cost & CT-AI Exam Reference

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Tags: Latest CT-AI Test Cost, CT-AI Exam Reference, New CT-AI Exam Answers, CT-AI Test Voucher, CT-AI Latest Exam Tips

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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
  • systems from those required for conventional systems.
Topic 3
  • 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 4
  • 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 5
  • 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 6
  • 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 7
  • Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based

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CT-AI Exam Reference | New CT-AI Exam Answers

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

NEW QUESTION # 73
Which of the following are the three activities in the data acquisition activities for data preparation?

  • A. Identifying, gathering, labelling
  • B. Cleaning, transforming, augmenting
  • C. Feature selecting, feature growing, feature augmenting
  • D. Building, approving, deploying

Answer: A

Explanation:
According to the ISTQB Certified Tester AI Testing (CT-AI) syllabus, data acquisition, a critical step in data preparation for machine learning (ML) workflows, consists of three key activities:
* Identification:This step involves determining the types of data required for training and prediction. For example, in a self-driving car application, data types such as radar, video, laser imaging, and LiDAR (Light Detection and Ranging) data may be identified as necessary sources.
* Gathering:After identifying the required data types, the sources from which the data will be collected are determined, along with the appropriate collection methods. An example could be gathering financial data from the International Monetary Fund (IMF) and integrating it into an AI-based system.
* Labeling:This process involves annotating or tagging the collected data to make it meaningful for supervised learning models. Labeling is an essential activity that helps machine learning algorithms differentiate between categories and make accurate predictions.
These activities ensure that the data is suitable for training and testing machine learning models, forming the foundation of data preparation.


NEW QUESTION # 74
A software component uses machine learning to recognize the digits from a scan of handwritten numbers. In the scenario above, which type of Machine Learning (ML) is this an example of?
SELECT ONE OPTION

  • A. Reinforcement learning
  • B. Clustering
  • C. Classification
  • D. Regression

Answer: C

Explanation:
Recognizing digits from a scan of handwritten numbers using machine learning is an example of classification. Here's a breakdown:
Classification: This type of machine learning involves categorizing input data into predefined classes. In this scenario, the input data (handwritten digits) are classified into one of the 10 digit classes (0-9).
Why Not Other Options:
Reinforcement Learning: This involves learning by interacting with an environment to achieve a goal, which does not fit the problem of recognizing digits.
Regression: This is used for predicting continuous values, not discrete categories like digit recognition.
Clustering: This involves grouping similar data points together without predefined classes, which is not the case here.


NEW QUESTION # 75
A system was developed for screening the X-rays of patients for potential malignancy detection (skin cancer). A workflow system has been developed to screen multiple cancers by using several individually trained ML models chained together in the workflow.
Testing the pipeline could involve multiple kind of tests (I - III):
I . Pairwise testing of combinations
II . Testing each individual model for accuracy
III . A/B testing of different sequences of models
Which ONE of the following options contains the kinds of tests that would be MOST APPROPRIATE to include in the strategy for optimal detection?
SELECT ONE OPTION

  • A. Only III
  • B. Only II
  • C. I and II
  • D. I and III

Answer: C

Explanation:
The question asks which combination of tests would be most appropriate to include in the strategy for optimal detection in a workflow system using multiple ML models.
Pairwise testing of combinations (I): This method is useful for testing interactions between different components in the workflow to ensure they work well together, identifying potential issues in the integration.
Testing each individual model for accuracy (II): Ensuring that each model in the workflow performs accurately on its own is crucial before integrating them into a combined workflow.
A/B testing of different sequences of models (III): This involves comparing different sequences to determine which configuration yields the best results. While useful, it might not be as fundamental as pairwise and individual accuracy testing in the initial stages.
Reference:
ISTQB CT-AI Syllabus Section 9.2 on Pairwise Testing and Section 9.3 on Testing ML Models emphasize the importance of testing interactions and individual model accuracy in complex ML workflows.


NEW QUESTION # 76
Consider a machine learning model where the model is attempting to predict if a patient is at risk for stroke.
The model collects information on each patient regarding their blood pressure, red blood cell count, smoking, status, history of heart disease, cholesterol level, and demographics. Then, using a decision tree the model predicts whether or not the associated patient is likely to have a stroke in the near future. One the model is created using a training data set, it is used to predict a stroke in 80 additional patients. The table below shows a confusion matrix on whether or not the model mode a correct or incorrect prediction.
The testers have calculated what they believe to be an appropriate functional performance metric for the model. They calculated a value of 2/3 or 0.6667.

  • A. Recall
  • B. Precision
  • C. F1 -source
  • D. Accuracy

Answer: D

Explanation:
The problem describes aclassification modelthat predicts whether a patient is at risk for a stroke. The confusion matrix is provided, and the testers have calculated a performance metric as2/3 or 0.6667.
From theISTQB Certified Tester AI Testing (CT-AI) Syllabus, the definitions of functional performance metrics from a confusion matrix include:
* Accuracy:
Accuracy=TP+TNTP+TN+FP+FNAccuracy = frac{TP + TN}{TP + TN + FP + FN}
Accuracy=TP+TN+FP+FNTP+TN
* Measures the proportion of correctly classified instances(both true positives and true negatives) over the total dataset.
* If the value is0.6667, it suggests that the metric includesboth correct positive and negative classifications, aligning with accuracy.
* Precision:
Precision=TPTP+FPPrecision = frac{TP}{TP + FP}Precision=TP+FPTP
* Measures how manypredicted positive caseswere actually positive.
* Doesnotmatch the given calculation.
* Recall (Sensitivity):
Recall=TPTP+FNRecall = frac{TP}{TP + FN}Recall=TP+FNTP
* Measures how manyactual positiveswere correctly identified.
* Doesnotmatch the 0.6667 value.
* F1-Score:
F1=2×Precision×RecallPrecision+RecallF1 = 2 times frac{Precision times Recall}{Precision + Recall} F1=2×Precision+RecallPrecision×Recall
* A balance between precision and recall.
* The formula isdifferent from the provided calculation.
Since the formula foraccuracymatches the calculated value of0.6667, the best answer isD. Accuracy.
Certified Tester AI Testing Study Guide References:
* ISTQB CT-AI Syllabus v1.0, Section 5.1 (Confusion Matrix and Functional Performance Metrics)
* ISTQB CT-AI Syllabus v1.0, Section 5.4 (Selecting ML Functional Performance Metrics)


NEW QUESTION # 77
Which ONE of the following models BEST describes a way to model defect prediction by looking at the history of bugs in modules by using code quality metrics of modules of historical versions as input?
SELECT ONE OPTION

  • A. Search of similar code based on natural language processing.
  • B. Using a classification model to predict the presence of a defect by using code quality metrics as the input data.
  • C. Identifying the relationship between developers and the modules developed by them.
  • D. Clustering of similar code modules to predict based on similarity.

Answer: B

Explanation:
Defect prediction models aim to identify parts of the software that are likely to contain defects by analyzing historical data and code quality metrics. The primary goal is to use this predictive information to allocate testing and maintenance resources effectively. Let's break down why option D is the correct choice:
Understanding Classification Models:
Classification models are a type of supervised learning algorithm used to categorize or classify data into predefined classes or labels. In the context of defect prediction, the classification model would classify parts of the code as either "defective" or "non-defective" based on the input features.
Input Data - Code Quality Metrics:
The input data for these classification models typically includes various code quality metrics such as cyclomatic complexity, lines of code, number of methods, depth of inheritance, coupling between objects, etc. These metrics help the model learn patterns associated with defects.
Historical Data:
Historical versions of the code along with their defect records provide the labeled data needed for training the classification model. By analyzing this historical data, the model can learn which metrics are indicative of defects.
Why Option D is Correct:
Option D specifies using a classification model to predict the presence of defects by using code quality metrics as input data. This accurately describes the process of defect prediction using historical bug data and quality metrics.
Eliminating Other Options:
A . Identifying the relationship between developers and the modules developed by them: This does not directly involve predicting defects based on code quality metrics and historical data.
B . Search of similar code based on natural language processing: While useful for other purposes, this method does not describe defect prediction using classification models and code metrics.
C . Clustering of similar code modules to predict based on similarity: Clustering is an unsupervised learning technique and does not directly align with the supervised learning approach typically used in defect prediction models.
Reference:
ISTQB CT-AI Syllabus, Section 9.5, Metamorphic Testing (MT), describes various testing techniques including classification models for defect prediction.
"Using AI for Defect Prediction" (ISTQB CT-AI Syllabus, Section 11.5.1).


NEW QUESTION # 78
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