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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
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	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 3	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 4	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 5	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 6	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.

>> CT-AI Frequent Updates <<

CT-AI - Newest Certified Tester AI Testing Exam Frequent Updates

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q68-Q73):

NEW QUESTION # 68
Which statement regarding the use of training, validation, and test data sets is correct?

A. If limited data is available, it may be better to work without a separate test data set.
B. The data in the test data set must be equivalent to the data in the training data sets and to the data in the validation data sets.
C. Optimally, the data should be distributed equally between the training, validation, and test data sets.
D. If only limited data is available, validation and test data sets can be combined in multiple ways during training.

Answer: B

Explanation:
The ISTQB CT-AI syllabus (Section3.2 - Model Evaluation) specifies the correct usage oftraining, validation, andtestdatasets. It emphasizes that the test dataset must be representative of the real operational dataand must beequivalent in distribution to the training and validation sets, ensuring a fair and unbiased evaluation. Option D precisely matches this requirement.

NEW QUESTION # 69
The activation value output for a neuron in a neural network is obtained by applying computation to the neuron.
Which ONE of the following options BEST describes the inputs used to compute the activation value?

A. Individual bias at the neuron level, and weights assigned to the connections between the neurons.
B. Individual bias at the neuron level, activation values of neurons in the previous layer, and weights assigned to the connections between the neurons.
C. Individual bias at the neuron level, and activation values of neurons in the previous layer.
D. Activation values of neurons in the previous layer, and weights assigned to the connections between the neurons.

Answer: B

NEW QUESTION # 70
In a conference on artificial intelligence (Al), a speaker made the statement, "The current implementation of Al using models which do NOT change by themselves is NOT true Al*. Based on your understanding of Al, is this above statement CORRECT or INCORRECT and why?

A. This statement is correct. In general, what is considered Al today may change over time.
B. This statement is correct. In general, today the term Al is utilized incorrectly.
C. This statement is incorrect. What is considered Al today will continue to be Al even as technology evolves and changes.
D. This statement is incorrect. Current Al is true Al and there is no reason to believe that this fact will change over time.

Answer: A

Explanation:
The term AI is dynamic and has evolved over the years. What is considered AI today might be viewed as standard computing in the future. Historically, as technologies become mainstream, they often cease to be considered "AI".

NEW QUESTION # 71
Which ONE of the following tests is LEAST likely to be performed during the ML model testing phase?
SELECT ONE OPTION

A. Testing the speed of the prediction by the model.
B. Testing the accuracy of the classification model.
C. Testing the speed of the training of the model.
D. Testing the API of the service powered by the ML model.

Answer: C

Explanation:
The question asks which test is least likely to be performed during the ML model testing phase. Let's consider each option:
* Testing the accuracy of the classification model (A): Accuracy testing is a fundamental part of the ML model testing phase. It ensures that the model correctly classifies the data as intended and meets the required performance metrics.
* Testing the API of the service powered by the ML model (B): Testing the API is crucial, especially if the ML model is deployed as part of a service. This ensures that the service integrates well with other systems and that the API performs as expected.
* Testing the speed of the training of the model (C): This is least likely to be part of the ML model testing phase. The speed of training is more relevant during the development phase when optimizing and tuning the model. During testing, the focus is more on the model's performance and behavior rather than how quickly it was trained.
* Testing the speed of the prediction by the model (D): Testing the speed of prediction is important to ensure that the model meets performance requirements in a production environment, especially for real- time applications.
References:
* ISTQB CT-AI Syllabus Section 3.2 on ML Workflow and Section 5 on ML Functional Performance Metrics discuss the focus of testing during the model testing phase, which includes accuracy and prediction speed but not the training speed.

NEW QUESTION # 72
Upon testing a model used to detect rotten tomatoes, the following data was observed by the test engineer, based on certain number of tomato images.

For this confusion matrix which combinations of values of accuracy, recall, and specificity respectively is CORRECT?
SELECT ONE OPTION

A. 0.87.0.9. 0.84
B. 1,0.9, 0.8
C. 1,0.87,0.84
D. 0.84.1,0.9

Answer: A

Explanation:
To calculate the accuracy, recall, and specificity from the confusion matrix provided, we use the following formulas:
Confusion Matrix:
Actually Rotten: 45 (True Positive), 8 (False Positive)
Actually Fresh: 5 (False Negative), 42 (True Negative)
Accuracy:
Accuracy is the proportion of true results (both true positives and true negatives) in the total population.
Formula: Accuracy=TP+TNTP+TN+FP+FN\text{Accuracy} = \frac{TP + TN}{TP + TN + FP + FN}Accuracy=TP+TN+FP+FNTP+TN Calculation: Accuracy=45+4245+42+8+5=87100=0.87\text{Accuracy} = \frac{45 + 42}{45 + 42 + 8 + 5} = \frac{87}{100} = 0.87Accuracy=45+42+8+545+42=10087=0.87 Recall (Sensitivity):
Recall is the proportion of true positive results in the total actual positives.
Formula: Recall=TPTP+FN\text{Recall} = \frac{TP}{TP + FN}Recall=TP+FNTP Calculation: Recall=4545+5=4550=0.9\text{Recall} = \frac{45}{45 + 5} = \frac{45}{50} = 0.9Recall=45+545=5045=0.9 Specificity:
Specificity is the proportion of true negative results in the total actual negatives.
Formula: Specificity=TNTN+FP\text{Specificity} = \frac{TN}{TN + FP}Specificity=TN+FPTN Calculation: Specificity=4242+8=4250=0.84\text{Specificity} = \frac{42}{42 + 8} = \frac{42}{50} = 0.84Specificity=42+842=5042=0.84 Therefore, the correct combinations of accuracy, recall, and specificity are 0.87, 0.9, and 0.84 respectively.
Reference:
ISTQB CT-AI Syllabus, Section 5.1, Confusion Matrix, provides detailed formulas and explanations for calculating various metrics including accuracy, recall, and specificity.
"ML Functional Performance Metrics" (ISTQB CT-AI Syllabus, Section 5).

NEW QUESTION # 73
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