CT-AI: Certified Tester AI Testing Exam Dumps & PassGuide CT-AI Examen

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ISTQB CT-AI Exam Overview:

Certification Vendor:	ISTQB
Exam Name:	ISTQB Certified Tester AI Testing (CT-AI) Exam
Exam Number:	CT-AI
Related Certifications:	ISTQB Certified Tester Foundation Level (CTFL)
Certificate Validity Period:	Lifetime
Available Languages:	English
Passing Score:	65%
Exam Format:	Multiple Choice
Exam Duration:	60 minutes
Real Exam Qty:	40
Recommended Training:	ISTQB Accredited Training Providers
Exam Registration:	ISTQB Official Website
Sample Questions:	ISTQB CT-AI Sample Questions
Exam Way:	Online and onsite proctored exam via accredited ISTQB examination providers
Pre Condition:	Recommended prior knowledge of ISTQB Foundation Level (CTFL) and basic understanding of software testing concepts and machine learning fundamentals.
Official Syllabus URL:	https://www.istqb.org

>> CT-AI Antworten <<

CT-AI Studienmaterialien: Certified Tester AI Testing Exam & CT-AI Zertifizierungstraining

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ISTQB CT-AI Prüfungsplan:

Thema	Einzelheiten
Thema 1	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.
Thema 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.
Thema 3	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Thema 4	systems from those required for conventional systems.
Thema 5	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.
Thema 6	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.
Thema 7	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.
Thema 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.
Thema 9	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.
Thema 10	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.
Thema 11	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based

ISTQB Certified Tester AI Testing Exam CT-AI Prüfungsfragen mit Lösungen (Q16-Q21):

16. Frage
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. Data testing
B. Deploying the model
C. Evaluating the model
D. Tuning the model

Antwort: D

Begründung:
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 isC.
Tuning the model.
:
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.

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

Antwort: C

Begründung:
Option A correctly identifies all components involved in computing the activation value: the individual bias, the activation values of the previous layer, and the weights of the connections.

18. Frage
Which ONE of the below statements BEST describes why test environments for autonomous systems might need to be different to other test environments?

A. Tools may be required to provide explanations of system behavior
B. AI-specific hardware may be required
C. Tools may be required to simulate extreme scenarios
D. Non-determinism may need to be introduced into the environment

Antwort: C

Begründung:
Tools may be required to simulate extreme scenarios because autonomous systems often need to be tested under challenging or rare conditions that are difficult to replicate in a real-world environment. These scenarios, such as extreme weather, system failures, or unexpected behaviors, need to be simulated in a controlled environment to ensure the system can handle a wide range of situations safely and effectively. This makes the test environment for autonomous systems unique compared to other types of testing.

19. Frage
Consider a natural language processing (NLP) algorithm that attempts to predict the next word that you would like to type in a text message. An update to the algorithm has been created that should increase the accuracy of the predictions based on user typing patterns. The old algorithm was rated for accuracy by the users. Then, after the new update was released, the users rated the updated algorithm. A statistical test was used to compare between the two versions of the algorithm to see whether or not the update should remain in place. This is an example of what type of testing?

A. Exploratory testing
B. A/B testing
C. Metamorphic testing
D. Pairwise testing

Antwort: B

Begründung:
The syllabus states:
"A/B testing can be used to test updates to an AI-based system where there are agreed acceptance criteria, such as ML functional performance metrics, as described in Chapter 5. A/B testing is used to compare the updated variant with the previous variant."

20. Frage
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. F1 -source
C. Accuracy
D. Precision

Antwort: C

Begründung:
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)

21. Frage
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