CT-AI Schulungsangebot & CT-AI Fragen Beantworten

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Die echten und originalen Prüfungsfragen und Antworten zu CT-AI（Certified Tester AI Testing Exam）bei Fast2test wurden verfasst von unseren ISTQB-Experten mit den Informationen von CT-AI（Certified Tester AI Testing Exam）aus dem Testcenter wie PROMETRIC oder VUE.

ISTQB CT-AI Prüfungsplan:

ThemaEinzelheiten
Thema 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.
Thema 2
  • 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 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.
Thema 4
  • 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 5
  • 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 6
  • systems from those required for conventional systems.
Thema 7
  • 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 8
  • Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Thema 9
  • 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.

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ISTQB Certified Tester AI Testing Exam CT-AI Prüfungsfragen mit Lösungen (Q45-Q50):

45. Frage
An Al based website will recommend holiday rental accommodation for a user based on their previous bookings, and inputs about their requirements. Metamorphic testing is being used by a test engineer, who has built up a history of previous bookings. The first test case they use (T1) is as follows:

The outputs from the test ate the following accommodations:
* Villa Fianca
* Villa Palma
* Apartments Mirasol
* Villa Paris
* Apartments Miramar
Which ONE of the below options are the MOST likely outputs from the test:

Antwort: A

Begründung:
The correct answer is D . Metamorphic testing derives follow-up test cases from a source test case by applying a metamorphic relation, where a controlled change in input should produce a predictable relationship in the output. The syllabus states that a metamorphic relation describes how a change in test inputs is reflected in the expected results, and that follow-up expected results are relative to the source test case rather than absolute values.
Here, the source result set contains five recommendations. If the follow-up test tightens or partitions the pool requirement, the valid recommendations should be consistent subsets of the original recommendations. Option D gives a coherent partition: T2 returns the accommodations satisfying the pool-related requirement, while T3 returns the accommodations aligned with "pool not required." It does not introduce recommendations outside the original result set and it preserves the expected relation between changed preference criteria and the returned list. Options A and B are unnecessarily narrow, while C mixes the partition by including Villa Franca in both branches.
References/topics: CT-AI Syllabus Chapter 9, Section 9.5 "Metamorphic Testing"; Section 9.5.1 "Hands- On Exercise: Metamorphic Testing."
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46. Frage
Which of the following are the three activities in the data acquisition activities for data preparation?

Antwort: C

Begründung:
The syllabus defines data acquisition as consisting of three steps:
"Data acquisition: The activity of acquiring data relevant to the business problem to be solved by an ML model, typically involving the activities of identifying, gathering and labelling data."


47. 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?
SELECT ONE OPTION

Antwort: B

Begründung:
In a neural network, the activation value of a neuron is determined by a combination of inputs from the previous layer, the weights of the connections, and the bias at the neuron level. Here's a detailed breakdown:
* Inputs for Activation Value:
* Activation Values of Neurons in the Previous Layer:These are the outputs from neurons in the preceding layer that serve as inputs to the current neuron.
* Weights Assigned to the Connections:Each connection between neurons has an associated weight, which determines the strength and direction of the input signal.
* Individual Bias at the Neuron Level:Each neuron has a bias value that adjusts the input sum, allowing the activation function to be shifted.
* Calculation:
* The activation value is computed by summing the weighted inputs from the previous layer and adding the bias.
* Formula: z=#(wi#ai)+bz = \sum (w_i \cdot a_i) + bz=#(wi#ai)+b, where wiw_iwi are the weights, aia_iai are the activation values from the previous layer, and bbb is the bias.
* The activation function (e.g., sigmoid, ReLU) is then applied to this sum to get the final activation value.
* Why Option A is Correct:
* 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.
* Eliminating Other Options:
* B. Activation values of neurons in the previous layer, and weights assigned to the connections between the neurons: This option misses the bias, which is crucial.
* C. Individual bias at the neuron level, and weights assigned to the connections between the neurons: This option misses the activation values from the previous layer.
* D. Individual bias at the neuron level, and activation values of neurons in the previous layer
This option misses the weights, which are essential.
References:
ISTQB CT-AI Syllabus, Section 6.1, Neural Networks, discusses the components and functioning of neurons in a neural network.
"Neural Network Activation Functions" (ISTQB CT-AI Syllabus, Section 6.1.1).


48. Frage
A motorcycle engine repair shop owner wants to detect a leaking exhaust valve and fix it before it falls and causes catastrophic damage to the engine. The shop developed and trained a predictive model with historical data files from known health engines and ones which experienced a catastrophic fails due to exhaust valve failure. The shop evaluated 200 engines using this model and then disassembled the engines to assess the true state of the valves, recording the results in the confusion matrix below.
What is the precision of this predictive model

Antwort: A

Begründung:
Precision is a performance metric used to evaluate the accuracy of positive predictions in a classification model. It is defined by the formula:
Precision=TPTP+FP×100%\text{Precision} = \frac{TP}{TP + FP} \times 100\%Precision=TP+FPTP×100% Where:
* TP (True Positives)= Number of correctly predicted positive cases
* FP (False Positives)= Number of incorrectly predicted positive cases
The confusion matrix provided in the question would typically list these values. Based on ISTQB's guidelines for calculating precision, selecting the correct number of true positives and false positives from the given data should yield94.2%as the precision.
* Section 5.1 - Confusion Matrix and ML Functional Performance Metricsexplains the calculation of precisionusing the confusion matrix.
Reference from ISTQB Certified Tester AI Testing Study Guide:


49. Frage
The stakeholders of a machine learning model have confirmed that they understand the objective and purpose of the model, and ensured that the proposed model aligns with their business priorities. They have also selected a framework and a machine learning model that they will be using. What should be the next step to progress along the machine learning workflow?

Antwort: A

Begründung:
The ML workflow typically involves iterative steps, beginning with data preparation once the model and framework are selected. The syllabus explains:
"The steps shown in Figure 1 (the ML workflow) do not include the integration of the ML model with the non-ML parts of the overall system. Typically, ML models cannot be deployed in isolation and need to be integrated with the non-ML parts... The next step would be data preparation as part of the ML workflow to provide input data to support training by an ML algorithm or prediction by an ML model."


50. Frage
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