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ISTQB CT-AI Exam Syllabus Topics:
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ISTQB Certified Tester AI Testing Exam Sample Questions (Q81-Q86):
NEW QUESTION # 81
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?
SELECT ONE OPTION
Answer: D
Explanation:
* A. This statement is incorrect. Current AI is true AI and there is no reason to believe that this fact will change over time.
AI is an evolving field, and the definition of what constitutes AI can change as technology advances.
* B. This statement is correct. In general, what is considered AI today may change over time.
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".
* C. This statement is incorrect. What is considered AI today will continue to be AI even as technology evolves and changes.
This perspective does not account for the historical evolution of the definition of AI . As new technologies emerge, the boundaries of AI shift.
* D. This statement is correct. In general, today the term AI is utilized incorrectly.
While some may argue this, it is not a universal truth. The term AI encompasses a broad range of technologies and applications, and its usage is generally consistent with current technological capabilities.
NEW QUESTION # 82
Which ONE of the following describes a situation of back-to-back testing the LEAST?
SELECT ONE OPTION
Answer: C
Explanation:
Back-to-back testing is a method where the same set of tests are run on multiple implementations of the system to compare their outputs. This type of testing is typically used to ensure consistency and correctness by comparing the outputs of different implementations under identical conditions. Let's analyze the options given:
A . Comparison of the results of a current neural network model ML model implemented in platform A (for example Pytorch) with a similar neural network model ML model implemented in platform B (for example Tensorflow), for the same data.
This option describes a scenario where two different implementations of the same type of model are being compared using the same dataset. This is a typical back-to-back testing situation.
B . Comparison of the results of a home-grown neural network model ML model with results in a neural network model implemented in a standard implementation (for example Pytorch) for the same data.
This option involves comparing a custom implementation with a standard implementation, which is also a typical back-to-back testing scenario to validate the custom model against a known benchmark.
C . Comparison of the results of a neural network ML model with a current decision tree ML model for the same data.
This option involves comparing two different types of models (a neural network and a decision tree). This is not a typical scenario for back-to-back testing because the models are inherently different and would not be expected to produce identical results even on the same data.
D . Comparison of the results of the current neural network ML model on the current data set with a slightly modified data set.
This option involves comparing the outputs of the same model on slightly different datasets. This could be seen as a form of robustness testing or sensitivity analysis, but not typical back-to-back testing as it doesn't involve comparing multiple implementations.
Based on this analysis, option C is the one that describes a situation of back-to-back testing the least because it compares two fundamentally different models, which is not the intent of back-to-back testing.
NEW QUESTION # 83
Which ONE of the following combinations of Training, Validation, Testing data is used during the process of learning/creating the model?
SELECT ONE OPTION
Answer: D
Explanation:
The process of developing a machine learning model typically involves the use of three types of datasets:
Training Data: This is used to train the model, i.e., to learn the patterns and relationships in the data.
Validation Data: This is used to tune the model's hyperparameters and to prevent overfitting during the training process.
Test Data: This is used to evaluate the final model's performance and to estimate how it will perform on unseen data.
Let's analyze each option:
A . Training data - validation data - test data
This option correctly includes all three types of datasets used in the process of creating and validating a model. The training data is used for learning, validation data for tuning, and test data for final evaluation.
B . Training data - validation data
This option misses the test data, which is crucial for evaluating the model's performance on unseen data after the training and validation phases.
C . Training data - test data
This option misses the validation data, which is important for tuning the model and preventing overfitting during training.
D . Validation data - test data
This option misses the training data, which is essential for the initial learning phase of the model.
Therefore, the correct answer is A because it includes all necessary datasets used during the process of learning and creating the model: training, validation, and test data.
NEW QUESTION # 84
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
Answer: D
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.
References:
* 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 # 85
An airline has created a ML model to project fuel requirements for future flights. The model imports weather data such as wind speeds and temperatures, calculates flight routes based on historical routings from air traffic control, and estimates loads from average passenger and baggage weights. The model performed within an acceptable standard for the airline throughout the summer but as winter set in the load weights became less accurate. After some exploratory data analysis it became apparent that luggage weights were higher in the winter than in summer.
Which of the following statements BEST describes the problem and how it could have been prevented?
Answer: D
Explanation:
The problem described in the question is a classic case ofconcept drift. Concept drift occurs when the relationship between input variables and the output variable changes over time, leading to a decline in model accuracy.
In this scenario, theaverage passenger and baggage weightsused in the model changed due to seasonal variations, but the model was not updated accordingly. This resulted in inaccurate predictions for fuel requirements in the winter season. This is an example ofseasonal drift, where model behavior changes periodically due to recurring trends (e.g., higher luggage weights in winter compared to summer).
To prevent such problems:
* Themodel should be regularly testedfor concept drift against agreed ML functional performance criteria.
* Exploratory Data Analysis (EDA)should be performed periodically to detect gradual changes in input distributions.
* Retraining of the modelwith updated training data should be done to maintain accuracy.
* If drift is detected, mitigation techniques such asincremental learning, retraining with new data, or adjusting model parametersshould be employed.
* Option B (Easing the performance standard instead of addressing drift): Lowering the performance standard is not a solution; it only masks the problem without fixing it. Instead, regular testing and retraining should be used to handle drift properly.
* Option C (Corruption and reloading the model): Model corruption is unrelated to this issue.
Corruption refers to accidental or malicious damage to the model or data, whereas this case is due to a changing data environment.
* Option D (Lack of transparency): Transparency refers to how understandable the model's decisions are, but the problem here is a change in data distributions, making drift the primary concern.
* ISTQB CT-AI Syllabus (Section 7.6: Testing for Concept Drift)
* "The operational environment can change over time without the trained model changing correspondingly. This phenomenon is known as concept drift and typically causes the outputs of the model to become increasingly less accurate and less useful."
* "Systems that may be prone to concept drift should be regularly tested against their agreed ML functional performance criteria to ensure that any occurrences of concept drift are detected soon enough for the problem to be mitigated."
* ISTQB CT-AI Syllabus (Section 7.7: Selecting a Test Approach for an ML System)
* "If concept drift is detected, it may be mitigated by retraining the system with up-to-date training data followed by confirmation testing, regression testing, and possibly A/B testing where the updated system must outperform the original system." Why Other Options Are Incorrect:Supporting References from ISTQB Certified Tester AI Testing Study Guide:Conclusion:Since the question describes a situation whereseasonal variations affected input data distributions, the correct answer isA: The model suffers from drift and therefore should be regularly tested to ensure that any occurrences of drift are detected soon enough for the problem to be mitigated.
NEW QUESTION # 86
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