QUIZ ISQI - HIGH PASS-RATE CT-AI_V1.0_WORLD - ISTQB CERTIFIED TESTER AI TESTING (V1.0) DUMPS

Quiz ISQI - High Pass-Rate CT-AI_v1.0_World - ISTQB Certified Tester AI Testing (v1.0) Dumps

Quiz ISQI - High Pass-Rate CT-AI_v1.0_World - ISTQB Certified Tester AI Testing (v1.0) Dumps

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ISQI ISTQB Certified Tester AI Testing (v1.0) Sample Questions (Q18-Q23):

NEW QUESTION # 18
Which ONE of the following types of coverage SHOULD be used if test cases need to cause each neuron to achieve both positive and negative activation values?
SELECT ONE OPTION

  • A. Neuron coverage
  • B. Threshold coverage
  • C. Sign change coverage
  • D. Value coverage

Answer: C

Explanation:
Coverage for Neuron Activation Values:Sign change coverage is used to ensure that test cases cause each neuron to achieve both positive and negative activation values. This type of coverage ensures that the neurons are thoroughly tested under different activation states.
Reference:ISTQB_CT-AI_Syllabus_v1.0, Section 6.2 Coverage Measures for Neural Networks, which details different types of coverage measures, including sign change coverage.


NEW QUESTION # 19
In a certain coffee producing region of Colombia, there have been some severe weather storms, resulting in massive losses in production. This caused a massive drop in stock price of coffee.
Which ONE of the following types of testing SHOULD be performed for a machine learning model for stock-price prediction to detect influence of such phenomenon as above on price of coffee stock.
SELECT ONE OPTION

  • A. Testing for security
  • B. Testing for bias
  • C. Testing for accuracy
  • D. Testing for concept drift

Answer: D

Explanation:
Type of Testing for Stock-Price Prediction Models:Concept drift refers to the change in the statistical properties of the target variable over time. Severe weather storms causing massive lossesin coffee production and affecting stock prices would require testing for concept drift to ensure that the model adapts to new patterns in data over time.
Reference:ISTQB_CT-AI_Syllabus_v1.0, Section 7.6 Testing for Concept Drift, which explains the need to test for concept drift in models that might be affected by changing external factors.


NEW QUESTION # 20
Which ONE of the following combinations of Training, Validation, Testing data is used during the process of learning/creating the model?
SELECT ONE OPTION

  • A. Training data - validation data - test data
  • B. Validation data - test data
  • C. Training data * test data
  • D. Training data - validation data

Answer: A

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 isAbecause it includes all necessary datasets used during the process of learning and creating the model: training, validation, and test data.


NEW QUESTION # 21
Which ONE of the following options describes a scenario of A/B testing the LEAST?
SELECT ONE OPTION

  • A. A comparison of two different offers in a recommendation system to decide on the more effective offer for same users.
  • B. A comparison of two different websites for the same company to observe from a user acceptance perspective.
  • C. A comparison of the performance of two different ML implementations on the same input data.
  • D. A comparison of the performance of an ML system on two different input datasets.

Answer: D

Explanation:
A/B testing, also known as split testing, is a method used to compare two versions of a product or system to determine which one performs better. It is widely used in web development, marketing, and machine learning to optimize user experiences and model performance. Here's why option C is the least descriptive of an A/B testing scenario:
* Understanding A/B Testing:
* In A/B testing, two versions (A and B) of a system or feature are tested against each other. The objective is to measure which version performs better based on predefined metrics such as user engagement, conversion rates, or other performance indicators.
* Application in Machine Learning:
* In ML systems, A/B testing might involve comparing two different models, algorithms, or system configurations on the same set of data to observe which yields better results.
* Why Option C is the Least Descriptive:
* Option C describes comparing the performance of an ML system on two different input datasets.
This scenario focuses on the input data variation rather than the comparison of system versions or features, which is the essence of A/B testing. A/B testing typically involves a controlled experiment with two versions being tested under the same conditions, not different datasets.
* Clarifying the Other Options:
* A. A comparison of two different websites for the same company to observe from a user acceptance perspective: This is a classic example of A/B testing where two versions of a website are compared.
* B. A comparison of two different offers in a recommendation system to decide on the more effective offer for the same users: This is another example of A/B testing in a recommendation system.
* D. A comparison of the performance of two different ML implementations on the same input data: This fits the A/B testing model where two implementations are compared under the same conditions.
References:
* ISTQB CT-AI Syllabus, Section 9.4, A/B Testing, explains the methodology and application of A/B testing in various contexts.
* "Understanding A/B Testing" (ISTQB CT-AI Syllabus).


NEW QUESTION # 22
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 II
  • B. I and III
  • C. I and II
  • D. Only 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.
References:
* 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 # 23
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