CT-AI_V1.0_WORLD EXAM TOPIC - CT-AI_V1.0_WORLD BRAINDUMPS PDF

CT-AI_v1.0_World Exam Topic - CT-AI_v1.0_World Braindumps Pdf

CT-AI_v1.0_World Exam Topic - CT-AI_v1.0_World Braindumps Pdf

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ISQI ISTQB Certified Tester AI Testing (v1.0) Sample Questions (Q12-Q17):

NEW QUESTION # 12
"AllerEgo" is a product that uses sell-learning to predict the behavior of a pilot under combat situation for a variety of terrains and enemy aircraft formations. Post training the model was exposed to the real- world data and the model was found to bebehaving poorly. A lot of data quality tests had been performed on the data to bring it into a shape fit for training and testing.
Which ONE of the following options is least likely to describes the possible reason for the fall in the performance, especially when considering the self-learning nature of the Al system?
SELECT ONE OPTION

  • A. There was an algorithmic bias in the Al system.
  • B. The difficulty of defining criteria for improvement before the model can be accepted.
  • C. The fast pace of change did not allow sufficient time for testing.
  • D. The unknown nature and insufficient specification of the operating environment might have caused the poor performance.

Answer: B

Explanation:
* A. The difficulty of defining criteria for improvement before the model can be accepted.
* Defining criteria for improvement is a challenge in the acceptance of AI models, but it is not directly related to the performance drop in real-world scenarios. It relates more to the evaluation and deployment phase rather than affecting the model's real-time performance post-deployment.
* B. The fast pace of change did not allow sufficient time for testing.
* This can significantly affect the model's performance. If the system is self-learning, it needs to adapt quickly, and insufficient testing time can lead to incomplete learning and poor performance.
* C. The unknown nature and insufficient specification of the operating environment might have caused the poor performance.
* This is highly likely to affect performance. Self-learning AI systems require detailed specifications of the operating environment to adapt and learn effectively. If the environment is insufficiently specified, the model may fail to perform accurately in real-world scenarios.
* D. There was an algorithmic bias in the AI system.
* Algorithmic bias can significantly impact the performance of AI systems. If the model has biases, it will not perform well across different scenarios and data distributions.
Given the context of the self-learning nature and the need for real-time adaptability, optionAis least likely to describe the fall in performance because it deals with acceptance criteria rather than real-time performance issues.


NEW QUESTION # 13
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. Threshold coverage
  • B. Neuron coverage
  • C. Value coverage
  • D. Sign change coverage

Answer: D

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 # 14
Which ONE of the following statements is a CORRECT adversarial example in the context of machine learning systems that are working on image classifiers.
SELECT ONE OPTION

  • A. Black box attacks based on adversarial examples create an exact duplicate model of the original.
  • B. These examples are model specific and are not likely to cause another model trained on same task to fail.
  • C. These attack examples cause a model to predict the correct class with slightly less accuracy even though they look like the original image.
  • D. These attacks can't be prevented by retraining the model with these examples augmented tothe training data.

Answer: B

Explanation:
* A. Black box attacks based on adversarial examples create an exact duplicate model of the original.
* Black box attacks do not create an exact duplicate model. Instead, they exploit the model by querying it and using the outputs to craft adversarial examples without knowledge of the internal workings.
* B. These attack examples cause a model to predict the correct class with slightly less accuracy even though they look like the original image.
* Adversarial examples typically cause the model to predict the incorrect class rather than just reducing accuracy. These examples are designed to be visually indistinguishable from the original image but lead to incorrect classifications.
* C. These attacks can't be prevented by retraining the model with these examples augmented to the training data.
* This statement is incorrect because retraining the model with adversarial examples included in the training data can help the model learn to resist such attacks, a technique known as adversarial training.
* D. These examples are model specific and are not likely to cause another model trained on the same task to fail.
* Adversarial examples are often model-specific, meaning that they exploit the specific weaknesses of a particular model. While some adversarial examples might transfer between models, many are tailored to the specific model they were generated for and may not affect other models trained on the same task.
Therefore, the correct answer isDbecause adversarial examples are typically model-specific and may not cause another model trained on the same task to fail.


NEW QUESTION # 15
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

  • A. This statement is correct. In general, what is considered Al today may change over time.
  • B. This statement is incorrect. Current Al is true Al and there is no reason to believe that this fact will change over time.
  • 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 correct. In general, today the term Al is utilized incorrectly.

Answer: A

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 # 16
A company producing consumable goods wants to identify groups of people with similar tastes for the purpose of targeting different products for each group. You have to choose and apply an appropriate ML type for this problem.
Which ONE of the following options represents the BEST possible solution for this above-mentioned task?
SELECT ONE OPTION

  • A. Clustering
  • B. Regression
  • C. Classification
  • D. Association

Answer: A

Explanation:
* A. Regression
* Regression is used to predict a continuous value and is not suitable for grouping people based on similar tastes.
* B. Association
* Association is used to find relationships between variables in large datasets, often in the form of rules (e.g., market basket analysis). It does not directly group individuals but identifies patterns of co-occurrence.
* C. Clustering
* Clustering is an unsupervised learning method used to group similar data points based on their features. It is ideal for identifying groups of people with similar tastes without prior knowledge of the group labels. This technique will help the company segment its customer base effectively.
* D. Classification
* Classification is a supervised learning method used to categorize data points into predefined classes. It requires labeled data for training, which is not the case here as we want to identify groups without predefined labels.
Therefore, the correct answer isCbecause clustering is the most suitable method for grouping people with similar tastes for targeted product marketing.


NEW QUESTION # 17
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