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ISQI ISTQB Certified Tester AI Testing (v1.0) Sample Questions (Q25-Q30):

NEW QUESTION # 25
ln the near future, technology will have evolved, and Al will be able to learn multiple tasks by itself without needing to be retrained, allowing it to operate even in new environments. The cognitive abilities of Al are similar to a child of 1-2 years.' In the above quote, which ONE of the following options is the correct name of this type of Al?
SELECT ONE OPTION

A. Technological singularity
B. General Al
C. Narrow Al
D. Super Al

Answer: B

Explanation:
A: Technological singularity
* Technological singularity refers to a hypothetical point in the future when AI surpasses human intelligence and can continuously improve itself without human intervention. This scenario involves capabilities far beyond those described in the question.
B: Narrow AI
* Narrow AI, also known as weak AI, is designed to perform a specific task or a narrow range of tasks. It does not have general cognitive abilities and cannot learn multiple tasks by itself without retraining.
C: Super AI
* Super AI refers to an AI that surpasses human intelligence and capabilities across all fields. This is an advanced concept and not aligned with the description of having cognitive abilities similar to a young child.
D: General AI
* General AI, or strong AI, has the ability to understand, learn, and apply knowledge across a wide range of tasks, similar to human cognitive abilities. It aligns with the description of AI that can learn multiple tasks and operate in new environments without needing retraining.

NEW QUESTION # 26
"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. The fast pace of change did not allow sufficient time for testing.
B. The unknown nature and insufficient specification of the operating environment might have caused the poor performance.
C. There was an algorithmic bias in the Al system.
D. The difficulty of defining criteria for improvement before the model can be accepted.

Answer: D

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 # 27
Which ONE of the following hardware is MOST suitable for implementing Al when using ML?
SELECT ONE OPTION

A. Hardware supporting fast matrix multiplication.
B. Hardware supporting high precision floating point operations.
C. High powered CPUs.
D. 64-bit CPUs.

Answer: A

Explanation:
* A. 64-bit CPUs.
* While 64-bit CPUs are essential for handling large amounts of memory and performing complex computations, they are not specifically optimized for the types of operations commonly used in machine learning.
* B. Hardware supporting fast matrix multiplication.
* Matrix multiplication is a fundamental operation in many machine learning algorithms, especially in neural networks and deep learning. Hardware optimized for fast matrix multiplication, such as GPUs (Graphics Processing Units), is most suitable for implementing AI and ML because it can handle the parallel processing required for these operations efficiently.
* C. High powered CPUs.
* High powered CPUs are beneficial for general-purpose computing tasks and some aspects of ML, but they are not as efficient as specialized hardware like GPUs for matrix multiplication and other ML-specific tasks.
* D. Hardware supporting high precision floating point operations.
* High precision floating point operations are important for scientific computing and some specific AI tasks, but for many ML applications, fast matrix multiplication is more critical than high precision alone.
Therefore, the correct answer isBbecause hardware supporting fast matrix multiplication, such as GPUs, is most suitable for the parallel processing requirements of machine learning.

NEW QUESTION # 28
Which ONE of the following options BEST DESCRIBES clustering?
SELECT ONE OPTION

A. Clustering requires you to know the classes.
B. Clustering is classification of a continuous quantity.
C. Clustering is done without prior knowledge of output classes.
D. Clustering is supervised learning.

Answer: C

Explanation:
Clustering is a type of machine learning technique used to group similar data points into clusters. It is a key concept in unsupervised learning, where the algorithm tries to find patterns or groupings in data without prior knowledge of output classes. Let's analyze each option:
* A. Clustering is classification of a continuous quantity.
* This is incorrect. Classification typically involves discrete categories, whereas clustering involves grouping similar data points. Classification of continuous quantities is generally referred to as regression.
* B. Clustering is supervised learning.
* This is incorrect. Clustering is an unsupervised learning technique because it does not rely on labeled data.
* C. Clustering is done without prior knowledge of output classes.
* This is correct. In clustering, the algorithm groups data points into clusters without any prior knowledge of the classes. It discovers the inherent structure in the data.
* D. Clustering requires you to know the classes.
* This is incorrect. Clustering does not require prior knowledge of classes. Instead, it aims to identify and form the classes or groups based on the data itself.
Therefore, the correct answer isCbecause clustering is an unsupervised learning technique done without prior knowledge of output classes.

NEW QUESTION # 29
Which ONE of the following is the BEST option to optimize the regression test selection and prevent the regression suite from growing large?
SELECT ONE OPTION

A. Using an Al-based tool to optimize the regression test suite by analyzing past test results
B. Automating test scripts using Al-based test automation tools.
C. Identifying suitable tests by looking at the complexity of the test cases.
D. Using of a random subset of tests.

Answer: A

Explanation:
* A. Identifying suitable tests by looking at the complexity of the test cases.
* While complexity analysis can help in selecting important test cases, it does not directly address the issue of optimizing the entire regression suite effectively.
* B. Using a random subset of tests.
* Randomly selecting test cases may miss critical tests and does not ensure an optimized regression suite. This approach lacks a systematic method for ensuring comprehensive coverage.
* C. Automating test scripts using AI-based test automation tools.
* Automation helps in running tests efficiently but does not inherently optimize the selection of tests to prevent the suite from growing too large.
* D. Using an AI-based tool to optimize the regression test suite by analyzing past test results.
* This is the most effective approach as AI-based tools can analyze historical test data, identify patterns, and prioritize tests that are more likely to catch defects based onpast results. This method ensures an optimized and manageable regression test suite by focusing on the most impactful test cases.
Therefore, the correct answer isDbecause using an AI-based tool to analyze past test results is the best option to optimize regression test selection and manage the size of the regression suite effectively.

NEW QUESTION # 30
......

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2025 New CT-AI_v1.0_World Test Pdf | The Best 100% Free CT-AI_v1.0_World Valid Exam Labs

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ISQI ISTQB Certified Tester AI Testing (v1.0) Sample Questions (Q25-Q30):

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