CT-AI_V1.0_WORLD LATEST TEST PREPARATION & NEW CT-AI_V1.0_WORLD TEST PDF

CT-AI_v1.0_World Latest Test Preparation & New CT-AI_v1.0_World Test Pdf

CT-AI_v1.0_World Latest Test Preparation & New CT-AI_v1.0_World Test Pdf

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ISQI ISTQB Certified Tester AI Testing (v1.0) Sample Questions (Q24-Q29):

NEW QUESTION # 24
A software component uses machine learning to recognize the digits from a scan of handwritten numbers. In the scenario above, which type of Machine Learning (ML) is this an example of?
SELECT ONE OPTION

  • A. Reinforcement learning
  • B. Regression
  • C. Classification
  • D. Clustering

Answer: C

Explanation:
Recognizing digits from a scan of handwritten numbers using machine learning is an example of classification.
Here's a breakdown:
* Classification: This type of machine learning involves categorizing input data into predefined classes.
In this scenario, the input data (handwritten digits) are classified into one of the 10 digit classes (0-9).
* Why Not Other Options:
* Reinforcement Learning: This involves learning by interacting with an environment to achieve a goal, which does not fit the problem of recognizing digits.
* Regression: This is used for predicting continuous values, not discrete categories like digit recognition.
* Clustering: This involves grouping similar data points together without predefined classes, which is not the case here.
References:The explanation is based on the definitions of different machine learning types as outlined in the ISTQB CT-AI syllabus, specifically under supervised learning and classification.


NEW QUESTION # 25
An image classification system is being trained for classifying faces of humans. The distribution of the data is
70% ethnicity A and 30% for ethnicities B, C and D. Based ONLY on the above information, which of the following options BEST describes the situation of this image classification system?
SELECT ONE OPTION

  • A. This is an example of expert system bias.
  • B. This is an example of algorithmic bias.
  • C. This is an example of hyperparameter bias.
  • D. This is an example of sample bias.

Answer: D

Explanation:
* A. This is an example of expert system bias.
* Expert system bias refers to bias introduced by the rules or logic defined by experts in the system, not by the data distribution.
* B. This is an example of sample bias.
* Sample bias occurs when the training data is not representative of the overall population that the model will encounter in practice. In this case, the over-representation of ethnicity A (70%) compared to B, C, and D (30%) creates a sample bias, as the model may become biased towards better performance on ethnicity A.
* C. This is an example of hyperparameter bias.
* Hyperparameter bias relates to the settings and configurations used during the training process, not the data distribution itself.
* D. This is an example of algorithmic bias.
* Algorithmic bias refers to biases introduced by the algorithmic processes and decision-making rules, not directly by the distribution of training data.
Based on the provided information, optionB(sample bias) best describes the situation because the training data is skewed towards ethnicity A, potentially leading to biased model performance.


NEW QUESTION # 26
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 * test data
  • B. Validation data - test data
  • C. Training data - validation data - test data
  • D. Training data - validation data

Answer: C

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

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

Answer: B

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

Answer: A

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
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