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NEW QUESTION # 44
Which of the following is a problem with AI-generated test cases that are generated from the requirements?
- A. They are usually missing the expected results, so verification is difficult or must resort to only detecting significant failures
- B. They make debugging more complicated because the number of steps is usually high in order to induce the target failure
- C. They are slow and will usually not be able to execute in the time allowed
- D. They are defect-prone because they are unable to detect nuances in the requirements
Answer: A
Explanation:
The syllabus mentions a drawback of AI-generated test cases:
"AI-based test generation tools can generate test cases... However, unless a test model that defines required behaviors is used as the basis of the tests, this form of test generation generally suffers from a test oracle problem because the AI-based tool does not know what the expected results should be." (Reference: ISTQB CT-AI Syllabus v1.0, Section 11.3, page 78 of 99)
NEW QUESTION # 45
A beer company is trying to understand how much recognition its logo has in the market. It plans to do that by monitoring images on various social media platforms using a pre-trained neural network for logo detection.
This particular model has been trained by looking for words, as well as matching colors on social media images. The company logo has a big word across the middle with a bold blue and magenta border.
Which associated risk is most likely to occur when using this pre-trained model?
- A. Insufficient function: the model was not trained to check for colors or words
- B. There is no risk, as the model has already been trained
- C. Improper data preparation
- D. Inherited bias: the model could have inherited unknown defects
Answer: D
Explanation:
According to the syllabus, pre-trained models often inherit biases and limitations from the data and processes used in their original training, which may not align with the new use case. Specifically, the syllabus states:
"When using a pre-trained model, the training data and process cannot be fully controlled or known by the user of the model. As a result, the model can inherit biases or inaccuracies that were part of its original development and training process." (Reference: ISTQB CT-AI Syllabus v1.0, Section 1.8.3)
NEW QUESTION # 46
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. I and III
- B. Only II
- C. Only III
- D. I and II
Answer: D
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.
Reference:
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 # 47
Which of the following is an example of overfitting?
- A. The model is too simplistic for the data.
- B. The model is not able to generalize to accommodate new types of data.
- C. The model is missing relationships between the inputs and outputs.
- D. The model discards data it considers to be noise or outliers.
Answer: B
Explanation:
Overfitting occurs when a machine learning (ML) model learns patterns that are too specific to the training data, leading to a lack of generalization for new, unseen data. This means the model performs exceptionally well on the training data but poorly on validation or test data because it has memorized the noise and minor details rather than learning the underlying patterns.
* Option A:"The model is not able to generalize to accommodate new types of data."
* This is the correct definition of overfitting. When a model cannot generalize beyond its training data, it struggles with new input, which results in overfitting.
* Option B:"The model is too simplistic for the data."
* This describes underfitting rather than overfitting. Underfitting happens when a model is too simple to capture the underlying patterns in the data.
* Option C:"The model is missing relationships between the inputs and outputs."
* This also aligns more with underfitting, where the model fails to capture important relationships in the data.
* Option D:"The model discards data it considers to be noise or outliers."
* While some ML models may ignore outliers, overfitting actually occurs when the model includes noise and outliers in its learning process rather than discarding them.
* Overfitting Definition:"Overfitting occurs when the model fits too closely to a set of data points and fails to properly generalize. It works well on training data but struggles with new data.".
* Testing for Overfitting:"Overfitting may be detected by testing the model with a dataset that is completely independent of the training dataset" Analysis of the Answer Options:ISTQB CT-AI Syllabus References:
NEW QUESTION # 48
Which of the following is a technique used in machine learning?
- A. Boundary value analysis
- B. Decision trees
- C. Equivalence partitioning
- D. Decision tables
Answer: B
Explanation:
Decision trees are a widely usedmachine learning (ML) techniquethat falls undersupervised learning. They are used for bothclassification and regressiontasks and are popular due to their interpretability and effectiveness.
* How Decision Trees Work:
* The model splits the dataset into branches based on feature conditions.
* It continues to divide the data until each subset belongs to a single category (classification) or predicts a continuous value (regression).
* The final result is a tree structure where decisions are made atnodes, and predictions are given at leaf nodes.
* Common Applications of Decision Trees:
* Fraud detection
* Medical diagnosis
* Customer segmentation
* Recommendation systems
* B (Equivalence Partitioning):This is asoftware testing technique, not a machine learning method. It is used to divide input data into partitions to reduce test cases while maintaining coverage.
* C (Boundary Value Analysis):Anothersoftware testing technique, used to check edge cases around input boundaries.
* D (Decision Tables):A structuredtesting techniqueused to validate business rules and logic, not a machine learning method.
* ISTQB CT-AI Syllabus (Section 3.1: Forms of Machine Learning - Decision Trees)
* "Decision trees are used in classification and regression models and are fundamental ML algorithms".
Why Other Options Are Incorrect:Supporting References from ISTQB Certified Tester AI Testing Study Guide:Conclusion:Sincedecision trees are a core technique in machine learning, while the other options are software testing techniques, thecorrect answer is A.
NEW QUESTION # 49
Which ONE of the following options is an example that BEST describes a system with Al-based autonomous functions?
SELECT ONE OPTION
- A. A system that utilizes a tool like Selenium.
- B. A system that utilizes human beings for all important decisions.
- C. A system that is fully able to respond to its environment.
- D. A fully automated manufacturing plant that uses no software.
Answer: C
Explanation:
* AI-Based Autonomous Functions: An AI-based autonomous system is one that can respond to its environment without human intervention. The other options either involve human decisions or do not use AI at all.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Sections on Autonomy and Testing Autonomous AI-Based Systems.
NEW QUESTION # 50
The activation value output for a neuron in a neural network is obtained by applying computation to the neuron.
Which ONE of the following options BEST describes the inputs used to compute the activation value?
SELECT ONE OPTION
- A. Individual bias at the neuron level, and weights assigned to the connections between the neurons.
- B. Individual bias at the neuron level, and activation values of neurons in the previous layer.
- C. Activation values of neurons in the previous layer, and weights assigned to the connections between the neurons.
- D. Individual bias at the neuron level, activation values of neurons in the previous layer, and weights assigned to the connections between the neurons.
Answer: D
Explanation:
In a neural network, the activation value of a neuron is determined by a combination of inputs from the previous layer, the weights of the connections, and the bias at the neuron level. Here's a detailed breakdown:
Inputs for Activation Value:
Activation Values of Neurons in the Previous Layer: These are the outputs from neurons in the preceding layer that serve as inputs to the current neuron.
Weights Assigned to the Connections: Each connection between neurons has an associated weight, which determines the strength and direction of the input signal.
Individual Bias at the Neuron Level: Each neuron has a bias value that adjusts the input sum, allowing the activation function to be shifted.
Calculation:
The activation value is computed by summing the weighted inputs from the previous layer and adding the bias.
Formula: z=∑(wiai)+bz = \sum (w_i \cdot a_i) + bz=∑(wiai)+b, where wiw_iwi are the weights, aia_iai are the activation values from the previous layer, and bbb is the bias.
The activation function (e.g., sigmoid, ReLU) is then applied to this sum to get the final activation value.
Why Option A is Correct:
Option A correctly identifies all components involved in computing the activation value: the individual bias, the activation values of the previous layer, and the weights of the connections.
Eliminating Other Options:
B . Activation values of neurons in the previous layer, and weights assigned to the connections between the neurons: This option misses the bias, which is crucial.
C . Individual bias at the neuron level, and weights assigned to the connections between the neurons: This option misses the activation values from the previous layer.
D . Individual bias at the neuron level, and activation values of neurons in the previous layer: This option misses the weights, which are essential.
Reference:
ISTQB CT-AI Syllabus, Section 6.1, Neural Networks, discusses the components and functioning of neurons in a neural network.
"Neural Network Activation Functions" (ISTQB CT-AI Syllabus, Section 6.1.1).
NEW QUESTION # 51
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 sample bias.
- C. This is an example of algorithmic bias.
- D. This is an example of hyperparameter bias.
Answer: B
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, option B (sample bias) best describes the situation because the training data is skewed towards ethnicity A, potentially leading to biased model performance.
NEW QUESTION # 52
Which of the following is correct regarding the layers of a deep neural network?
- A. There is only an input and output layer
- B. There is at least one internal hidden layer
- C. The output layer is not connected with the other layers to maintain integrity
- D. There must be a minimum of five total layers to be considered deep
Answer: B
Explanation:
The syllabus clearly explains the structure of a deep neural network (DNN):
"A deep neural network comprises three types of layers. The input layer receives inputs... Between the input and output layers are hidden layers made up of artificial neurons, which are also known as nodes." (Reference: ISTQB CT-AI Syllabus v1.0, Section 6.1, page 45 of 99)
NEW QUESTION # 53
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. Narrow Al
- C. Super Al
- D. General Al
Answer: D
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 # 54
Pairwise testing can be used in the context of self-driving cars for controlling an explosion in the number of combinations of parameters.
Which ONE of the following options is LEAST likely to be a reason for this incredible growth of parameters?
SELECT ONE OPTION
- A. ML model metrics to evaluate the functional performance
- B. Different weather conditions
- C. Different Road Types
- D. Different features like ADAS, Lane Change Assistance etc.
Answer: A
Explanation:
Pairwise testing is used to handle the large number of combinations of parameters that can arise in complex systems like self-driving cars. The question asks which of the given options is least likely to be a reason for the explosion in the number of parameters.
Different Road Types (A): Self-driving cars must operate on various road types, such as highways, city streets, rural roads, etc. Each road type can have different characteristics, requiring the car's system to adapt and handle different scenarios. Thus, this is a significant factor contributing to the growth of parameters.
Different Weather Conditions (B): Weather conditions such as rain, snow, fog, and bright sunlight significantly affect the performance of self-driving cars. The car's sensors and algorithms must adapt to these varying conditions, which adds to the number of parameters that need to be considered.
ML Model Metrics to Evaluate Functional Performance (C): While evaluating machine learning (ML) model performance is crucial, it does not directly contribute to the explosion of parameter combinations in the same way that road types, weather conditions, and car features do. Metrics are used to measure and assess performance but are not themselves variable conditions that the system must handle.
Different Features like ADAS, Lane Change Assistance, etc. (D): Advanced Driver Assistance Systems (ADAS) and other features add complexity to self-driving cars. Each feature can have multiple settings and operational modes, contributing to the overall number of parameters.
Hence, the least likely reason for the incredible growth in the number of parameters is C. ML model metrics to evaluate the functional performance.
Reference:
ISTQB CT-AI Syllabus Section 9.2 on Pairwise Testing discusses the application of this technique to manage the combinations of different variables in AI-based systems, including those used in self-driving cars.
Sample Exam Questions document, Question #29 provides context for the explosion in parameter combinations in self-driving cars and highlights the use of pairwise testing as a method to manage this complexity.
NEW QUESTION # 55
The activation value output for a neuron in a neural network is obtained by applying computation to the neuron.
Which ONE of the following options BEST describes the inputs used to compute the activation value?
SELECT ONE OPTION
- A. Individual bias at the neuron level, and weights assigned to the connections between the neurons.
- B. Individual bias at the neuron level, and activation values of neurons in the previous layer.
- C. Activation values of neurons in the previous layer, and weights assigned to the connections between the neurons.
- D. Individual bias at the neuron level, activation values of neurons in the previous layer, and weights assigned to the connections between the neurons.
Answer: D
Explanation:
In a neural network, the activation value of a neuron is determined by a combination of inputs from the previous layer, the weights of the connections, and the bias at the neuron level. Here's a detailed breakdown:
* Inputs for Activation Value:
* Activation Values of Neurons in the Previous Layer:These are the outputs from neurons in the preceding layer that serve as inputs to the current neuron.
* Weights Assigned to the Connections:Each connection between neurons has an associated weight, which determines the strength and direction of the input signal.
* Individual Bias at the Neuron Level:Each neuron has a bias value that adjusts the input sum, allowing the activation function to be shifted.
* Calculation:
* The activation value is computed by summing the weighted inputs from the previous layer and adding the bias.
* Formula: z=#(wi#ai)+bz = \sum (w_i \cdot a_i) + bz=#(wi#ai)+b, where wiw_iwi are the weights, aia_iai are the activation values from the previous layer, and bbb is the bias.
* The activation function (e.g., sigmoid, ReLU) is then applied to this sum to get the final activation value.
* Why Option A is Correct:
* Option A correctly identifies all components involved in computing the activation value: the individual bias, the activation values of the previous layer, and the weights of the connections.
* Eliminating Other Options:
* B. Activation values of neurons in the previous layer, and weights assigned to the connections between the neurons: This option misses the bias, which is crucial.
* C. Individual bias at the neuron level, and weights assigned to the connections between the neurons: This option misses the activation values from the previous layer.
* D. Individual bias at the neuron level, and activation values of neurons in the previous layer: This option misses the weights, which are essential.
References:
* ISTQB CT-AI Syllabus, Section 6.1, Neural Networks, discusses the components and functioning of neurons in a neural network.
* "Neural Network Activation Functions" (ISTQB CT-AI Syllabus, Section 6.1.1).
NEW QUESTION # 56
Which of the following is a problem with AI-generated test cases that are generated from the requirements?
- A. They are slow and will usually not be able to execute in the time allowed.
- B. They are defect prone because they are unable to detect nuances in the requirements.
- C. They make debugging more complicated because the number of steps is usually high in order to induce the target failure.
- D. They are usually missing the expected results, so verification is difficult or must resort to only detecting significant failures.
Answer: D
Explanation:
AI-generated test cases are often created using machine learning (ML) models or heuristic algorithms. While these can be effective in generating large numbers of test cases quickly, they oftensuffer from the "test oracle problem."
* Test Oracle Problem:A test oracle is the mechanism used to determine the expected output of a test case. AI-generated test cases oftenlack expected resultsbecause AI-based tools do not inherently understand what the correct output should be.
* Difficulty in Verification:Without expected results, verifying test cases becomes challenging. Testers mustrely on heuristics, anomaly detection, or significant failures, rather than traditional pass/fail conditions.
* A (Slow Execution Time):AI-generated tests are typically automated and designed for efficiency. They are not inherently slow and often executefasterthan manually written tests.
* B (Defect-Prone Due to Nuance Issues):While AI-generated tests may struggle with some complexities in requirements, they primarilylack expected results, rather than failing due to an inability to detect nuances.
* C (Complicated Debugging Due to Many Steps):AI-generated testsreducedebugging complexity by limiting the number of steps required to reproduce failures.
* ISTQB CT-AI Syllabus (Section 11.3: Using AI for Test Case Generation)
* "AI-generated test cases often lack expected results, making it difficult to verify correctness without a test oracle.".
* "Verification often relies on detecting significant failures rather than having predefined expected results.".
Why Other Options Are Incorrect:Supporting References from ISTQB Certified Tester AI Testing Study Guide:Conclusion:Since AI-generated test cases frequentlylack expected results, verification becomes difficult, requiring testers tofocus on major failuresrather than precise pass/fail conditions. Thus, thecorrect answer is D.
NEW QUESTION # 57
When verifying that an autonomous AI-based system is acting appropriately, which of the following are MOST important to include?
- A. Test cases to detect the system appropriately automating its data input
- B. Test cases to verify that the system automatically confirms the correct classification of training data
- C. Test cases to detect the system prompting for unnecessary human intervention
- D. Test cases to verify that the system automatically suppresses invalid output data
Answer: C
Explanation:
When verifyingautonomous AI-based systems, a critical aspect is ensuring that they maintain an appropriate level of autonomy whileonly requesting human intervention when necessary. If an AI system unnecessarily asks for human input, it defeats the purpose of autonomy and can:
* Slow down operations.
* Reduce trust in the system.
* Indicate improper confidence thresholds in decision-making.
This is particularly crucial inautonomous vehicles, AI-driven financial trading, and robotic process automation, where excessive human intervention would hinder performance.
* A. Test cases to verify that the system automatically confirms the correct classification of training data# This is relevant for verifying training consistency but not for autonomy validation.
* B. Test cases to detect the system appropriately automating its data input# While relevant, data automation does not directly address the verification of autonomy.
* D. Test cases to verify that the system automatically suppresses invalid output data# This focuses on output filtering rather than decision-making autonomy.
Why are the other options incorrect?Thus, the mostcritical test casefor verifyingautonomous AI-based systemsis ensuring that itdoes not unnecessarily request human intervention.
* Section 8.2 - Testing Autonomous AI-Based Systemsstates that it is crucial to testwhether the system requests human intervention only when necessaryand does not disrupt autonomy.
Reference from ISTQB Certified Tester AI Testing Study Guide:
NEW QUESTION # 58
Which of the following is an example of a clustering problem that can be resolved by unsupervised learning?
- A. Grouping individual fish together based on their types of fins
- B. Classifying muffin purchases based on the perceived attractiveness of their packaging
- C. Estimating the expected purchase of cat food after a particularly successful ad campaign
- D. Associating shoppers with their shopping tendencies
Answer: D
Explanation:
The syllabus defines clustering as:
"Clustering: This is when the problem requires the identification of similarities in input data points that allows them to be grouped based on common characteristics or attributes. For example, clustering is used to categorize different types of customers for the purpose of marketing." (Reference: ISTQB CT-AI Syllabus v1.0, Section 3.1.2, page 26 of 99)
NEW QUESTION # 59
Which of the following is an example of a clustering problem that can be resolved by unsupervised learning?
- A. Grouping individual fish together based on their types of fins
- B. Classifying muffin purchases based on the perceived attractiveness of their packaging
- C. Estimating the expected purchase of cat food after a particularly successful ad campaign
- D. Associating shoppers with their shopping tendencies
Answer: D
Explanation:
Clustering is a form ofunsupervised learning, which groups data points based onsimilarities without predefined labels. According toISTQB CT-AI Syllabus, clustering is used in scenarios where:
* The objective is to find natural groupings in data.
* The dataset does not have labeled outputs.
* Patterns and structures need to be identified automatically.
Analyzing the answer choices:
* A. Associating shoppers with their shopping tendencies # Correct
* Shoppers can be grouped based on purchasing behaviors(e.g., luxury shoppers vs. budget- conscious shoppers), which is a typical clustering application in market segmentation.
* B. Grouping individual fish together based on their types of fins # Incorrect
* If thetypes of fins are labeled, it becomes aclassification problem, which requires supervised learning.
* C. Classifying muffin purchases based on packaging attractiveness # Incorrect
* Classification, not clustering, because attractiveness scores or labels must be predefined.
* D. Estimating the expected purchase of cat food after an ad campaign # Incorrect
* This is a prediction task, best suited forregression models, which are part of supervised learning.
Thus,Option A is the best answer, asclusteringis used togroup shoppers based on tendencies without predefined labels.
Certified Tester AI Testing Study Guide References:
* ISTQB CT-AI Syllabus v1.0, Section 3.1.2 (Unsupervised Learning - Clustering and Association)
* ISTQB CT-AI Syllabus v1.0, Section 3.3 (Selecting a Form of ML - Clustering).
NEW QUESTION # 60
Which of the following is a problem with AI-generated test cases that are generated from the requirements?
- A. They are usually missing the expected results, so verification is difficult or must resort to only detecting significant failures
- B. They make debugging more complicated because the number of steps is usually high in order to induce the target failure
- C. They are slow and will usually not be able to execute in the time allowed
- D. They are defect-prone because they are unable to detect nuances in the requirements
Answer: A
Explanation:
The syllabus mentions a drawback of AI-generated test cases:
"AI-based test generation tools can generate test cases... However, unless a test model that defines required behaviors is used as the basis of the tests, this form of test generation generally suffers from a test oracle problem because the AI-based tool does not know what the expected results should be." (Reference: ISTQB CT-AI Syllabus v1.0, Section 11.3, page 78 of 99)
NEW QUESTION # 61
Which of the following characteristics of AI-based systems make it more difficult to ensure they are safe?
- A. Sustainability
- B. Simplicity
- C. Robustness
- D. Non-determinism
Answer: D
Explanation:
AI-based systems oftenexhibit non-deterministic behavior, meaning theydo not always produce the same output for the same input. This makesensuring safety more difficult, as the system's behavior can change based on new data, environmental factors, or updates.
* Why Non-determinism Affects Safety:
* In traditional software, the same input always produces the same output.
* In AI systems, outputsvary probabilisticallydepending on learned patterns and weights.
* This unpredictability makes itharder to verify correctness, reliability, and safety, especially in critical domains likeautonomous vehicles, medical AI, and industrial automation.
* A (Simplicity):AI-based systems are typicallycomplex, not simple, which contributes to safety challenges.
* B (Sustainability):While sustainability is an important AI consideration, it doesnot directly affect safety.
* D (Robustness):Lack of robustnesscan make AI systems unsafe, butnon-determinism is the primary issuethat complicates safety verification.
* ISTQB CT-AI Syllabus (Section 2.8: Safety and AI)
* "The characteristics of AI-based systems that make it more difficult to ensure they are safe include: complexity, non-determinism, probabilistic nature, self-learning, lack of transparency, interpretability and explainability, lack of robustness".
Why Other Options Are Incorrect:Supporting References from ISTQB Certified Tester AI Testing Study Guide:Conclusion:Sincenon-determinism makes AI behavior unpredictable, complicating safety assurance, thecorrect answer is C.
NEW QUESTION # 62
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 is B because hardware supporting fast matrix multiplication, such as GPUs, is most suitable for the parallel processing requirements of machine learning.
NEW QUESTION # 63
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