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
Correct Answer:B
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.
Which ONE of the following options is the MOST APPROPRIATE stage of the ML workflow to set model and algorithm hyperparameters?
SELECT ONE OPTION
Correct Answer:C
Setting model and algorithm hyperparameters is an essential step in the machine learning workflow, primarily occurring during the tuning phase.
✑ Evaluating the model (A): This stage involves assessing the model's performance
using metrics and does not typically include the setting of hyperparameters.
✑ Deploying the model (B): Deployment is the stage where the model is put into production and used in real-world applications. Hyperparameters should already be set before this stage.
✑ Tuning the model (C): This is the correct stage where hyperparameters are set.
Tuning involves adjusting the hyperparameters to optimize the model's performance.
✑ Data testing (D): Data testing involves ensuring the quality and integrity of the data
used for training and testing the model. It does not include setting hyperparameters.
Hence, the most appropriate stage of the ML workflow to set model and algorithm hyperparameters is C. Tuning the model.
References:
✑ ISTQB CT-AI Syllabus Section 3.2 on the ML Workflow outlines the different stages of the ML process, including the tuning phase where hyperparameters are set.
✑ Sample Exam Questions document, Question #31 specifically addresses the stage in the ML workflow where hyperparameters are configured.
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
Correct Answer:B
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.
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
Correct Answer:D
* 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.
"Splendid Healthcare" has started developing a cancer detection system based on ML. The type of cancer they plan on detecting has 2% prevalence rate in the population of a particular geography. It is required that the model performs well for both normal and cancer patients.
Which ONE of the following combinations requires MAXIMIZATION? SELECT ONE OPTION
Correct Answer:C
✑ Prevalence Rate and Model Performance:
✑ Importance of Recall:
✑ Importance of Precision:
✑ Balancing Recall and Precision:
✑ Accuracy and Specificity:
✑ Conclusion:
: This explanation aligns with the principles outlined in the ISTQB CT-AI Syllabus, particularly sections on performance metrics for ML models and handling imbalanced datasets (Chapter 5: ML Functional Performance Metrics).