BCS Foundation Certificate in Artificial Intelligence Interview Questions
To assist you in passing your BCS Foundation Certificate in Artificial Intelligence interview, we have created a list of Artificial Intelligence interview questions. In this blog, we’ve covered topics like AI programming languages and applications, Turing test, expert systems, details of various search algorithms, game theory, fuzzy logic, inductive, deductive, and abductive Machine Learning, ML algorithm techniques, Nave Bayes, Perceptron, KNN, LSTM, autoencoder, and much more.
1. Describe the Alpha–Beta pruning process in BCS Foundation Certificate in Artificial Intelligence.
Alpha–Beta pruning is a search algorithm that seeks to limit the number of nodes in the search tree searched by the minimax algorithm. It can trim entire subtrees and leaves and can be applied to n depths.
2. What is fuzzy logic, and how does it work?
Fuzzy logic is a subset of artificial intelligence; it is a method of encoding human learning for machine processing. It’s a type of multiple-valued logic. IF-THEN rules are use to represent it.
3. Make a list of fuzzy logic’s applications.
- Recognition of facial patterns
- Air conditioners, washing machines, and vacuum cleaners are all examples of household appliances.
- Transmission and anti-skid braking systems
- Subway systems and unmanned helicopters are under control.
- Forecasting systems for the weather
- Risk assessment for the project
- Plans for medical diagnosis and treatment
- Investing in stocks
4. What is partial-order planning, and what does it entail?
To achieve the aim, a problem must be solve sequentially. The partial-order plan lays out all of the steps that must be follow, but only when they must be done in a specific order.
5. What exactly is FOPL?
A collection of formal systems in which each statement is separate into a subject and a predicate is known as first-order predicate logic. The predicate refers to only one subject and can modify or specify the subject’s attributes.
6. What is the meaning of Naive Bayes?
The Naive Bayes Machine Learning method is a sophisticated predictive modelling tool. It is a collection of algorithms based on the Bayes Theorem that share a similar principle. The basic Naive Bayes assumption is that each feature contributes equally and independently to the outcome.
7. What is a Backpropagation Algorithm?
Backpropagation is a Neural Network algorithm that is primarily use to analyze noisy data and find unrecognized patterns in order to gain a better understanding of the situation. It’s a full-state algorithm with an iterative component. Backpropagation is an ANN algorithm with three layers: input, hidden, and output.
The user or the outside environment provides input values and limitations to the input layers. The data is then sent to the Hidden layer, where it is processed. Finally, utilising the output layer, the processed data is turn into certain values or patterns that may be share.
8. How are route weights optimised to lower the model’s error?
In AI, weights define how much influence the input has on the output. Weights are use in neural networks to process input and train the model. The output should have the same properties as the target attributes. However, the output may contain inaccuracies that must be corrected to produce the accurate output. When there is an error in the output of the Backpropagation method, for example, the algorithm will backpropagate to the hidden layer and reroute the weights to produce an optimum output.
9. In Machine Learning, what is regularisation?
When a model is either overfit or underfit, regularisation comes into play. Its main purpose is to reduce the amount of error in a dataset. To avoid problems with fitting, a new piece of data is added to the dataset.
10. What is the difference between model accuracy and model performance?
Model accuracy is a subset of model performance that is based on an algorithm’s model performance. Model performance, on the other hand, is determined by the datasets we give into the algorithm as inputs.
11. What is the definition of a recommendation system?
A recommendation system is a data filtering system that predicts user preferences based on the user’s choosing patterns while browsing or using the system.
12. What techniques are employed to reduce dimensionality?
The reduction of the number of random variables is known as dimensionality reduction. Missing values ratio, low variance filter, high correlation filter, random forest, principal component analysis, and other approaches can be use to reduce dimensionality.
13. Make a list of different sequential supervised learning approaches.
- Make a list of different sequential supervised learning approaches.
- Methods for sliding windows
- Methods for recurring sliding windows
- Models of hidden Markov chains
- Markov models with maximum entropy
- Random fields with conditions
- Create a graph of transformer networks.
14. What is TensorFlow, and how does it work?
TensorFlow is a Machine Learning library that is open-source. It is a low-level toolset for performing sophisticate algorithms that allow users to customise experimental learning architectures and work on them to get desired results.
15. What is the definition of a cost function?
A cost function is a scalar function that measures the neural network’s error factor. The neural network performs better when the cost function is lower. The input picture in the MNIST dataset, for example, is digit 2, but the neural network incorrectly predicts it to be 3.
16. What are the ANN hyperparameters?
- The learning rate refers to how quickly the network learns its parameters.
- Momentum is a parameter that aids in breaking free from local minima and smoothing out jumps during gradient descent.
- The number of epochs refers to the number of times the whole training data is supplied to the network during the training process. Even if the training accuracy is improving, we increase the number of epochs until the validation accuracy starts to decline (overfitting).
17. What is a vanishing gradient, and how does it work?
Backpropagation becomes less useful in transferring information to the lower layers as we add more buried layers. As information is transmit back, the gradients begin to fade and become modest in comparison to the network’s weights.
18. What are the consequences of dropping out?
Dropout is a basic method of preventing overfitting in a neural network. It’s when some of the units in a neural network stop working. It’s analogous to natural reproduction, in which nature creates offspring by combining different genes (while leaving others out) rather than boosting their co-adaptation.
19. What exactly do you mean when you say TensorFlow cluster in BCS Foundation Certificate in Artificial Intelligence?
A TensorFlow cluster is a collection of ‘tasks’ that work together to execute a TensorFlow graph in a distributed fashion. Each task is associate with a TensorFlow server, which includes a’master’ for creating sessions and a ‘worker’ for executing graph operations. A cluster can also be broken down into one or more ‘jobs,’ each of which has one or more tasks.
20. Intermediate tensors are what they sound like. Do sessions last indefinitely?
The intermediate tensors are tensors that are neither inputs nor outputs of the Session.run() call, but are in the path between the inputs and outputs; they will be freed at or before the call’s end. Sessions can have their own resources, such as tf.Variable, tf.QueueBase, and tf.ReaderBase, and they consume a lot of memory. When the session is stopped with tf.Session.close, these resources (and the related memory) are relinquish.
21. In neural networks, how is overfitting avoided?
In neural nets, overfitting is minimize by employing a regularisation approach known as ‘dropout.’ When a neural network is being train to utilize a model that does not overfit, random neurons are discard using the idea of dropouts. If the dropout value is too low, the effect will be minor. If it is set too high, the model will struggle to learn.
22. Describe the process of Deep Learning in BCS Foundation Certificate in Artificial Intelligence.
- Deep Learning is based on a brain cell, also known as a neuron, which is the most basic element of the brain. An artificial neuron, also known as a perceptron, was create after being inspired by a neuron.
- Dendrites are the parts of a biological neuron that receive input.
- A perceptron, on the other hand, takes many inputs, applies various transformations and functions, and then outputs.
- A Deep neural network comprises a network of artificial neurons called perceptrons, similar to how our brain has several connected neurons called neural networks.
23. In Deep Neural Networks, what are hyperparameters in BCS Foundation Certificate in Artificial Intelligence?
- The structure of the network is define by hyperparameters, which are variables. Variables like the learning rate, for example, determine how the network is train.
- They specify the minimum number of hidden layers required in a network.
- More hidden units can improve the network’s accuracy, whereas fewer units can lead to underfitting.
24. What do Deep Learning frameworks like Keras, TensorFlow, and PyTorch serve?
- Keras is a Python-based open-source neural network library.
- TensorFlow is an open-source dataflow programming software package. It’s utilize in neural networks and other machine learning applications.
- PyTorch is a Torch-based open-source machine learning package for Python. It’s utilize in a variety of applications, including natural language processing.
25. What is the difference between stemming and lemmatization in NLP?
Stemming algorithms function by chopping off the end or beginning of a word while considering a list of often found prefixes and suffixes in inflected words. On some occasions, indiscriminate cutting might be successful, but not always. On the other hand, lemmatization considers the morphological examination of the words. To do so, detailed dictionaries must be available for the algorithm to search through in order to link the form back to its lemma.
26. Which method is more effective for image classification? Is categorization supervise or unsupervise? Justify.
- The photos are manually supplied and evaluate by the Machine Learning specialist to construct feature classes in supervise classification.
- Machine Learning software builds feature classes based on picture pixel values in unsupervised categorization.
- In terms of accuracy, it is thus preferable to use supervised classification for picture classification.
27. What is the relationship between computer vision and artificial intelligence?
Computer Vision is a branch of Artificial Intelligence that uses images or multi-dimensional data to extract information. Machine Learning techniques such as K-means, Support Vector Machine, and others are use for image segmentation, image classification, etc. As a result, Computer Vision employs AI to handle complicated issues such as Object Detection, Image Processing, and so on.
28. Explain what “Q-Learning” means in BCS Foundation Certificate in Artificial Intelligence.
Q-learning is a prominent reinforcement learning algorithm. The Bellman equation is at the heart of it. The agent in this algorithm seeks to learn policies that can provide the optimum behaviours to take in order to maximize rewards under specific scenarios. From previous encounters, the agent learns the best policies.
29. What is the Markov decision-making process in BCS Foundation Certificate in Artificial Intelligence?
The Markov decision process, or MDP, can be use to solve a reinforcement learning problem. As a result, the RL problem is formalize using MDP. It is a mathematical method for resolving a reinforcement learning problem. The fundamental goal of this procedure is to maximize positive incentives by selecting the best policy.
30. What exactly do you mean when you say “reward maximisation” in BCS Foundation Certificate in Artificial Intelligence?
The word “reward maximisation” is use in reinforcement learning, and it refers to the reinforcement learning agent’s purpose. In real life, a reward is a good response to taking action to move from one state to another. If the agent takes a good action by following optimal policies, he is reward, and if he takes a negative action, he is penalize. The agent’s purpose is to maximise these benefits by implementing the best regulations possible, a process known as reward maximization.
