1) What do you understand by Artificial Intelligence? List some real-life applications of AI.
A) Artificial Intelligence is a branch of computer science wherein the human brain’s cognitive functions are studied and replicated on a machine or a system. The purpose of AI is to create intelligent machines that can mimic human behavior. It is needed to solve complex problems, automate routine work. Artificial Intelligence is extensively used in various industries, including science and technology, healthcare, telecommunications, energy.
Some of the AI applications are Google’s search engine, computer vision, speech recognition, decision-making, perception, reasoning, cognitive capabilities, classification of email as spam, detection of disease, virtual assistants such as Amazon’s Alexa, Echo, Google Home Speaker.
2) Name some popular programming languages in AI.
A) Python is the most popular programming language for AI because of its modularity. Python offers open-source libraries such as Matplotlib, NumPy, Scikit-learn, and Tensorflow making the workflow efficient.. The other commonly used languages for AI are:
3) What is the difference between Statistical AI and Classical AI?
A) Statistical artificial intelligence focuses on inductive thought given a set of patterns or trends. On the other hand, classical artificial intelligence is based on deductive thought, using a set of constraints, deduce a conclusion.
4) Are AI and ML the same? If yes, how, and if not then why so?
A) No, Artificial intelligence and Machine Learning are not the same. Artificial Intelligence is a field of computer science concerned with building machines that are capable of thinking like humans and performing tasks that typically require human intelligence. AI is a rule-based if-else programming input approach that is static and hardcoded.
M is a subset of AI wherein the machines are not specifically or explicitly programmed to perform certain tasks but rather the machines learn by themselves and improve automatically through experience. In contrast to AI, ML is dynamic where the rules are not predefined and are not known beforehand.
5) What is the difference between Artificial Intelligence, Machine Learning, and Deep Learning?
A) Artificial intelligence is the all-encompassing branch of computer science to build machines that are capable like humans. Example: Robotics
Machine learning is the subset of AI. It is the practice of getting machines to make decisions without being programmed. It aims to build machines learning through data so that they can solve problems. Example: churn prediction, detection of disease, text classification.
Deep Learning is the subset of Machine Learning. It has neural networks that can perform unsupervised learning from unstructured data. They learn through representation learning, and it could be unsupervised, supervised, or semi-supervised. Deep learning aims to build neural networks that automatically discover patterns for feature detection. Example: uncrewed cars and how they can recognize stop signs on the road.
6) What is Machine Learning? What are the types of Machine Learning?
A) A subset of artificial intelligence, Machine learning is the study of algorithms and models, which computers use for performing specific tasks without having particular instructions. It is a framework that takes the past data and identifies the relationships among the features and machines can predict the new data with the help mathematical relationships by getting dynamic, accurate and stable models.
The types of Machine Learning are:
7) Explain Reinforcement Learning. How does it work?
A) Reinforcement Learning, a type of Machine learning algorithm, which is based on the feedback loop where an agent and environment is set up.
The way it works is the agent learns to behave in an environment, by performing certain actions and observing the rewards and results which it gets from those actions. Hence This technique is behavior-driven and is based on the reinforcements learned via trial-and-error. Example: to learn how to ride a bicycle. This method can be used to optimize the operational productivity of systems including supply chain logistics, manufacturing, and robotics.
8) What is the difference between Stemming and Lemmatization?
A) Stemming: Stemming is a rudimentary rule-based process of stripping the suffixes (“ing”, “ly”, “es”, “s” etc) from a word.
Lemmatization: Lemmatization, on the other hand, is an organized & step by step procedure of obtaining the root form of the word. It uses vocabulary (dictionary importance of words) and morphological analysis (word structure and grammar relations).
9) What is the difference between Information Extraction and Information Retrieval?
A) Information retrieval (IR): It is concerned with storing, searching, and retrieving information. It is a separate field within computer science (closer to databases), but IR relies on some NLP methods (stemming). Some current research and applications seek to bridge the gap between IR and NLP.
Information extraction (IE): It is concerned with the extraction of semantic information from text. This covers tasks such as named entity recognition (NER), coreference resolution, relationship extraction.
10) What is an Expert System?
A) An expert system, an Artificial Intelligence program, has expert-level knowledge about a specific area of data and utilizes the information to react appropriately. These systems tend to have the capability to substitute a human expert and solve real-life problems.
11) Explain the term Q-Learning.
A) Q-learning is a popular reinforcement learning algorithm. In this, the agent tries to learn the optimal policies that can provide the best actions to maximize the environment’s rewards. The agent learns these optimal policies from past experiences.
12) What is the Tower of Hanoi?
A) Tower of Hanoi is a mathematical puzzle that shows how recursion is utilized to build an algorithm to solve a specific problem. In artificial intelligence, the Tower of Hanoi can be solved using a decision tree and a breadth-first search (BFS) algorithm.
13) What is the A* algorithm search method?
A) When you want to find the best route between two nodes, you will use an A* algorithm search. Its purpose is to traverse a graph or find a path for this purpose.
A* algorithm, based on the best first search method, is the algorithm used to find the paths or traversing graph. The purpose is to find the most optimal route between the nodes. It is extensively used in solving video games for pathfinding. A* is formulated with weighted graphs, and meaning can find the best route by the shortest distance and time, making the algorithm flexible and versatile.
14) Explain a bidirectional search algorithm. What is it?
A) In a bidirectional search algorithm, two searches are run simultaneously. The first search begins forward from the initial state, and the second goes backward in reverse from the goal state. Both the searches meet to identify a common state, and this way, the search ends. The initial state is linked with the goal state in a reverse manner.
15) How would you explain a uniform cost search algorithm?
A) In a uniform cost search algorithm, the search starts from the initial state and goes to the neighboring state to choose the ‘least costly’ state. It performs in increasing the cost of the path to a node and expands the least cost node. A breadth-first search algorithm will become a uniform cost search algorithm if it has the same cost in every iteration. It investigates ways in the expanding order of the cost.
16) What are iterative deepening depth-first search algorithms?
A) In iterative deepening DFS algorithms, the search process of levels 1 and 2 takes place. The search process continues until the solution is found. It generates nodes until it finds the goal node and saves the stack of nodes it had created.
17) What is fuzzy logic? List its applications.
A) A subset of AI, fuzzy logic is a way of encoding human learning for artificial processing. It is represented as IF-THEN rules and the digital values of YES and NO. It is based on degrees of truth.
Some of its important applications include:
- Facial pattern recognition.
- Air conditioners, washing machines, and vacuum cleaners.
- Anti Skid braking systems and transmission systems.
- Control of subway systems and crewless helicopters.
- Weather forecasting systems.
- Project risk assessment.
- Medical diagnosis and treatment plans.
- Stock marketing prediction and trading.
18) What is partial order planning?
A) In partial-order planning, the search takes place over the space of possible plans rather than searching over all the possible situations. The partial-order plan specifies all actions that need to be undertaken but specifies the actions only when required. The goal is to construct a planned piece by piece.
When a plan specifies all the actions you need to perform but specifies the order of the steps only when necessary, it’s called a partial-order plan.
19) What is FOPL?
A) First-Order Predicate Logic (FOPL) is a collection of formal systems, where each statement is divided into a subject and a predicate. The predicate refers to only one subject, and it can either modify or define the properties of the subject. It provides:
A language to express assertions about certain “World”
An inference system to deductive apparatus whereby we may draw conclusions from such assertion
A semantic based on set theory
20) What does FOPL consist of?
A) FOPL consists of:
- A set of constant symbols
- A set of variables
- A set of predicate symbols
- A set of function symbols
- The logical connective
- The Universal Quantifier and Existential Quantifier
- A special binary relation of equality
21) What is A/B testing in Machine Learning?
A) A statistical way of comparing two (or more) techniques, typically an incumbent against a new rival. A/B testing aims to determine not only which technique performs better but also to understand whether the difference is statistically significant. A/B testing usually considers only two techniques using one measurement, but it can be applied to any finite number of techniques and measures.
22) What is activation function in Machine Learning?
A) A function (for example, ReLU or sigmoid) that takes in the weighted sum of all of the inputs from the previous layer and then generates and passes an output value (typically nonlinear) to the next layer.
23) What is Rectified Linear Unit (ReLU) in Machine learning?
A) An activation function with the following rules:
If input is negative or zero, output is 0.
If input is positive, output is equal to input.
24) What is sigmoid function in Machine learning?
A) A function that maps logistic or multinomial regression output (log odds) to probabilities, returning a value between 0 and 1.
25) What is AdaGrad algorithm in machine learning?
A) A sophisticated gradient descent algorithm that rescales the gradients of each parameter, effectively giving each parameter an independent learning rate.
26) What is AUC (Area under the ROC Curve) in machine learning?
A) An evaluation metric that considers all possible classification thresholds.
27) What is backpropagation in machine learning?
A) The primary algorithm for performing gradient descent on neural networks. First, the output values of each node are calculated (and cached) in a forward pass. Then, the partial derivative of the error with respect to each parameter is calculated in a backward pass through the graph.
The Area Under the ROC curve is the probability that a classifier will be more confident that a randomly chosen positive example is actually positive than that a randomly chosen negative example is positive.
28) What is baseline in machine learning?
A) A simple model or heuristic used as reference point for comparing how well a model is performing. A baseline helps model developers quantify the minimal, expected performance on a particular problem.
29) What is batch in machine learning?
A) The set of examples used in one iteration (that is, one gradient update) of model training.
30) What is model training in machine learning?
A) Model training is the process of determining the best model.
31) What is batch size machine learning?
A) The number of examples in a batch. For example, the batch size of SGD is 1, while the batch size of a mini-batch is usually between 10 and 1000. Batch size is usually fixed during training and inference.
32) What is bias in machine learning?
A) An intercept or offset from an origin. Bias (also known as the bias term) is referred to as b or w0 in machine learning models.
33) What is bias in machine learning?
A) An intercept or offset from an origin. Bias (also known as the bias term) is referred to as b or w0 in machine learning models.
34) What is a binary classification in machine learning?
A) A type of classification task that outputs one of two mutually exclusive classes. For example, a machine learning model that evaluates email messages and outputs either “spam” or “not spam” is a binary classifier.
35) What is bucketing in machine learning?
A) Converting a (usually continuous) feature into multiple binary features called buckets or bins, typically based on value range. For example, instead of representing temperature as a single continuous floating-point feature, you could chop ranges of temperatures into discrete bins. Given temperature data sensitive to a tenth of a degree, all temperatures between 0.0 and 15.0 degrees could be put into one bin, 15.1 to 30.0 degrees could be a second bin, and 30.1 to 50.0 degrees could be a third bin
36) What is calibration layer in machine learning?
A) A post-prediction adjustment, typically to account for prediction bias. The adjusted predictions and probabilities should match the distribution of an observed set of labels.
37) What is candidate sampling in machine learning?
A) A training-time optimization in which a probability is calculated for all the positive labels, using, for example, softmax, but only for a random sample of negative labels. For example, if we have an example labeled beagle and dog candidate sampling computes the predicted probabilities and corresponding loss terms for the beagle and dog class outputs in addition to a random subset of the remaining classes (cat, lollipop, fence).
38) What is checkpoint in machine learning?
A) Data that captures the state of the variables of a model at a particular time. Checkpoints enable exporting model weights, as well as performing training across multiple sessions. Checkpoints also enable training to continue past errors (for example, job preemption). Note that the graph itself is not included in a checkpoint.
39) What is class in machine learning?
A) One of a set of enumerated target values for a label. For example, in a binary classification model that detects spam, the two classes are spam and not spam. In a multi-class classification model that identifies dog breeds, the classes would be poodle, beagle, pug, and so on.
40) What is class-imbalanced data set in machine learning?
A) A binary classification problem in which the labels for the two classes have significantly different frequencies. For example, a disease data set in which 0.0001 of examples have positive labels and 0.9999 have negative labels is a class-imbalanced problem, but a football game predictor in which 0.51 of examples label one team winning and 0.49 label the other team winning is not a class-imbalanced problem.
41) What is classification model in machine learning?
A) A type of machine learning model for distinguishing among two or more discrete classes. For example, a natural language processing classification model could determine whether an input sentence was in French, Spanish, or Italian. Compare with regression model.
42) What is Cloud Computing?
A) Cloud computing can be defined as the computational power which completely resides on the cloud at all times. It is one of the latest innovations in the internet saga domain and primarily uses the Internet i.e. the Cloud, as its chosen method of delivery. The cloud computing service is truly global and contains no border or geographical restrictions as such.
43) What is the cloud?
A) The cloud is a combination of network, hardware, storage, interface, and storage that combines and delivers cloud computing as a service throughout the globe. The cloud computing platform mainly has 2 stakeholders in the process.
44) What are the main features of Cloud Services?
A) Cloud Service and Cloud Computing as a whole also have a multitude of features and advantages associated with it. Mentioned below are the same. The ease to access and manage commercial software from anywhere around the globe.
All the management activities associated with software can and is centralized easily to a central web service.
The ability to design and develop web applications which are capable of handling multiple clients from across the globe at the same time.
Putting an end to the need of downloading software upgrades by centralizing and automating the updating process of all software that is installed on the platform.
45) What and how many types of development models are used in the Cloud?
A) Similar to other complex and latest innovations in the technology industry, the development of cloud computing also calls for the use of a variety of development models. Mentioned is the list of the same.
- Community Cloud
- Private Cloud
- Public Cloud
- Hybrid Cloud
46) Why do organizations need to manage their workload?
A) The workload in an organization can be defined as an independent service of its own and having a set of code which needs to be executed. This workload contains everything starting from data-intensive workload all the way up to transaction and storage processing. All this workload doesn’t depend at all on outside elements.
The main reasons as to why organizations should manage their workload are as follows:
To have an idea about how their applications are running.
To exactly know what functions are taking place.
To get an idea about the charges of an individual department in exchange for using these services.
47) What are the advantages of Cloud Services?
A) The main idea behind the creation of the cloud and publishing cloud services to consumers around the world was because of its greatest advantages. Here are some of them:
The entire infrastructure saves a lot of cost at the consumer’s end since the consumer doesn’t necessarily have to install any kind of infrastructure at their end to use this service. It supports the development of both robust and highly scalable applications. Since its arrival, the whole development and scaling process which earlier used to take months can now be done in a matter of days. Maintenance and deployment of any application on the platform are super easy and efficient which saves a lot of time in the long run.
48) Share your views on Cloud Computing Revolution.
A) Since its inception into the market, cloud computing has created a revolution of sorts in the market. The main idea behind the cloud computing revolution doesn’t lie in the ideology of doing old things a new way, but also to make the entire process cheaper and much more efficient in the long run. With the advancement of cloud computing every day, new doors are being explored and the future of the IT industry does seem very promising.
49) What can be done using Cloud Computing?
A) As Google is a cloud computing platform, Google Cloud interview questions are composed of the general cloud computing questions. So, while going for a Google cloud interview, you need to prepare yourself with the basic cloud computing knowledge.
The potential of what all can be done using cloud computing is far greater than what most people might assume. The fact that the technology is super speedy, added with the fact that the consumer can just start using it without the need to purchase anything, itself are solid factors which point towards a bright future.
For example, the medical and healthcare industry now uses cloud computing on a regular basis to reach and be in touch with their patients at all times. Thus it can be confidently said that the future holds a plethora of opportunities for cloud computing and its patrons.
50) How would you save your applications, software, and drivers for a long term without using any magnetic disk?
A) With the invention and advancement made in the past few years in the Cloud Computing industry, the need for disks or storage devices has become null and void. These days, any data, be it in any format can be easily stored away for a long period of time, just by uploading it on a cloud computing service.
Once uploaded, the data will remain there forever unless the user wants to make some changes or modifications. Although it is a general question on cloud computing you may find this among Google Cloud interview questions and answers.
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