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If you’re getting ready for an AI related job interview, this page will help you. We’ve gathered common AI questions that employers often ask, paired with simple, clear answers to help candidates practice for their interviews. As our experts have curated these answers, you can be assured of the reliability of the content.
You’ll find topics like AI Fundamentals, Machine Learning Algorithms, Deep Learning, Natural Language Processing, and more. Whether you’re just starting out or want to practice advanced AI concepts, this page is perfect for refreshing and updating your knowledge.
The page contains questions most commonly asked in a real interview, so you can practice and feel prepared. Check out the questions now and enhance your AI knowledge to crack your next AI interview.
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gradient_start_position=”0″ gradient_end_position=”100″ gradient_type=”linear” radial_direction=”center center” linear_angle=”180″ background_color=”” background_image=”” background_position=”left top” background_repeat=”no-repeat” background_blend_mode=”none” render_logics=”” filter_type=”regular” filter_hue=”0″ filter_saturation=”100″ filter_brightness=”100″ filter_contrast=”100″ filter_invert=”0″ filter_sepia=”0″ filter_opacity=”100″ filter_blur=”0″ filter_hue_hover=”0″ filter_saturation_hover=”100″ filter_brightness_hover=”100″ filter_contrast_hover=”100″ filter_invert_hover=”0″ filter_sepia_hover=”0″ filter_opacity_hover=”100″ filter_blur_hover=”0″ animation_type=”” animation_direction=”left” animation_speed=”0.3″ animation_offset=”” last=”true” border_position=”all” first=”true”][fusion_accordion type=”accordions” boxed_mode=”no” border_size=”1″ border_color=”” background_color=”” hover_color=”” divider_line=”” title_font_size=”” icon_size=”” icon_color=”” icon_boxed_mode=”” icon_box_color=”#4f4f4f” icon_alignment=”right” toggle_hover_accent_color=”#3ed9df” hide_on_mobile=”small-visibility,medium-visibility,large-visibility” class=”” id=””][fusion_toggle title=”Question 81: Enumerate the main components of LSTM.” open=”no” class=”” id=””]
Answer:
- Gates (including forget, memory, update, and read gates)
- Tanh activation function (producing values between -1 and 1)
- Sigmoid activation function (producing values between 0 and 1)
[/fusion_toggle][fusion_toggle title=”Question 82: List the variations of RNN.” open=”no” class=”” id=””]
Answer:
- LSTM (Long Short-term Memory)
- GRU (Gated Recurrent Unit)
- End-to-end Network
- Memory Network
[/fusion_toggle][fusion_toggle title=”Question 83: What is the definition of an autoencoder? Provide a few examples of its applications” open=”no” class=”” id=””]
Answer:
An autoencoder is a neural network type that learns to encode and decode data, typically aiming to reconstruct the original input. Some applications of autoencoders include:
- Data denoising
- Dimensionality reduction
- Image reconstruction
- Image colorization
[/fusion_toggle][fusion_toggle title=”Question 84: What are the components of a generative adversarial network (GAN)? How is it deployed?” open=”no” class=”” id=””]
Answer:
Components of GAN:
- Generator
- Discriminator
Deployment steps:
- Train the model
- Validate and finalize the model
- Save the model
- Load the saved model for future predictions
[/fusion_toggle][fusion_toggle title=”Question 85: What does ”TensorFlow cluster” refer to?” open=”no” class=”” id=””]
Answer:
A TensorFlow cluster consists of multiple “tasks” that collaborate in executing a distributed TensorFlow graph. Each task is linked with a TensorFlow server, which contains a “master” used for creating sessions and a “worker” responsible for executing graph operations. A cluster can be divided into one or more “jobs,” with each job containing one or more tasks.
[/fusion_toggle][fusion_toggle title=”Question 86: List the extraction techniques utilized for dimensionality reduction.” open=”no” class=”” id=””]
Answer:
- Independent component analysis
- Principal component analysis
- Kernel-based principal component analysis
[/fusion_toggle][fusion_toggle title=”Question 87: What are intermediate tensors? Do sessions have a lifespan?” open=”no” class=”” id=””]
Answer:
Intermediate tensors are tensors that are not direct inputs or outputs of the Session.run() call but are involved in the computation between the inputs and outputs. They will be freed at or before the end of the call.
Sessions can own resources, such as tf.Variable, tf.QueueBase, and tf.ReaderBase, which consume memory. When the session is closed by invoking tf.Session.close(), these resources and the associated memory are released.
[/fusion_toggle][fusion_toggle title=”Question 88: What is the lifespan of a variable?” open=”no” class=”” id=””]
Answer:
When the tf.Variable.initializer operation for a variable is run in a session for the first time, its lifespan begins. It is destroyed when the tf.Session.close operation is executed.
[/fusion_toggle][fusion_toggle title=”Question 89: Can logical inference be solved in propositional logic?” open=”no” class=”” id=””]
Answer:
Yes, logical inference can be solved in propositional logic by utilizing concepts such as logical equivalence, process satisfaction, and validation checking.
[/fusion_toggle][fusion_toggle title=”Question 90: What are some commonly used algorithms for hyperparameter optimization?” open=”no” class=”” id=””]
Answer:
There are several algorithms employed for hyperparameter optimization, but three widely used ones are:
- Bayesian optimization
- Grid search
- Random search
[/fusion_toggle][fusion_toggle title=”Question 91: Enumerate the stages of AI learning.” open=”no” class=”” id=””]
Answer:
The stages of AI learning include the following:
- Artificial General Intelligence (AGI): Also known as Strong AI, AGI refers to machines that possess human-like thinking and decision-making abilities, raising concerns about their impact on human existence.
- Artificial Normal Intelligence (ANI): Also known as Weak AI, ANI refers to AI systems that can perform specific tasks or activities but lack the broader cognitive abilities of humans.
- Artificial Super Intelligence (ASI): ASI represents AI systems that surpass human capabilities and can perform any task that a human can. Examples include advanced humanoid robots like Alpha 2.
[/fusion_toggle][fusion_toggle title=”Question 92: Describe Markov’s Decision Processes (MDPs) in the context of Artificial Intelligence.” open=”no” class=”” id=””]
Answer:
Markov’s Decision Processes (MDPs) are a mathematical framework used to address sequential decision problems under uncertainty and in the field of reinforcement learning. The objective of MDPs is to determine the optimal policy that maximizes the cumulative reward gained by an agent.
[/fusion_toggle][fusion_toggle title=”Question 93: Name the elements comprising a Markov Decision Process (MDP).” open=”no” class=”” id=””]
Answer:
The key elements of a Markov Decision Process (MDP) are as follows:
- A set of finite states (S)
- A set of finite actions (A)
- Rewards
- Policy (Pa)
In an MDP, an agent takes actions (A) to transition from an initial state to an end state, receiving rewards along the way. The agent’s sequence of actions can be defined as a policy (Pa).
[/fusion_toggle][fusion_toggle title=”Question 94: Explain the concept of K-means clustering.” open=”no” class=”” id=””]
Answer:
K-means clustering is an unsupervised learning algorithm widely used to identify clusters within a dataset. It aims to group data objects based on similarity without any prior labeled data. The algorithm iteratively assigns data points to clusters and updates the cluster centroids until convergence. Once the clustering is complete, new data points can be assigned to the most appropriate cluster.
[/fusion_toggle][fusion_toggle title=”Question 95: Provide real-world examples of K-means clustering.” open=”no” class=”” id=””]
Answer:
K-means clustering finds applications in various domains, including:
- Astronomy: Identifying star clusters or galaxies.
- Search engines: Grouping similar web pages or search results.
- Computer vision: Image segmentation and object recognition.
- Customer profiling: Segmenting customers based on their purchasing behavior.
- Market segmentation: Identifying distinct customer segments based on demographic or behavioral data.
[/fusion_toggle][fusion_toggle title=”Question 96: Which approach is best suited for game-playing problems?” open=”no” class=”” id=””]
Answer:
The heuristic approach is often the best choice for game-playing problems. It involves employing intelligent guesswork to make decisions. For example, in chess matches between humans and computers, brute force computation is combined with heuristics to evaluate hundreds of thousands of positions.
[/fusion_toggle][fusion_toggle title=”Question 97: Differentiate between breadth-first search and best-first search in artificial intelligence.” open=”no” class=”” id=””]
Answer:
Breadth-first search and best-first search are two search strategies used in AI:
- Breadth-first search explores nodes at the current depth level before moving to the next level, ensuring a complete exploration of each depth level.
- Best-first search selects the most promising node for expansion based on an evaluation function, typically considering heuristics. It does not guarantee complete exploration of depth levels but focuses on the most promising path.
[/fusion_toggle][fusion_toggle title=”Question 98: Define frames and scripts in the context of ”Artificial Intelligence.”” open=”no” class=”” id=””]
Answer:
In “Artificial Intelligence,” frames and scripts are knowledge representation structures:
- Frames are a variant of semantic networks and organize non-procedural knowledge in an expert system. They divide knowledge into substructures and represent stereotyped situations.
- Scripts are similar to frames but emphasize the ordered values that fill the slots. They are used in natural language understanding systems to organize a knowledge base around situational understanding.
[/fusion_toggle][fusion_toggle title=”Question 99: In top-down inductive learning methods, how many literals are available, and what are they?” open=”no” class=”” id=””]
Answer:
In top-down inductive learning methods, three types of literals are available:
- Predicates: Used to express relationships or properties.
- Equality and Inequality: Used to compare values or variables.
- Arithmetic Literals: Used to perform mathematical operations or comparisons.
[/fusion_toggle][fusion_toggle title=”Question 100: Name different platforms for Artificial Intelligence (AI) development.” open=”no” class=”” id=””]
Answer:
Various software platforms for AI development include:
- Amazon AI services
- TensorFlow
- Google AI services
- Microsoft Azure AI platform
- Infosys Nia
- IBM Watson
- H2O
- Polyaxon
- PredictionI
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