AI Search Algorithms Interview Questions – Part 5
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01. What is a Constraint Satisfaction Problem (CSP)?
A Constraint Satisfaction Problem is an AI problem in which values are assigned to variables while satisfying a set of predefined constraints.
Key Points
- Variables and Domains
- Constraints between Variables
- Used in Sudoku and Timetable Scheduling
02. What is Backtracking Search?
Backtracking Search is an algorithm that builds a solution step by step. If a choice violates a constraint, it goes back and tries another option.
Key Points
- Explores possible solutions
- Rejects invalid choices
- Commonly used in CSPs
03. What is Arc Consistency in AI?
Arc Consistency is a technique used in Constraint Satisfaction Problems to remove domain values that cannot satisfy constraints between connected variables.
Key Points
- Reduces possible values
- Helps eliminate inconsistent choices
- Simplifies the search process
04. What is Forward Checking?
Forward Checking checks the domains of unassigned variables after a variable receives a value. It removes values that conflict with the current assignment.
Key Points
- Detects conflicts early
- Reduces unnecessary searching
- Commonly used with Backtracking Search
05. What is Constraint Propagation?
Constraint Propagation reduces the possible values of variables by repeatedly applying constraints until no further reduction is possible or a contradiction is found.
Key Points
- Reduces the search space
- Helps identify contradictions
- Used in Sudoku and scheduling problems
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01. What is a Constraint Satisfaction Problem (CSP)?
A Constraint Satisfaction Problem is an AI problem in which values are assigned to variables while satisfying a set of predefined constraints.
Key Points
- Variables and Domains
- Constraints between Variables
- Used in Sudoku and Timetable Scheduling
02. What is Backtracking Search?
Backtracking Search is an algorithm that builds a solution step by step. If a choice violates a constraint, it goes back and tries another option.
Key Points
- Explores possible solutions
- Rejects invalid choices
- Commonly used in CSPs
03. What is Arc Consistency in AI?
Arc Consistency is a technique used in Constraint Satisfaction Problems to remove domain values that cannot satisfy constraints between connected variables.
Key Points
- Reduces possible values
- Helps eliminate inconsistent choices
- Simplifies the search process
04. What is Forward Checking?
Forward Checking checks the domains of unassigned variables after a variable receives a value. It removes values that conflict with the current assignment.
Key Points
- Detects conflicts early
- Reduces unnecessary searching
- Commonly used with Backtracking Search
05. What is Constraint Propagation?
Constraint Propagation reduces the possible values of variables by repeatedly applying constraints until no further reduction is possible or a contradiction is found.
Key Points
- Reduces the search space
- Helps identify contradictions
- Used in Sudoku and scheduling problems
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Generative AI Interview Questions – Part 11
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01. What is Prompt Injection in AI?
Prompt Injection is an attack in which malicious input attempts to manipulate an AI model into ignoring its intended instructions or performing unintended actions.
Key Points
- Can target user prompts or external content
- May affect AI agents using tools
- Requires appropriate security controls
02. What is Jailbreaking in AI?
Jailbreaking refers to attempts to bypass an AI system's safety restrictions through specially crafted prompts or instructions.
Key Points
- Attempts to bypass model safeguards
- Can produce policy-violating outputs
- Tested during AI security evaluations
03. What is AI Red Teaming?
AI Red Teaming is the process of testing an AI system to identify security weaknesses, harmful outputs, privacy risks, and other potential failures.
Key Points
- Tests model vulnerabilities
- Evaluates misuse and security risks
- Helps improve system safeguards
04. What is Function Calling in LLMs?
Function Calling allows a Large Language Model to request a predefined function using structured arguments. The application executes the function and can return the result to the model.
Key Points
- Uses structured arguments
- Connects models with external functions
- Supports database queries and API operations
05. What is Tool Use in AI Agents?
Tool Use allows AI agents to interact with external systems such as APIs, databases, search engines, and calculators to complete tasks.
Key Points
- Extends agent capabilities
- Supports external information retrieval
- Requires suitable permissions and validation
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01. What is Prompt Injection in AI?
Prompt Injection is an attack in which malicious input attempts to manipulate an AI model into ignoring its intended instructions or performing unintended actions.
Key Points
- Can target user prompts or external content
- May affect AI agents using tools
- Requires appropriate security controls
02. What is Jailbreaking in AI?
Jailbreaking refers to attempts to bypass an AI system's safety restrictions through specially crafted prompts or instructions.
Key Points
- Attempts to bypass model safeguards
- Can produce policy-violating outputs
- Tested during AI security evaluations
03. What is AI Red Teaming?
AI Red Teaming is the process of testing an AI system to identify security weaknesses, harmful outputs, privacy risks, and other potential failures.
Key Points
- Tests model vulnerabilities
- Evaluates misuse and security risks
- Helps improve system safeguards
04. What is Function Calling in LLMs?
Function Calling allows a Large Language Model to request a predefined function using structured arguments. The application executes the function and can return the result to the model.
Key Points
- Uses structured arguments
- Connects models with external functions
- Supports database queries and API operations
05. What is Tool Use in AI Agents?
Tool Use allows AI agents to interact with external systems such as APIs, databases, search engines, and calculators to complete tasks.
Key Points
- Extends agent capabilities
- Supports external information retrieval
- Requires suitable permissions and validation
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Advanced AI Interview Questions
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01. What is the Attention Mechanism in AI?
Attention is a mechanism that allows a neural network to focus on the most relevant parts of the input while processing information.
Key Points
- Assigns importance to different input elements
- Used in Transformer architectures
- Helps LLMs understand relationships between tokens
02. What is Self-Attention?
Self-Attention calculates relationships between tokens within the same sequence using Query, Key, and Value representations.
Example:
In the sentence "The animal didn't cross the road because it was tired," self-attention can help the model relate "it" to "animal."
03. What is Retrieval-Augmented Generation (RAG)?
RAG combines information retrieval with text generation. It retrieves relevant documents and supplies them as context to an AI model before generating an answer.
Key Points
- Connects LLMs with external knowledge
- Can reduce unsupported answers when retrieval is relevant
- Used in document chatbots and knowledge assistants
04. What is the Difference Between RAG and Fine-Tuning?
RAG supplies external information during inference, while fine-tuning updates a model's parameters using training examples.
Key Points
- RAG is useful for frequently changing knowledge
- Fine-tuning can adapt behavior and output formats
- Both approaches can be combined
05. What is an AI Agent?
An AI Agent is a system that uses a model to interpret a goal, decide on actions, and interact with tools or external systems to complete tasks.
Example Workflow
User Request → Planning → Tool Call → Result Analysis → Final Response
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01. What is the Attention Mechanism in AI?
Attention is a mechanism that allows a neural network to focus on the most relevant parts of the input while processing information.
Key Points
- Assigns importance to different input elements
- Used in Transformer architectures
- Helps LLMs understand relationships between tokens
02. What is Self-Attention?
Self-Attention calculates relationships between tokens within the same sequence using Query, Key, and Value representations.
Example:
In the sentence "The animal didn't cross the road because it was tired," self-attention can help the model relate "it" to "animal."
03. What is Retrieval-Augmented Generation (RAG)?
RAG combines information retrieval with text generation. It retrieves relevant documents and supplies them as context to an AI model before generating an answer.
Key Points
- Connects LLMs with external knowledge
- Can reduce unsupported answers when retrieval is relevant
- Used in document chatbots and knowledge assistants
04. What is the Difference Between RAG and Fine-Tuning?
RAG supplies external information during inference, while fine-tuning updates a model's parameters using training examples.
Key Points
- RAG is useful for frequently changing knowledge
- Fine-tuning can adapt behavior and output formats
- Both approaches can be combined
05. What is an AI Agent?
An AI Agent is a system that uses a model to interpret a goal, decide on actions, and interact with tools or external systems to complete tasks.
Example Workflow
User Request → Planning → Tool Call → Result Analysis → Final Response
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