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AI Coding MCQs (Multiple-Choice Questions)
These AI Coding multiple-choice questions cover fundamental and advanced concepts involved in using artificial intelligence to assist with software development. The questions explore topics such as AI code generation, code completion, prompt engineering, debugging, refactoring, code review, testing, documentation, repository context, coding agents, tool use, security, code quality, and AI-assisted software development workflows.
AI Coding MCQs
These AI Coding MCQs are useful for students, developers, software engineers, AI professionals, and anyone preparing for technical interviews or looking to understand how AI can be used to write, analyze, test, debug, and maintain software.
List of AI Coding MCQs
Below is a list of 50 AI Coding multiple-choice questions with answers and explanations.
1. What is AI coding?
- Using artificial intelligence to assist with software development tasks
- Using only traditional compilers
- Managing computer hardware manually
- Designing database schemas without software
Answer: A) Using artificial intelligence to assist with software development tasks
Explanation:
AI coding refers to using AI systems to assist with tasks such as generating code, explaining code, debugging, testing, refactoring, documentation, and software development automation.
2. What is AI code generation?
- Generating source code from natural-language instructions or other context
- Compiling source code into machine code only
- Manually typing every line of a program
- Deleting unused source files
Answer: A) Generating source code from natural-language instructions or other context
Explanation:
AI code generation uses machine learning models to produce source code based on prompts, existing code, repository context, or other inputs.
3. Which type of AI model is commonly used for modern AI coding assistants?
- Large Language Model
- Relational database
- Operating system kernel
- Compression algorithm
Answer: A) Large Language Model
Explanation:
Large Language Models (LLMs) are commonly used for AI coding because they can process natural language and source code and generate contextually relevant code and explanations.
4. What is code completion in an AI coding assistant?
- Predicting or suggesting code based on the current coding context
- Compiling a program automatically
- Deleting incomplete functions
- Installing an operating system
Answer: A) Predicting or suggesting code based on the current coding context
Explanation:
AI code completion predicts useful code based on surrounding source code, comments, function signatures, and other available context.
5. What is the main purpose of a coding prompt?
- To describe the task and constraints the AI should follow
- To replace the programming language compiler
- To increase CPU clock speed
- To delete project dependencies
Answer: A) To describe the task and constraints the AI should follow
Explanation:
A coding prompt communicates the developer's intent to the AI system. A clear prompt can specify the required behavior, language, framework, constraints, inputs, outputs, and acceptance criteria.
6. Which prompt is generally more useful for generating a specific function?
- Write code
- Make something good
- Write a Python function that accepts a list of integers and returns the second-largest unique value
- Code this
Answer: C) Write a Python function that accepts a list of integers and returns the second-largest unique value
Explanation:
The third prompt provides a programming language, input type, and expected behavior, giving the AI clearer requirements to follow.
7. Why is providing existing code as context useful to an AI coding assistant?
- It helps the model understand the existing implementation and coding conventions
- It automatically guarantees correct output
- It eliminates the need for testing
- It prevents all security vulnerabilities
Answer: A) It helps the model understand the existing implementation and coding conventions
Explanation:
Existing code provides context about interfaces, naming conventions, dependencies, data structures, and architectural patterns that can improve the relevance of generated suggestions.
8. What is repository context in AI-assisted coding?
- Information from relevant project files, structure, code, configuration, and documentation
- Only the name of the repository
- The developer's computer wallpaper
- The operating system version only
Answer: A) Information from relevant project files, structure, code, configuration, and documentation
Explanation:
Repository context helps an AI coding system understand how a project is organized and how existing components interact, which can improve generated changes.
9. What is an AI coding assistant primarily intended to do?
- Assist developers with software development tasks
- Replace the programming language runtime
- Replace all software testing
- Act as a physical computer processor
Answer: A) Assist developers with software development tasks
Explanation:
AI coding assistants support developers with tasks such as writing, explaining, debugging, testing, refactoring, and reviewing code. They do not eliminate the need for developer review.
10. What is AI-assisted debugging?
- Using AI to analyze errors, code, logs, and context to help identify or fix problems
- Deleting all error messages
- Reinstalling the operating system
- Changing the programming language automatically
Answer: A) Using AI to analyze errors, code, logs, and context to help identify or fix problems
Explanation:
AI-assisted debugging can analyze error messages, stack traces, source code, and related context to suggest possible causes and fixes.
11. What should a developer do before accepting an AI-generated bug fix?
- Review and test the proposed change
- Deploy it immediately
- Delete the test suite
- Assume it is correct because it compiles
Answer: A) Review and test the proposed change
Explanation:
AI-generated code can contain logical, security, compatibility, or performance problems. Developers should review and validate changes before using them in production.
12. What is AI-assisted refactoring?
- Using AI to restructure existing code while preserving or improving intended behavior
- Deleting all source code
- Changing only file names
- Converting every program into machine code
Answer: A) Using AI to restructure existing code while preserving or improving intended behavior
Explanation:
AI-assisted refactoring can help simplify code, improve structure, extract functions, reduce duplication, or modernize implementations while preserving required behavior.
13. What is code review in AI-assisted development?
- Examining code changes for correctness, quality, maintainability, and security
- Generating random code
- Compiling code without inspecting it
- Changing the project name
Answer: A) Examining code changes for correctness, quality, maintainability, and security
Explanation:
Code review evaluates proposed changes for issues such as bugs, security vulnerabilities, maintainability problems, and deviations from project conventions.
14. Why should AI-generated code undergo human code review?
- AI-generated code can contain incorrect, insecure, or unsuitable implementations
- AI code can never compile
- Human review is required only for comments
- AI systems cannot generate any source code
Answer: A) AI-generated code can contain incorrect, insecure, or unsuitable implementations
Explanation:
AI coding tools can generate plausible-looking code that does not satisfy all requirements or introduces security and maintenance problems. Human review remains important.
15. What is AI-generated unit testing?
- Using AI to generate test cases or test code for software
- Testing only the computer hardware
- Deleting existing tests
- Compiling tests without executing them
Answer: A) Using AI to generate test cases or test code for software
Explanation:
AI can analyze a function or component and generate unit tests covering expected inputs, outputs, edge cases, and potential failure conditions.
16. Which test case is particularly valuable when asking AI to generate tests for a function?
- An edge case that tests behavior at an important boundary
- A random unrelated input
- A test for an unrelated application
- A test with no expected result
Answer: A) An edge case that tests behavior at an important boundary
Explanation:
Boundary and edge cases can expose bugs that normal inputs do not reveal. AI-generated tests should therefore include meaningful edge cases in addition to common scenarios.
17. What is hallucination in AI coding?
- When an AI produces incorrect or nonexistent code, APIs, libraries, or technical claims
- When a compiler optimizes code
- When a test passes successfully
- When a repository contains documentation
Answer: A) When an AI produces incorrect or nonexistent code, APIs, libraries, or technical claims
Explanation:
AI coding systems can generate plausible but incorrect implementations or refer to APIs that do not exist. Developers should verify important technical details against reliable documentation and actual project behavior.
18. What is a common cause of incorrect AI-generated code?
- Insufficient or ambiguous context
- Using source control
- Writing unit tests
- Using meaningful variable names
Answer: A) Insufficient or ambiguous context
Explanation:
If requirements, constraints, interfaces, or project context are unclear, an AI system may make assumptions that do not match the actual application.
19. What is context window management important for in AI coding?
- Providing the model with relevant information without exceeding its supported context
- Increasing CPU frequency
- Changing source-code indentation automatically
- Removing all project files
Answer: A) Providing the model with relevant information without exceeding its supported context
Explanation:
AI coding tasks can involve large repositories. Selecting relevant files and information helps the model focus on the task while staying within the model's context limits.
20. What is retrieval-augmented generation (RAG) useful for in AI coding?
- Retrieving relevant documentation or project information to provide additional context
- Replacing the programming language compiler
- Increasing hard-disk speed
- Deleting source-code comments
Answer: A) Retrieving relevant documentation or project information to provide additional context
Explanation:
RAG can retrieve relevant documentation, repository information, or other external knowledge and provide it to the model during generation.
21. What is a coding agent?
- An AI system that can perform multi-step software development tasks using tools
- A static syntax highlighter
- A programming language compiler
- A database table
Answer: A) An AI system that can perform multi-step software development tasks using tools
Explanation:
Coding agents can go beyond simple code suggestions by planning tasks, inspecting files, modifying code, running commands or tests, and iterating on changes.
22. What distinguishes an AI coding agent from simple code completion?
- An agent can perform multiple steps toward a development goal
- An agent can only complete one character at a time
- An agent cannot access project files
- An agent only changes font colors
Answer: A) An agent can perform multiple steps toward a development goal
Explanation:
Code completion typically provides local suggestions, while coding agents can work through broader tasks involving planning, file changes, tool execution, testing, and iteration.
23. Which capability allows an AI coding agent to inspect files before modifying them?
- File-reading or repository tools
- CSS styling
- Image compression
- Database indexing
Answer: A) File-reading or repository tools
Explanation:
Repository and file tools provide an agent with access to relevant project information so it can understand existing implementations before making changes.
24. Why might an AI coding agent run tests after modifying code?
- To check whether the changes behave as expected
- To increase the number of source files
- To remove the project's dependencies
- To change the programming language
Answer: A) To check whether the changes behave as expected
Explanation:
Running tests provides feedback about whether a code change satisfies existing expectations and can help an agent identify and correct problems.
25. What is an iterative AI coding workflow?
- Generate or modify code, test it, inspect results, and improve the implementation
- Generate code once and never verify it
- Delete all previous versions after every edit
- Compile without checking the output
Answer: A) Generate or modify code, test it, inspect results, and improve the implementation
Explanation:
Iterative workflows use feedback from tests, errors, reviews, and requirements to progressively improve the implementation.
26. What is the purpose of a system or repository coding instruction?
- To provide persistent project-specific rules and conventions for AI-assisted work
- To increase processor speed
- To replace the compiler
- To remove source-control history
Answer: A) To provide persistent project-specific rules and conventions for AI-assisted work
Explanation:
Coding instructions can specify conventions such as preferred frameworks, testing requirements, naming rules, architecture constraints, and formatting expectations.
27. Which instruction is most useful when asking an AI to modify an existing API?
- Preserve backward compatibility with existing callers unless explicitly required otherwise
- Change every function name
- Remove all tests
- Rewrite the entire project unnecessarily
Answer: A) Preserve backward compatibility with existing callers unless explicitly required otherwise
Explanation:
Explicit constraints help the AI avoid unintended breaking changes and keep the modification aligned with the existing system's requirements.
28. What is AI-assisted documentation generation?
- Using AI to create or improve documentation based on code and project context
- Deleting comments from source files
- Compiling documentation into machine code
- Replacing all API endpoints
Answer: A) Using AI to create or improve documentation based on code and project context
Explanation:
AI can generate function descriptions, README sections, API explanations, usage examples, and other documentation based on available code and project context.
29. What is code summarization?
- Generating a concise explanation of what code does
- Deleting unused variables automatically
- Compressing source code into a ZIP archive
- Changing the programming language
Answer: A) Generating a concise explanation of what code does
Explanation:
Code summarization describes the purpose, behavior, important logic, or structure of source code in natural language.
30. What is one advantage of asking AI to explain unfamiliar code before modifying it?
- It can help the developer understand the existing logic and dependencies
- It guarantees the code is bug-free
- It removes the need for source control
- It automatically approves the change
Answer: A) It can help the developer understand the existing logic and dependencies
Explanation:
An explanation can provide a useful starting point for understanding unfamiliar code, although developers should still verify the explanation against the actual implementation.
31. What is AI-assisted code translation?
- Converting source code from one programming language or framework to another
- Translating a database into an image
- Changing binary files into hardware
- Converting code into a network address
Answer: A) Converting source code from one programming language or framework to another
Explanation:
AI can assist with tasks such as translating Java code to C#, converting Python code to another language, or adapting APIs between frameworks.
32. Why can automatic code translation require manual review?
- Different languages and frameworks can have different semantics, libraries, and conventions
- All programming languages have identical behavior
- Translated code never requires dependencies
- Syntax is the only concern in software development
Answer: A) Different languages and frameworks can have different semantics, libraries, and conventions
Explanation:
A syntactically valid translation may still have incorrect behavior because APIs, concurrency models, error handling, type systems, and framework conventions can differ.
33. What is a common security risk when using AI-generated code?
- The generated code may contain insecure patterns or vulnerabilities
- The code automatically becomes cryptographically secure
- AI-generated code cannot access databases
- Security review becomes unnecessary
Answer: A) The generated code may contain insecure patterns or vulnerabilities
Explanation:
AI-generated code can contain issues such as unsafe input handling, insecure authentication logic, hardcoded secrets, or vulnerable dependencies. Security review and testing remain necessary.
34. Which practice helps prevent secrets from being accidentally included in AI-generated code?
- Use environment variables or a dedicated secret-management system
- Hardcode API keys directly in source files
- Commit passwords to public repositories
- Include production credentials in prompts
Answer: A) Use environment variables or a dedicated secret-management system
Explanation:
Secrets such as API keys and passwords should be managed separately from source code using appropriate environment configuration or secret-management solutions.
35. Why should developers avoid unnecessarily including sensitive information in AI coding prompts?
- It reduces the risk of exposing confidential information
- It makes code compile faster
- It increases CPU performance
- It automatically improves database indexing
Answer: A) It reduces the risk of exposing confidential information
Explanation:
Developers should follow the security and privacy policies applicable to their AI tools and avoid providing unnecessary credentials, personal data, proprietary secrets, or other sensitive information.
36. What is static analysis useful for in AI-assisted coding?
- Finding certain code-quality, bug, and security issues without executing the program normally
- Generating random passwords
- Increasing monitor resolution
- Replacing version control
Answer: A) Finding certain code-quality, bug, and security issues without executing the program normally
Explanation:
Static analysis examines source code or related artifacts to identify patterns associated with bugs, security issues, code smells, or violations of coding rules.
37. What is linting in software development?
- Automatically checking source code against defined style and quality rules
- Running a database backup
- Training a neural network
- Compressing source files
Answer: A) Automatically checking source code against defined style and quality rules
Explanation:
Linters identify issues such as style violations, suspicious constructs, unused variables, or other project-specific problems without requiring the full application to execute.
38. Why is version control particularly important when using AI coding tools?
- It allows developers to review, compare, revert, and track AI-generated changes
- It guarantees all generated code is correct
- It replaces unit tests
- It prevents all AI hallucinations
Answer: A) It allows developers to review, compare, revert, and track AI-generated changes
Explanation:
Version control provides a history of changes and makes it easier to inspect and revert modifications produced with AI assistance.
39. What is a pull request in an AI-assisted development workflow?
- A proposed set of repository changes submitted for review and integration
- A compiler configuration
- A database query
- A programming language feature
Answer: A) A proposed set of repository changes submitted for review and integration
Explanation:
A pull request provides a structured mechanism for reviewing proposed code changes before they are merged into a target branch. AI coding agents can also create or update pull requests as part of development workflows.
40. What is the purpose of CI in an AI-assisted software development workflow?
- Automatically building and testing changes as part of the development process
- Generating passwords manually
- Replacing source control
- Writing every program without developers
Answer: A) Automatically building and testing changes as part of the development process
Explanation:
Continuous Integration (CI) automatically runs processes such as builds, tests, and checks when code changes are submitted, helping identify problems before integration.
41. What is an AI coding agent's tool-calling capability used for?
- Allowing the agent to interact with files, terminals, tests, or other supported development tools
- Changing the physical CPU
- Increasing monitor brightness
- Replacing all source code with natural language
Answer: A) Allowing the agent to interact with files, terminals, tests, or other supported development tools
Explanation:
Tool use allows coding agents to perform actions beyond generating text, such as reading files, editing code, running commands, executing tests, and inspecting results.
42. Why should terminal commands suggested by an AI coding agent be reviewed before execution?
- A command may modify, delete, expose, or otherwise affect files and systems
- Terminal commands can never execute successfully
- AI agents cannot generate commands
- Reviewing commands makes the CPU faster
Answer: A) A command may modify, delete, expose, or otherwise affect files and systems
Explanation:
Commands can have significant side effects. Developers should understand and verify potentially destructive or security-sensitive commands before allowing them to run.
43. What is human-in-the-loop AI coding?
- A workflow where humans review, approve, guide, or intervene in AI-generated development tasks
- A workflow without any human involvement
- A compiler optimization technique
- A method for removing source control
Answer: A) A workflow where humans review, approve, guide, or intervene in AI-generated development tasks
Explanation:
Human-in-the-loop workflows keep developers involved in important decisions, reviews, approvals, and corrections rather than allowing AI output to be accepted without oversight.
44. Which task is generally well suited to AI-assisted coding?
- Generating boilerplate code from clearly defined requirements
- Approving production security changes without review
- Deploying destructive changes without testing
- Exposing production credentials for debugging
Answer: A) Generating boilerplate code from clearly defined requirements
Explanation:
Well-defined and repetitive coding tasks can benefit significantly from AI assistance. Higher-risk changes should still undergo appropriate review and testing.
45. What is a good way to improve the reliability of AI-generated code?
- Combine clear requirements with tests, static analysis, code review, and verification
- Accept every generated suggestion immediately
- Remove all automated tests
- Use increasingly vague prompts
Answer: A) Combine clear requirements with tests, static analysis, code review, and verification
Explanation:
No single technique guarantees correct AI-generated code. Combining clear requirements, automated tests, static checks, human review, and runtime validation provides multiple layers of verification.
46. A developer asks an AI assistant to fix a failing test but provides only the test name and no source code or error output. What is the main problem?
- The AI lacks important context needed to diagnose the failure reliably
- The AI automatically has access to every private repository
- The test cannot be executed by any system
- The programming language becomes irrelevant
Answer: A) The AI lacks important context needed to diagnose the failure reliably
Explanation:
Error messages, stack traces, relevant source code, expected behavior, and environment details can be important for diagnosing a failure. Providing useful context can make the AI's analysis more accurate.
47. A developer wants an AI assistant to refactor a large function without changing its behavior. Which instruction is most useful?
- Refactor the function into smaller logical units, preserve its public behavior, and add or update tests to verify existing behavior
- Rewrite the entire application
- Delete all existing tests
- Change all function signatures
Answer: A) Refactor the function into smaller logical units, preserve its public behavior, and add or update tests to verify existing behavior
Explanation:
The instruction establishes a clear refactoring goal while explicitly preserving behavior and requiring verification through tests.
48. An AI coding agent generates a database query using user-provided input. What should the developer verify carefully?
- Whether the query safely handles untrusted input and avoids injection vulnerabilities
- Whether the variable names are colorful
- Whether the database has enough table names
- Whether the code contains enough comments regardless of security
Answer: A) Whether the query safely handles untrusted input and avoids injection vulnerabilities
Explanation:
AI-generated database code should be reviewed for security issues such as SQL injection. Parameterized queries or the appropriate safe database APIs should be used instead of constructing queries unsafely from untrusted input.
49. An AI coding agent modifies several files to implement a feature. Which workflow provides the strongest verification?
- Review the diff, run relevant tests and linters, inspect the implementation, and verify the feature against its requirements
- Merge immediately because the agent completed the task
- Review only the file names
- Skip testing because generated code is deterministic
Answer: A) Review the diff, run relevant tests and linters, inspect the implementation, and verify the feature against its requirements
Explanation:
Multi-file AI changes should be validated through code review and automated checks, followed by verification against the original requirements. This reduces the risk of accepting unintended or incorrect changes.
50. A development team wants to use an AI coding agent to implement a new feature in an existing production repository. Which workflow is most appropriate?
- Provide clear requirements and repository context, let the agent inspect and plan the change, implement it in a controlled branch or workspace, run tests and static checks, review the generated diff, and require human approval before merging
- Give the agent unrestricted production access and merge every generated change automatically
- Ask the agent to rewrite the complete repository before understanding the existing architecture
- Disable tests and code review to allow the agent to work faster
Answer: A) Provide clear requirements and repository context, let the agent inspect and plan the change, implement it in a controlled branch or workspace, run tests and static checks, review the generated diff, and require human approval before merging
Explanation:
A controlled workflow combines AI assistance with repository context, planning, implementation, automated validation, security checks, and human review. Modern coding-agent workflows can support repository research, code changes, testing, and pull requests, but generated changes should still be reviewed and verified before integration.
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