Free GH-300 practice test: 30 questions with answers

Inline suggestions that appear as the developer types are code completions. Code review, workspace planning and Dependabot updates are separate capabilities.

Copilot Chat provides a conversational interface in the IDE for explanations, questions and edit suggestions. The licensing dashboard manages seats, Dependabot alerts dependencies and Pages hosts sites.

The Copilot coding agent can work on an assigned issue and propose changes in a pull request for review. Inline completions only suggest code while typing, secret scanning detects credentials, and Pages publishes websites.

Suggestions depend on context such as surrounding code and open files, and the model generates probabilistically. Availability, other users' code and themes are not the reasons.

Explicit file references add the file to the context for the chat. Renaming and images do not provide reliable context, and turning off context removes information the answer needs.

Copilot edits and agent mode propose multi-file changes from a natural language request. Spell checkers, file explorers and font changers do not perform code changes.

Reviewing commands for safety and scope prevents unintended changes or data exposure. Approving blindly, using production servers and ignoring output all increase risk.

Inline suggestions and chat for questions and edits are core IDE capabilities. Payroll, calendar sync and mailbox rules are unrelated to Copilot.

Specific prompts with language, inputs, outputs, edge cases and deliverables give Copilot clear targets. Vague prompts produce unpredictable results.

Clear names, comments and type hints give Copilot the intent it needs to suggest correct code. Random input, deleting comments and meaningless names reduce suggestion quality.

The /tests command generates unit tests for the selected code in Copilot Chat. The /explain command explains code, /pr relates to pull requests and /license is not a test generator.

/explain asks Copilot to explain code or error context in plain language. /tests generates tests, /new starts a new project, and /clear resets the conversation.

@workspace provides workspace-wide context so answers can consider code across files. A single-line selection limits context and no reference restricts the search.

Refining the prompt and reviewing alternative suggestions improves results efficiently. Accepting poor suggestions, closing the editor and reporting files are not effective steps.

Relevant context and explicit language or framework details narrow the answer to the code that matters. Broad, detail-free questions and repeated identical prompts do not add information.

Describing behavior and giving an example input and output communicates intent clearly. Unreadable language, emoji-only comments and blank comments give Copilot little to work with.

Suggested dependencies must be vetted like any other dependency, including licensing and security. Merging immediately, installing without review and hiding dependencies all bypass governance.

Human review, tests and code scanning catch insecure or incorrect suggestions before they reach production. Blanket acceptance, disabled review and ignored alerts all remove safeguards.

Duplicate detection and public code filters reduce the chance of matching public code, and licensing review addresses remaining concerns. Assuming free use, hiding names and removing license files are not appropriate controls.

Sensitive data in prompts must be handled under organizational policy, so only appropriate data should be shared. The other statements are incorrect or unsupported.

Tests validate behavior, and reviewing and documenting AI assistance keeps accountability clear. Merging unread code, disabling linting and hiding usage all reduce quality and transparency.

Code scanning and human review catch insecure patterns in generated code, and secret scanning catches exposed credentials. Disabling tools, style-only review and trusting the provider leave vulnerabilities undetected.

Recording where AI assistance was used and keeping review evidence supports audits and accountability. Hiding use, keeping no records and skipping review undermine compliance.

Content exclusion and organization policies control which code and users are covered by Copilot. Stars, logos and profile pictures do not control Copilot access.

Content exclusion rules define files or paths that Copilot should not use for context or suggestions. README entries, renaming and public visibility do not restrict Copilot.

Seat management and usage metrics in the organization settings allow assignment and tracking by team. Release notes, wikis and discussions do not manage seats.

Acceptance and usage metrics with feedback show how Copilot is used and its value. Seat counts alone do not show usefulness, and emojis and logos are irrelevant.

Organization owners manage Copilot feature availability for members through policies that match requirements. Policies do not automatically govern personal accounts, they are managed centrally, and they can be changed.

Organization policies control which Copilot features and model options are available to members. Topics, avatars and discussion categories do not govern feature access.

Content exclusion keeps specified paths out of Copilot context and suggestions. Public exposure, public gists and random renaming do not exclude the code.