GH-300 Practice Test
GitHub Copilot
GH-300 tests whether you actually understand how to operate GitHub Copilot responsibly and effectively — prompt crafting, data handling, organization-wide policy, and real productivity workflows — not just which button enables which feature.
Our 500 scenario-based questions cover the full GH-300 blueprint across six domains: using GitHub Copilot responsibly (responsible AI principles, validating AI output, risks and limitations of generative AI), using GitHub Copilot features (IDE suggestions and chat, the Copilot CLI, Agent Mode, Copilot Edits, MCP, code review, Spaces, Spark, and organization-wide policy management), understanding GitHub Copilot data and architecture (data flow, input processing, prompt building, proxy filtering, the code suggestion lifecycle, and LLM limitations), applying prompt engineering and context crafting (prompt structure, zero-shot and few-shot prompting, context determination, chat history usage), improving developer productivity (code generation, refactoring, documentation, test generation, edge-case identification, security and performance suggestions), and configuring privacy, content exclusions, and safeguards (public code filtering, content exclusions, output ownership, troubleshooting suggestions). Every question is grounded against live GitHub Docs and Microsoft Learn training content.
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GH-300 Exam Details
What to expect on exam day
Questions
40
Duration
100 minutes
Passing Score
700
Exam Cost
$99
Exam Domains Covered
Master all topics tested on the GH-300 exam
Use GitHub Copilot responsibly
Use GitHub Copilot features
Understand GitHub Copilot data and architecture
Apply prompt engineering and context crafting
Improve developer productivity with GitHub Copilot
Configure privacy, content exclusions, and safeguards
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5 Free GH-300 Questions
See how ready you are for the GH-300 exam. Each question includes a detailed explanation so you learn as you go.
Sample GH-300 Practice Questions
10 real questions from the free GH-300bank. Pick an answer in your head, then open “Show answer” for the explanation.
1. Which responsible AI principle is most directly addressed when a team pseudonymizes personal data and encrypts it both in transit and at rest before it reaches an AI system?
- A. Fairness
- B. Inclusiveness
- C. Privacy and security
- D. Reliability and safety
Show answer
Correct answer: C
Privacy and security calls for anonymizing personal data through techniques like pseudonymization and aggregation, and encrypting sensitive data in transit and at rest. Fairness addresses differential treatment, Inclusiveness addresses equal access and empowerment, and Reliability and safety addresses consistent, safe operation rather than data protection specifically.
2. A team lead wants to speed up onboarding by having new hires rely on Copilot Chat to explain unfamiliar legacy code, while a senior architect is concerned that trusting an AI-generated explanation of business-critical logic without cross-checking against the actual code could let a subtly wrong mental model take root early in a new hire's understanding of the system. Which documented risk best supports the senior architect's concern?
- A. The GitHub Copilot Trust Center's SLA guarantees zero downtime for Copilot Chat
- B. Hallucination - the explanation can be plausible-sounding yet factually incorrect, unsupported, or fabricated relative to what the code actually does
- C. New hires are contractually barred from using Copilot Chat during their first 90 days
- D. Copilot Chat explanations are cryptographically verified against the underlying source code before being shown
Show answer
Correct answer: B
Hallucination applies to any generated text, including explanations, meaning a plausible-sounding walkthrough of legacy logic could still be inaccurate or unsupported by what the code actually does - exactly the risk of an incorrect mental model taking root. There's no such SLA guarantee tied to this scenario, no contractual onboarding restriction, and no cryptographic verification step for chat explanations.
3. An enterprise wants to both (1) preserve its Copilot Copyright Commitment IP indemnity coverage and (2) let a subset of developers experiment with the public-code-matching setting set to Allow so they can inspect match details for research. Legal flags a potential conflict between these two goals. Based on GitHub's documented indemnity condition, what should the enterprise's Copilot administrator confirm before allowing this experiment?
- A. IP indemnity has no relationship to the public-code-matching setting, so the Allow experiment carries no legal implication
- B. Indemnity coverage is determined solely by which IDE a developer uses, not by any Copilot policy setting
- C. Indemnity for unmodified suggestions requires the duplication filter set to Block, so an Allow setting could put a covered repo's suggestions outside coverage
- D. IP indemnity coverage, once granted, is unaffected by any subsequent policy change
Show answer
Correct answer: C
GitHub's documented condition for indemnity is that the duplication detection filter be enabled and set to Block for unmodified suggestions, so allowing matches through (Allow) in a covered repository could take those suggestions outside the indemnity condition - legal's concern is well-founded. Indemnity is directly tied to this filter setting, is unrelated to IDE choice, and is not a one-time, unconditional grant immune to later policy changes.
4. A fintech company's compliance team requires that any AI-assisted code changes touching the payments module be verifiable as free of fabricated business logic, while the engineering team is under a tight release deadline and wants to accept Copilot suggestions with minimal friction. A senior engineer proposes accepting all payments-module suggestions automatically if they pass the existing unit test suite, arguing tests already catch any hallucinated logic. Why does GitHub's documented guidance recommend against this approach?
- A. Unit tests written before Copilot was introduced automatically become invalid and must be rewritten from scratch
- B. Copilot's duplication detection filter is designed to catch hallucinated suggestions before they reach the editor
- C. Accepting suggestions faster than manual review generally improves code quality regardless of domain
- D. Hallucinated output can be plausible-sounding and syntactically valid, so it can pass an existing test suite while still being factually incorrect or unsupported by the intended business logic
Show answer
Correct answer: D
GitHub warns that hallucinated output is "factually incorrect, unsupported by the provided context, or entirely fabricated" and can still be plausible-sounding - a passing test suite written for expected inputs does not guarantee the logic matches actual payments requirements, so review remains necessary in a compliance-sensitive module. Existing tests don't become invalid, the duplication detection filter targets public-code matches (not hallucination), and skipping review does not universally improve quality, especially for consequential domains like payments.
5. A developer repeatedly accepts GitHub Copilot suggestions without reviewing them, then discovers weeks later that several accepted suggestions introduced subtle logic errors across the codebase. Which documented harm of over-reliance does this best match?
- A. Decreased productivity and potential financial loss from errors that were difficult to detect
- B. A breach of the GitHub Copilot Trust Center's data retention policy
- C. An automatic suspension of the developer's Copilot license
- D. A violation of the public code duplication detection filter
Show answer
Correct answer: A
GitHub's documentation warns that over-reliance can mean "mistakes in AI outputs may be difficult [to detect]," which "can lead to decreased productivity, financial loss, or compromised application quality." This scenario doesn't involve a data-retention breach, an automatic suspension, or the duplication filter.
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6. A data engineering team asks Copilot to generate a script that reformats a production dataset in place, and the suggestion looks clean and idiomatic. One engineer wants to run it immediately against production to save time before an approaching deadline, while another insists on validating it against a staging copy first, citing the tool's own documented guidance on semantic correctness. Which concept most directly justifies the second engineer's caution?
- A. The GitHub Copilot Trust Center's uptime commitment, which has no bearing on script correctness
- B. PR text completion's English-language limitation, which does not apply to code generation
- C. The duplication detection filter, which evaluates suggestions for license matches rather than correctness
- D. Semantic incorrectness — code can appear valid while failing to achieve the developer's actual intent or containing logical errors
Show answer
Correct answer: D
GitHub documents semantic incorrectness as a risk where generated code "may appear valid while failing to achieve developer intent or containing logical errors requiring human validation" - clean-looking code is not proof of correct behavior against real data, which is why staging validation matters before a production run. The duplication filter addresses license matching, the uptime SLA is unrelated to logical correctness, and the PR text completion language limitation doesn't apply to this code-generation scenario.
7. A government contractor is building a benefits-eligibility screening tool with GitHub Copilot's help. The program manager wants to ship an MVP within two weeks, while an internal ethics reviewer insists the team demonstrate the six responsible AI principles were considered, particularly around differential treatment of applicant demographics, before any code touches production data. Which principle most directly addresses the ethics reviewer's specific concern about differential treatment of demographic groups?
- A. Inclusiveness - ensuring interfaces support screen readers and other assistive technology
- B. Fairness - ensuring AI systems treat all people fairly and avoid differential impacts on similarly situated groups
- C. Transparency - ensuring the system's design choices are documented and justified
- D. Reliability and safety - ensuring the system behaves predictably under normal conditions
Show answer
Correct answer: B
Fairness is defined as treating "all people fairly" and specifically calls out avoiding "differential impacts on similarly situated groups," which is exactly the demographic-treatment concern raised. Inclusiveness centers on accessibility and empowerment (not demographic parity of decisions), Transparency concerns explainability of design choices, and Reliability and safety concerns consistent operation rather than fairness of outcomes.
8. According to Microsoft and GitHub's six principles of responsible AI, which principle specifically requires that AI creators remain responsible for how their systems operate and continuously monitor performance?
- A. Inclusiveness
- B. Accountability
- C. Fairness
- D. Transparency
Show answer
Correct answer: B
Accountability states that "people should be accountable for AI systems" and requires creators to continuously monitor system performance and mitigate risks. Inclusiveness concerns equal access and empowerment, Fairness concerns treating people without differential impact, and Transparency concerns systems being understandable and auditable.
9. A multinational SaaS company wants its AI-assisted developer tools to work equally well for teams in low-connectivity regions and for developers using screen readers, while a regional product manager argues that supporting these cases is optional "nice-to-have" polish rather than a core responsible AI requirement. Which principle contradicts the regional product manager's framing?
- A. Accountability - which concerns who is responsible for monitoring an AI system's performance over time
- B. Reliability and safety - which concerns consistent, predictable operation under normal conditions
- C. Inclusiveness - which calls for AI systems to be accessible to people with disabilities, work across geographies, and function with limited connectivity
- D. Transparency - which concerns explaining a system's design choices to end users
Show answer
Correct answer: C
Inclusiveness explicitly requires that AI systems be accessible "regardless of physical or mental abilities" and work "offline and with limited connectivity," positioning accessibility and low-connectivity support as core requirements rather than optional polish. Accountability concerns oversight responsibility, Reliability and safety concerns consistent operation, and Transparency concerns explainability - none address accessibility or connectivity directly.
10. An engineering director wants to claim in a customer-facing security questionnaire that GitHub Copilot's outputs are "free of bias because the underlying model was trained exclusively on curated, bias-free code." The compliance reviewer pushes back, citing responsible AI documentation. Which documented fact supports the compliance reviewer's pushback?
- A. GitHub's documentation confirms training data underwent a bias-elimination certification process before use
- B. Fairness bias-mitigation techniques are relevant mainly to models trained after 2024
- C. Bias affects natural-language chat responses but not code completion suggestions
- D. Training data sources may contain biases that get perpetuated, so fairness requires active mitigation such as reviewing data and balanced testing
Show answer
Correct answer: D
GitHub's documentation states training data sources "may contain biases and errors that can be perpetuated by the tool," which is why fairness principles call for active mitigation such as reviewing training data, balanced-sample testing, and monitoring - a claim of being inherently "bias-free" contradicts this. There is no documented bias-elimination certification, no training-date cutoff on relevance, and bias risk is documented for code suggestions specifically, not just chat.
GH-300 study resources
Why Practice with MSCertQuiz?
Microsoft Learn teaches concepts. We prepare you for the actual exam.
Full coverage of Copilot's newest capabilities — Agent Mode, Copilot Edits, MCP, Sub-Agents, and the Copilot CLI — alongside the responsible-AI and data-handling fundamentals every exam question is ultimately built on
Real prompt-engineering scenarios: zero-shot vs. few-shot prompting, context determination, and chat history usage, not just definitions of the terms
Organization-governance content most free question banks skip entirely: org-wide policy management, content exclusions, public code filtering, and audit log events
Every question verified against live GitHub Docs and Microsoft Learn training modules before it ships, so content stays accurate as Copilot features keep shipping
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“I use Copilot every day but had never really thought about how the data flow and prompt building actually work under the hood. The architecture questions filled real gaps. Passed on my first attempt.”
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“The prompt engineering domain was the one I underestimated going in. Practicing the zero-shot vs. few-shot scenarios here made the difference between guessing and actually knowing the right approach on exam day.”
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Software Engineer
“As the person rolling out Copilot policy for our org, the questions on content exclusions and org-wide administration were spot on. Way more practical than I expected from a practice test.”
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Passed GH-300 first try
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