GitHub Agentic AI Developer (GH-600) · 18% practice allocation

GitHub GH-600: Perform evaluation, error analysis, and tuning Practice Questions

5 free sample questions from the Perform evaluation, error analysis, and tuning domain, with answers and explanations. The full GH-600 bank has 61 questions across 6 domains. Questions are scenario-based and written from the official GitHub exam guide.

1.

After reviewing evaluation results, your team finds that a GitHub Copilot agent frequently retrieves stale context from its memory store when handling long-running tasks that span multiple sessions. The retrieved context causes the agent to repeat work already completed in prior sessions. What is the MOST appropriate tuning action?

  • A. Refine the memory retrieval strategy to prioritize recency and include session completion markers✓ Correct
  • B. Disable memory entirely so the agent relies only on the current session's context
  • C. Increase the agent's maximum token context window to retain more information per session
  • D. Add a post-execution instruction telling the agent to summarize its work at the end of every response
Explanation

The root cause is that the memory store returns stale, outdated entries rather than current state. The targeted fix is to refine memory retrieval — for example, by weighting recent entries, filtering by recency, or ensuring session completion markers are written and used during retrieval. Option C (larger context window) addresses token capacity, not stale retrieval logic. Option B (disabling memory) is a drastic measure that would prevent the agent from maintaining any cross-session continuity. Option D (post-execution summaries) may help write better memory entries going forward but does not fix the retrieval logic that currently selects stale content.

2.

A platform engineering team is designing evaluation criteria for a GitHub Copilot agent that manages infrastructure-as-code changes. They want to ensure the evaluation criteria reflect both correctness and safety. Which THREE of the following signals should they include? (Select THREE.)

  • A. Whether the agent never proposes deletion of production databases without an explicit human approval step✓ Correct
  • B. Whether the agent's commit message follows the conventional commits specification
  • C. Whether the agent surfaces a diff of planned changes before execution is confirmed
  • D. Whether the generated Terraform plan contains no resources flagged by policy-as-code checks✓ Correct
  • E. Whether the applied changes match the intended state described in the task specification✓ Correct
  • F. Whether the agent's response was generated in fewer than 5 API round-trips
Explanation

Option D (policy-as-code compliance) measures safety by ensuring generated infrastructure passes organizational guardrails. Option E (changes match the intended state) measures correctness against the task specification. Option A (no production database deletion without human approval) is a critical safety operational constraint aligned with responsible agentic execution. Option F (API round-trips) is an efficiency metric, not a correctness or safety signal. Option B (commit message format) is a style/quality concern unrelated to correctness or safety. Option C (surfacing a diff) is a good UX/workflow practice but is a process step, not itself an evaluation signal for correctness or safety outcomes.

3.

Your team is evaluating a coding agent in GitHub Copilot Workspace. You want to align evaluation criteria with the original development intent. The agent was designed to autonomously fix failing CI tests without altering public API signatures. Which TWO of the following evaluation criteria are MOST aligned with that intent? (Select TWO.)

  • A. The agent adds new unit tests to improve overall code coverage
  • B. The agent does not modify any method signatures in files marked as public API✓ Correct
  • C. The agent produces fixes in under 30 seconds per test case
  • D. The agent resolves all failing test cases identified in the CI run✓ Correct
  • E. The agent generates detailed commit messages explaining each change
Explanation

The development intent has two explicit goals: fix failing CI tests and preserve public API signatures. Option D directly measures whether the agent achieves the primary objective (resolving failing tests). Option B directly measures adherence to the key constraint (no public API signature changes). Option E (commit message quality) is a communication quality concern not stated in the intent. Option C (speed per test case) is a performance metric not mentioned as a design goal. Option A (adding new tests) goes beyond the stated scope and may even violate the constraint of minimal change.

4.

A GitHub Copilot agent is tasked with generating pull request summaries. After reviewing evaluation results, the team notices the agent consistently omits security-related file changes from its summaries. Logs confirm the agent's plan never includes a step to inspect files in the /security directory. What is the MOST likely root cause classification for this failure?

  • A. Reasoning error — the agent's planning logic does not prioritize or recognize security-relevant paths✓ Correct
  • B. Tool misuse — the agent is calling the file-listing tool with incorrect parameters
  • C. Memory corruption — the agent's long-term memory has overwritten the file scanning instructions
  • D. Context/environment issue — the agent lacks access to the /security directory at runtime
Explanation

Because the logs show the agent's plan never includes a step to inspect the /security directory, the failure originates in the agent's reasoning/planning phase — it does not recognize or prioritize security-relevant paths when constructing its plan. This is a reasoning error. Option B (tool misuse) would apply if the tool were called but with wrong parameters; the plan never reaches that step. Option D (context/environment issue) would apply if access were denied at runtime, but the plan omission occurs before any tool is invoked. Option C (memory corruption) is not a standard failure classification and is not supported by the evidence provided.

5.

A developer reviews the workflow artifacts from a GitHub Copilot coding agent run and notices the agent correctly identified the failing test, proposed a valid fix, but then applied the fix to the wrong file. Which artifact would MOST directly help confirm this is a tool invocation error rather than a reasoning error?

  • A. The system prompt used to initialize the agent at the start of the session
  • B. The evaluation scorecard generated after the session completed
  • C. The agent's final natural-language summary of the changes it made
  • D. The diff artifact showing which file was actually modified by the write_file tool call✓ Correct
Explanation

The diff artifact shows the exact file path and content that the write_file tool modified. If the diff shows the correct fix content applied to the wrong file path, this confirms the tool was invoked with the correct content but an incorrect file path — a tool invocation error. Option C (the agent's summary) is generated by the agent itself and may not accurately report which file was changed (especially if the agent also made an error in self-reporting). Option A (the system prompt) documents initial instructions and does not show what happened during execution. Option B (the evaluation scorecard) gives aggregate results but not the granular tool call details needed to distinguish error types.

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