The correct option is GitHub Copilot is trained on static data so it does not truly reason it cannot be trusted for calculations and it may surface older patterns and misread ambiguous prompts.

This statement is accurate because Copilot is powered by large language models that learn from a fixed training set and do not update their knowledge in real time. These models generate predictions based on patterns rather than performing step by step symbolic reasoning, so they can be unreliable for precise calculations or strict logical proofs. Since the training data reflects code available at the time of training, suggestions can mirror older practices. When prompts are ambiguous the model may infer an unintended direction, so clear and specific instructions help reduce misinterpretation.

When integrated with Vertex AI Search Copilot gains real time knowledge and performs symbolic reasoning for exact answers is incorrect because adding a separate search service does not convert Copilot into a system with real time knowledge or symbolic reasoning. Copilot remains a probabilistic generator and it does not provide exact guarantees.

GitHub Copilot continuously learns from live repositories and therefore delivers current code with rigorous logical reasoning is incorrect because Copilot does not continuously learn from your repositories and it only uses your open files and context during a session. Its training is not updated from your code in real time and its outputs are not the result of rigorous logical reasoning.

Copilot relies only on curated snippets and applies advanced symbolic reasoning that guarantees secure and compliant code although formatting might be inconsistent is incorrect because Copilot is trained on broad public data rather than only curated snippets and it does not perform symbolic reasoning. It cannot guarantee security or compliance, so all outputs require review.

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