New Release CCAR-F Claude Certified Architect Questions

The reviewer corrections have exposed a narrow and repeatable interpretation failure. The desired policy is clear: informal measurements are valid source values and must be preserved verbatim rather than normalized into invented quantities or treated as missing. This behavior can be communicated efficiently through targeted few-shot examples.

Anthropic recommends examples for demonstrating expected behavior and improving consistency. Examples can pair source phrases such as “a handful of spinach,” “a splash of vinegar,” and “a pinch of salt” with outputs that retain handful , splash , and pinch exactly. Additional counterexamples can show that Claude must not convert these phrases into grams, millilitres, or estimated serving quantities. ( )

Option A introduces a substantially heavier training workflow for a problem that can be addressed directly through the prompt. Option C creates a parallel extraction mechanism based on pattern matching; it will be brittle across linguistic variations and may populate a value without understanding its relationship to the correct ingredient. Option D adds useful classification metadata, but it does not instruct Claude to preserve the original measurement instead of inventing or omitting it.

The revised prompt should combine an explicit verbatim-extraction rule with several varied examples derived from the corrected cases, followed by regression evaluation against the identified failure set.

Official references/topics: Few-Shot Examples, Feedback-Driven Prompt Improvement, Verbatim Extraction, Regression Evaluation.

Request B benefits substantially more from an explicit multi-phase workflow because it is broad, judgment-intensive, and likely to affect multiple execution paths. Improving error handling requires the agent to identify current failure modes, determine which exceptions should be caught, preserve useful diagnostic information, prevent partial state corruption, and verify that the resulting behavior remains consistent across the module. An analyze → propose → implement → review sequence separates those concerns and creates checkpoints before consequential edits are made.

Request A is comparatively deterministic. The function and target name are already defined, and the task primarily requires locating references, applying the rename, and running tests or static analysis. Anthropic’s Claude Code guidance states that planning is most useful when the approach is uncertain, the work spans multiple files, or the developer is unfamiliar with the affected code. For a small change that can be described as a straightforward diff, direct implementation is usually preferable. ( )

Anthropic also characterizes prompt chaining as appropriate when a task can be divided into fixed stages and additional latency is justified by increased accuracy. Request B fits that pattern because analysis and review materially reduce the risk of introducing swallowed exceptions or inconsistent recovery behavior. ( )

Official references/topics: Prompt Chaining; Plan Before Editing; Multi-Phase Coding Workflows; Verification and Review.

Forking is specifically designed for exploring alternative directions from a shared body of prior analysis. Each fork starts with a copy of yesterday’s conversation history, including the files read, architectural observations, dependency findings, and decisions already recorded. The microservice approach and the in-place refactoring approach can then develop independently under separate session IDs.

Anthropic’s Agent SDK documentation states that a fork creates a new session from a copy of the original history while leaving the original session unchanged. Each resulting session can subsequently be resumed independently. The documented implementation combines resume with fork_session=True in Python or forkSession: true in TypeScript. ( )

Option B allows conclusions, assumptions, and proposed edits from the first approach to contaminate the evaluation of the second. Option C preserves context for only one branch and forces the engineer to reconstruct context manually for the other. Option D discards the detailed analysis already captured in the session and depends on potentially incomplete summaries.

Two forks provide equivalent starting conditions, preserve the parent investigation, and support a fair comparison of scope, migration risk, operational complexity, and required code changes. Filesystem edits should still be isolated through worktrees or checkpointing because session forking branches conversation history, not the working directory.

Official references/topics: Agent SDK Sessions; Session Forking; Alternative-Approach Exploration; Context Preservation.

Forking the existing analysis session creates independent continuations that inherit the accumulated conversation context. Each branch begins with the same understanding of the service module, request flow, source files, and error-handling patterns, but subsequent work on one testing strategy does not alter the other branch or the original session.

Anthropic’s Agent SDK documentation states that sessions can be resumed with their full context and forked to explore different approaches. In the SDK, enabling fork_session while resuming causes the continuation to receive a new session identifier rather than modifying the original session. ( )

Option B wastes time, tokens, and tool calls by requiring both new sessions to rebuild the same 23-file analysis. Option C mixes two experimental implementations into one conversation, increasing the risk that assumptions, edits, or conclusions from the first strategy influence the second. Option D preserves only a manually selected summary, which may omit details contained in the full session history.

The appropriate design is to create one fork for the end-to-end strategy and another fork for the snapshot strategy. The original analysis remains a stable parent, while each child session develops and evaluates its approach independently.

Official references/topics: Agent SDK Sessions, Session Forking, Context Preservation, Alternative-Approach Evaluation.