Claude Certified Architect - Foundations: Claude Fundamentals and Model Capabilities

What to know

Understand the Claude model family as a set of capability tiers rather than a single product. Candidates should be able to explain the practical trade-offs among model sizes: the largest models favor complex reasoning, long-form synthesis, and nuanced instruction-following, while smaller/faster models favor latency, throughput, and cost. Know that all current Claude models are conversational text models accessed through a Messages-style API where the request carries a system prompt plus an ordered list of user and assistant turns, and the response contains content blocks (text blocks, and tool-use blocks when tools are supplied).

Key concepts: the distinction between the system prompt and user turns; that the model is stateless, so conversation history must be resent each call; max_tokens as an output cap (not a target); stop sequences; temperature/top-p as sampling controls; and the idea that the model returns structured content blocks rather than a bare string. You should also know that context window size is a hard limit and that output counts toward your budget.

Traps

• Assuming the model remembers prior calls. It does not; state lives in your application.
• Treating max_tokens as "how long the answer will be." It is an upper bound; truncation shows up as a stop_reason of max_tokens.
• Believing a bigger model is always the right choice. Questions often reward matching model tier to task complexity and latency/cost constraints.
• Confusing "the model refused" with "the model was truncated" or "a tool call was requested."

Rule of thumb

Read stop_reason first: end_turn means a natural finish, max_tokens means you were cut off, tool_use means the model wants you to act. Diagnose before you change the prompt.