Claude Certified Developer - Foundations study plan

Map the anatomy of a Messages API request and response, labeling every required and optional field from memory

Differentiate the model family tiers by latency, cost, and capability, and justify a model choice for three given workloads

Estimate token counts for sample prompts and completions and convert them into a per-1000-call cost figure

Write and run a minimal request that sets system prompt, max_tokens, temperature, and stop sequences, then explain each parameter's effect on output

Diagnose five common API error responses and state the correct corrective action for each

Describe how the conversation array preserves state across turns and rewrite a stateless single call into a correct multi-turn exchange

Explain how stop_reason values should drive branching logic in a caller

Rewrite five vague prompts into specific ones by adding role, task, constraints, and output format

Apply few-shot exemplars to a classification task and measure whether label accuracy improves

Compare zero-shot, few-shot, and role-primed variants of the same task and record the tradeoffs

Compose a system prompt that encodes persona, scope limits, and refusal behavior without contradicting the user turn

Structure prompts with explicit XML-style delimiters and demonstrate reduced instruction bleed on a long input

Implement chain-of-thought and structured reasoning prompts and compare output quality against a direct-answer baseline

Design an output-format contract that yields reliably parseable JSON across twenty sampled runs

Identify and correct three prompt anti-patterns such as conflicting instructions, buried constraints, and ambiguous pronouns

Define a tool schema with accurate JSON Schema types, required fields, and descriptions that steer correct argument selection

Trace the full tool-use loop from user turn through tool_use block, your execution, and tool_result block back to the model

Handle malformed or missing tool arguments defensively and return structured errors the model can recover from

Decide when to force tool use versus let the model choose, and defend the choice for three scenarios

Compare single-tool and multi-tool selection accuracy on ten ambiguous requests

Decompose a multi-step task into discrete agent steps with explicit entry and exit conditions per step

Implement a loop with a hard iteration cap, budget guard, and termination condition that cannot spin indefinitely

Add retry with backoff around transient failures and a fallback path for unrecoverable steps

Design a state object that carries only the context each step needs, and justify what you excluded

Log step-level timing, token usage, and decisions so a failed run can be reconstructed after the fact

Apply prompt caching to a large stable prefix and measure the resulting latency and cost delta against an uncached baseline

Order prompt content so cacheable material stays stable and volatile material sits at the end

Design a retrieval step that selects relevant chunks and injects them without exceeding a token budget

Handle long-context overflow by summarizing, truncating, or chunking, and state when each strategy is appropriate

Evaluate retrieval quality on ten queries and identify at least two failure cases in your chunking or ranking

Build an eval set of at least twenty labeled cases covering normal, edge, and adversarial inputs

Score outputs with a mix of exact-match, rubric grading, and model-as-judge, and note where each method is unreliable

Instrument a call path with structured logging for latency, token counts, errors, and stop reasons

Red-team your own application with injection attempts, out-of-scope requests, and ambiguous prompts, then document each result

Write a deployment checklist covering rate limits, fallback behavior, data handling, and human escalation

Consolidate the seven domain cheat sheets into a single condensed review document

Take a timed full-length practice exam and score each domain separately

Rank your weakest three subdomains by error count and re-drill each with targeted practice

Re-explain every missed concept aloud or in writing without consulting notes

Verify each earlier week's deliverable still runs and patch anything that broke