Claude Certified Associate - Foundations study plan
Claude Certified Associate - Foundations · Give each week a clear focus.
8 weeks · About 7 hours per week
Claude Fundamentals and Model Landscape
Map the Claude model family into a comparison table covering capability tier, typical latency and cost trade-offs, and intended use cases, citing at least four distinct models.
Explain in writing how context windows, input versus output token limits, and max_tokens interact when a request is submitted.
Identify the primary surfaces for using Claude (chat interface, developer API, and cloud-hosted endpoints) and state one constraint unique to each.
Describe, in your own words, what Constitutional AI training and RLHF-style alignment contribute to model behaviour and refusal patterns.
Your checkpoint
A one-page model selection matrix that assigns the most appropriate model tier to five written scenarios, each with a two-sentence justification and the runner-up choice.
Prompt Design and Prompt Engineering Techniques
Rewrite ten deliberately vague prompts into structured prompts that specify role, task, context, constraints, and output format.
Construct a three-example few-shot prompt for a classification task and compare its output against a zero-shot version on the same five inputs.
Vary temperature and top-p across a fixed prompt and record how determinism and creativity shift in the outputs.
Apply chain-of-thought and step-by-step decomposition to one reasoning task and note where the technique helped or added noise.
Write a reusable prompt template with clearly marked placeholder variables and an explicit refusal or uncertainty instruction.
Your checkpoint
A ten-entry prompt library where each entry shows the original prompt, the rewritten prompt, the technique applied, and a pass/fail criterion for judging the output.
Prompt Design and Prompt Engineering Techniques & API and Integration Essentials
Differentiate the system prompt, user turn, and assistant prefill roles and demonstrate how moving content between them changes behaviour.
Write a minimal working API request that sets the system prompt, sends a user message, and reads the returned content and stop reason from the response body.
Handle the four most common integration failure modes: authentication errors, rate limits, context-length overflow, and malformed request payloads.
Compare streaming versus non-streaming responses and state which user experience each suits.
Explain how conversation history is maintained across stateless API calls and implement a simple message-array append pattern.
Your checkpoint
A documented request/response walkthrough containing one successful call, one streamed call, and three error cases with the exact remediation taken for each.
API and Integration Essentials & Context, Token, and Output Management
Estimate token counts for a sample prompt and predict whether it will fit a given context window before sending it.
Design a chunking and summarisation strategy for a document that exceeds the context window, and justify the overlap you chose.
Reduce a bloated prompt by at least thirty percent while preserving output quality, documenting each cut and its rationale.
Set output-length controls deliberately and explain the trade-off between max_tokens, truncation risk, and cost.
Your checkpoint
A token budget worksheet for one realistic task showing input tokens, reserved output tokens, headroom, measured cost per call, and the before/after of the trimmed prompt.
Safety, Alignment, and Responsible Use
Summarise the responsible-use principles that shape Claude's behaviour, including honesty, harm avoidance, and avoidance of manipulative outputs.
Identify three categories of requests that should trigger a refusal and write a prompt-side instruction that reduces false refusals on benign edge cases.
Explain the difference between a model limitation, a policy refusal, and a prompt design flaw, and classify five example outputs accordingly.
Describe how to handle sensitive user data in prompts, covering minimisation, redaction, and logging considerations.
Your checkpoint
A safety review checklist of at least ten items applied to your own prompt library, with each flagged prompt annotated and revised where needed.
Context, Token, and Output Management & Evaluation, Testing, and Workflow Integration
Build a small labelled test set of at least fifteen input/expected-output pairs that covers typical, edge, and adversarial cases.
Define three scoring criteria (for example factual accuracy, format compliance, and tone) and rate your prompt outputs against them consistently.
Run a regression comparison between two prompt versions on the same test set and quantify the difference.
Add a structured output contract (schema or rigid format) to one prompt and verify compliance across the full test set.
Your checkpoint
An evaluation scorecard for one prompt version showing per-case scores, aggregate accuracy, and a written verdict on whether it is ready to ship.
API and Integration Essentials & Evaluation, Testing, and Workflow Integration
Assemble an end-to-end workflow that chains a system prompt, retrieval of supplied reference text, model call, and output validation.
Instrument the workflow with logging of prompt version, token usage, latency, and outcome status for each run.
Decide which steps belong in the model versus in deterministic code, and record the reasoning for each boundary.
Document a rollback plan describing how a prompt regression would be detected and reverted in production.
Your checkpoint
A workflow design document plus a run log of ten executions showing prompt version, tokens consumed, latency, and pass/fail against your evaluation criteria.
Claude Fundamentals and Model Landscape & Prompt Design and Prompt Engineering Techniques & API and Integration Essentials & Context, Token, and Output Management & Safety, Alignment, and Responsible Use & Evaluation, Testing, and Workflow Integration
Consolidate all prior deliverables into a single indexed study binder organised by exam domain and weighted by percentage.
Retake your own evaluation scorecard and confirm no regression across the prompt library.
Complete two timed mixed-domain practice sets and record your score and error category for every missed item.
Revisit only the weakest two domains, writing a one-page correction sheet for each recurring mistake.
Your checkpoint
A timed full-length mock exam result with a domain-by-domain score breakdown and a ranked list of the five concepts to review in the final 48 hours.
Milestones
Week 2: You can explain the model landscape and prompt technique choices out loud without notes, and your prompt library passes its own criteria on at least eight of ten entries.
Week 4: You can trace a request from prompt construction through API response, correctly predict token fit, and explain every error path you handled.
Week 6: Your evaluation scorecard scores above your own shipping threshold, and a regression run cleanly separates the better prompt version.
Week 8: Mock exam score is at or above your target pass margin, with no domain below sixty percent and every missed item mapped to a reviewed concept.