The full CCDV-F Masterclass is live, and you get the exclusive Skool Member pricing 🔥 👉 https://www.udemy.com/course/ccdv-f-exam-prep/?couponCode=SKOOLER (On 8/16, the SKOOLER coupon expires, at that point use the one below, a bit less discount.) https://www.udemy.com/course/ccdv-f-exam-prep/?couponCode=CURRENT_STUDENT As part of this community, your price is set to the lowest Udemy ever allows in your region. I won't quote it here, because it varies. Far below the public rate. Heads up, Udemy doesn't like these discounts, they force the code to only last five days. After that there is a less-awesome discount with the 'current_student' code. This is the credential for building on Claude, not just using it. CCDV-F is the Developer foundations cert, and the course is written for the engineers who ship the code: working with the Claude API and the Agent SDK, designing tool use and building MCP servers, standing up agents and multi-step workflows, and getting structured, reliable output back. You learn to engineer prompts for production reliability, manage the context window for cost and latency, evaluate and test what your agents actually do, isolate work with subagents, and handle the errors and edge cases that break real integrations. If CCAO-F is about mastering Claude.ai and the architect track is about designing Claude Code systems, CCDV-F is about building them with your own hands, and it is the skillset every developer on the team should have. This was a BEAST to build, so if you notice anything weird or off, please reach out and we'll fix it up right away. Kyle and I both feel like we need to sleep for three days after this monster build. Let's get you Developer certified. Another little prize, if you leave a review for the course and send us a screenshot here (in a DM), you're entered for a free premium membership for life. We're giving away 3 lifetime memberships after the course has been up for a week!

A Multi-Agent Research orchestrator's prompt currently has 'think step by step' in the system prompt. The team adds a few-shot example showing a reasoning chain followed by a final answer. Which interpretation is MOST consistent? A. Few-shot Chain-of-Thought and zero-shot Chain-of-Thought must both be present for either to work, since the instruction primes the behavior and the examples demonstrate it, and the model needs both signals B. Few-shot examples with reasoning chains can teach CoT pattern more reliably than the instruction alone; this is a stronger CoT than zero-shot CoT and may not need the 'think step by step' phrase C. Adding the worked examples disables the 'think step by step' instruction that was already in the prompt D. Few-shot examples cannot include reasoning chains, so the new example only teaches the final-answer format Drop your answer (A / B / C / D) in the comments 👇 I'll reveal the correct answer and the why tomorrow.

A Customer Support Agent team is asked to implement password resets. The flow is verify-identity, generate-token, send-email, always in that order. Which architectural class fits BEST? A. A multi-agent system with one agent per step B. An agent because password resets fundamentally involve security-sensitive runtime decisions to make C. A conversational system that asks the user step-by-step D. A deterministic workflow because the three steps are fixed and ordered Drop your answer (A / B / C / D) in the comments 👇 I'll reveal the correct answer and the why tomorrow.

A Multi-Agent Research firm's escalation system has a fixed 60-second timeout — if the human reviewer does not respond, the agent proceeds with its default decision. The team finds reviewers regularly miss the window. Which adjustment BEST preserves oversight? A. Lower the timeout to 10 seconds to force reviewers to respond quickly before the agent proceeds with its default B. Treat timeout as continued waiting, not auto-proceed; surface the queue depth to operations C. Auto-proceed faster and rely on post-hoc review D. Disable triggers when reviewer load is high Drop your answer (A / B / C / D) in the comments 👇 I'll reveal the correct answer and the why tomorrow.

A CI/CD Integration team has a validation-retry loop on PR review extractions. The team notices retries cluster on certain PR types. Which approach is MOST aligned with continuous improvement? A. Ignore the failure clusters since some extraction failures are inevitable, since chasing every cluster costs more engineering time than simply tolerating the retries B. Increase the retry cap to absorb the spike on those PR types, since allowing more attempts lets the loop eventually succeed on the inputs that currently cluster failures and absorbing the spike this way is faster than diagnosing each failing PR type C. Lower temperature on the retries so they converge faster, since a more deterministic setting helps the failing PR types reach valid output in fewer attempts D. Log retry-triggering inputs and failure reasons; periodically analyze the cluster to identify systematic issues, likely schema gaps or missing few-shot examples for the failing pattern Drop your answer (A / B / C / D) in the comments 👇 I'll reveal the correct answer and the why tomorrow.