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Anthropic CCDV-F Exam Syllabus Topics:

Anthropic Claude Certified Developer-Foundations Sample Questions:

Question 1

Your Claude application is hitting context window limits when processing long customer service transcripts.
A junior developer suggests increasing the temperature parameter to fix the issue.
How would you respond?

A. Explain that temperature controls sampling randomness and is unrelated to context capacity, then address the context issue through summarization or chunking.
B. Adjust the temperature parameter together with the max_tokens parameter, treating the combined adjustment as the team's mechanism for managing context window pressure during long-transcript processing.
C. Remove the system prompt entirely to make room for longer transcripts in each request, freeing up context window space the system prompt would otherwise consume.
D. Increase the temperature parameter as the junior developer suggested and observe whether the context window issue resolves over the next several runs of the application in production.

Question 2

A teammate has asked you to explain when a Skill would be the right choice over an MCP server. The teammate is unsure how the two differ in practice when both can be reused across teams.
How would you explain the distinction?

A. A Skill is the older mechanism and an MCP server is the newer one, so the team should prefer an MCP server for any reusable capability that the team builds going forward.
B. A Skill bundles prompts, scripts, and data into a package the model loads as a unit while an MCP server exposes resources, tools, and prompts through a standard client interface.
C. A Skill and an MCP server are equivalent extension mechanisms that the team can use interchangeably for any reusable capability that needs to be accessible across teams.
D. A Skill is preferable for cross-team reuse because it loads more efficiently than an MCP server during normal operation in the team's typical multi-team workloads.

Question 3

A new agent your team built handles customer support tickets, but it routinely gets confused when a single ticket spans billing, shipping, and product issues. The agent often loses track of which sub-issue it has already addressed and revisits the same one. The team is considering architectural changes.
What architectural change would you recommend?

A. Increase the size of the agent's context window so it can hold the full ticket history at once.
B. Switch to a deterministic workflow that handles billing, shipping, and product issues in a fixed sequence.
C. Introduce an orchestrator agent that delegates billing, shipping, and product sub-issues to dedicated subagents.
D. Add detailed prompting that instructs the agent to track which sub-issues have been resolved and which remain.

Question 4

You are designing an agent that processes vendor invoices. The work involves a small number of well- understood steps, but occasionally an invoice arrives in an unexpected format that requires the system to decide between rerouting, requesting clarification, or flagging for human review.
The most appropriate architecture for this system is...

A. A workflow for the standard path with an agent invoked at the decision point for unexpected formats.
B. A manager agent that delegates each step of standard invoice processing to a dedicated subagent, with a separate subagent handling each unexpected format.
C. A fully autonomous agent that handles every invoice from start to finish across all formats.
D. A single large prompt that processes every incoming invoice, both standard and unexpected, in one model call.

Question 5

You are deciding between Claude models for a task. The team has identified three relevant tradeoff dimensions: quality, latency, and cost.
The right model is the one that...

A. Meets the task's cost target within a defined latency budget, with quality validated against a representative sample of inputs after the model is selected.
B. Satisfies the task's latency requirement first, then is evaluated against quality and cost thresholds to confirm the selection is acceptable across all three dimensions.
C. Fits the task's quality, latency, and cost requirements together, recognizing that improving one dimension typically affects the others.
D. Meets the task's quality requirements at an acceptable latency, with cost reviewed separately once the quality and latency bar has been established.

Solutions: