Anthropic CCDV-F Exam
Claude Certified Developer - Foundations
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, 1.Scenario: Developer Productivity with Claude You are building developer productivity tools using the
Claude Agent SDK. The agent helps engineers explore unfamiliar codebases, understand legacy systems,
generate boilerplate code, and automate repetitive tasks. It uses built-in tools (Read, Write, Bash, Grep,
Glob) and integrates with MCP servers. During a legacy payment module analysis, the coordinator first
assigns one subagent to map database tables and another to trace API callers. Both return useful findings.
The coordinator then invokes a planning subagent to propose a migration strategy, but the plan ignores
the database constraints and caller list already discovered, recommending changes that would break
known integrations.
What should you change to make this orchestration more reliable?
A. Replace the specialized subagents with one long-running generalist subagent that performs discovery
and planning together.
B. Instruct the planning subagent to infer missing dependencies from file names when prior findings are
unavailable.
C. Allow subagents to message each other directly so the planning subagent can request missing
analysis details.
D. Have the coordinator maintain shared investigation state and include relevant prior findings in each
subsequent subagent prompt.
Answer: D
Explanation:
Coordinator-owned state is central to reliable multi-agent orchestration. In a coordinator-subagent pattern,
the coordinator decomposes work, collects results, and decides which findings must be supplied to later
subagents so they can produce grounded outputs.
The underlying principle is that subagents operate in isolated task contexts. They should not be designed
as if they can automatically see prior coordinator conversations or other subagents' outputs. The
coordinator should maintain a structured investigation state, such as discovered tables, caller lists,
constraints, open questions, and confidence notes, then include the relevant subset in each downstream
prompt.
Letting subagents communicate directly weakens the hub-and-spoke architecture by hiding information
flow from the coordinator. Replacing specialists with one generalist can create context bloat and reduce
the value of parallel specialized analysis. Asking a planning subagent to infer missing dependencies from
file names is especially risky because it substitutes guesses for evidence.
For more on agent orchestration and subagent patterns, see the Agent SDK documentation and
the Claude Code Sub-agents documentation.
2.Scenario: Structured Data Extraction You are building a structured data extraction system using Claude.
The system extracts information from unstructured documents, validates output using JSON schemas,
and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream
systems. Your extraction QA pass reviews Claude's JSON outputs before downstream ingestion.
Reviewers dismiss many findings because the QA prompt flags harmless differences: inferred date
formats, optional fields absent from the source, and wording variations that do not change extracted
values. The current prompt says, "Check the extraction for accuracy and report any problems.".
What change would most effectively improve precision?
A. Increase the validation sample size and ask reviewers to manually ignore findings that are not
actionable.
Claude Certified Developer - Foundations
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Pass CCDV-F Exam with PassQuestion CCDV-F questions and
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, 1.Scenario: Developer Productivity with Claude You are building developer productivity tools using the
Claude Agent SDK. The agent helps engineers explore unfamiliar codebases, understand legacy systems,
generate boilerplate code, and automate repetitive tasks. It uses built-in tools (Read, Write, Bash, Grep,
Glob) and integrates with MCP servers. During a legacy payment module analysis, the coordinator first
assigns one subagent to map database tables and another to trace API callers. Both return useful findings.
The coordinator then invokes a planning subagent to propose a migration strategy, but the plan ignores
the database constraints and caller list already discovered, recommending changes that would break
known integrations.
What should you change to make this orchestration more reliable?
A. Replace the specialized subagents with one long-running generalist subagent that performs discovery
and planning together.
B. Instruct the planning subagent to infer missing dependencies from file names when prior findings are
unavailable.
C. Allow subagents to message each other directly so the planning subagent can request missing
analysis details.
D. Have the coordinator maintain shared investigation state and include relevant prior findings in each
subsequent subagent prompt.
Answer: D
Explanation:
Coordinator-owned state is central to reliable multi-agent orchestration. In a coordinator-subagent pattern,
the coordinator decomposes work, collects results, and decides which findings must be supplied to later
subagents so they can produce grounded outputs.
The underlying principle is that subagents operate in isolated task contexts. They should not be designed
as if they can automatically see prior coordinator conversations or other subagents' outputs. The
coordinator should maintain a structured investigation state, such as discovered tables, caller lists,
constraints, open questions, and confidence notes, then include the relevant subset in each downstream
prompt.
Letting subagents communicate directly weakens the hub-and-spoke architecture by hiding information
flow from the coordinator. Replacing specialists with one generalist can create context bloat and reduce
the value of parallel specialized analysis. Asking a planning subagent to infer missing dependencies from
file names is especially risky because it substitutes guesses for evidence.
For more on agent orchestration and subagent patterns, see the Agent SDK documentation and
the Claude Code Sub-agents documentation.
2.Scenario: Structured Data Extraction You are building a structured data extraction system using Claude.
The system extracts information from unstructured documents, validates output using JSON schemas,
and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream
systems. Your extraction QA pass reviews Claude's JSON outputs before downstream ingestion.
Reviewers dismiss many findings because the QA prompt flags harmless differences: inferred date
formats, optional fields absent from the source, and wording variations that do not change extracted
values. The current prompt says, "Check the extraction for accuracy and report any problems.".
What change would most effectively improve precision?
A. Increase the validation sample size and ask reviewers to manually ignore findings that are not
actionable.
