,https://www.passcert.com/CCAR-F.html
23 questions selected from source version V9.02
CLAUDE CERTIFIED ARCHITECT
Question 1
You are building a multi-agent research system using the Claude Agent SDK. A coordinator agent delegates
to specialized subagents: one searches the web, one analyzes documents, one synthesizes findings, and
one generates reports. The system researches topics and produces comprehensive, cited reports.
A user expands the research system beyond its original web-search agent by adding specialized data
sources. A financial API agent returns structured JSON containing revenue, margins, and growth rates. A
news-monitoring agent returns prose summaries of recent developments. A patent-analysis agent returns
structured lists of technology areas. The synthesis agent combines these results into executive briefings.
Currently, it converts everything into bullet points, causing financial comparisons to lose tabular clarity and
news summaries to lose their narrative flow.
What change would most improve briefing quality?
A. Standardize all subagent outputs as prose summaries with inline citations.
B. Add a format-conversion layer that transforms every subagent output into a common intermediate
representation.
C. Update the synthesis agent to render each content type appropriately-for example, financial data as
tables, news as prose, and patent areas as structured lists.
D. Standardize all subagent outputs as JSON containing claim, evidence, source, and confidence fields.
Answer: C
Explanation
Option C preserves the information structure that makes each source useful. Financial metrics share comparable fields and
therefore benefit from rows, columns, aligned units, and reporting periods. News findings require connected prose to
preserve chronology and causal relationships, while patent technology areas are naturally represented as categorized lists.
Anthropic's output-consistency guidance recommends specifying the exact output format needed for the task rather than
relying on an unspecified default. Anthropic' s discussion of its multi-agent research system also recognizes specialized
output stages for reports, structured data, and visualizations because specialist prompts can produce better results than
generic coordinator processing.
Option A destroys the comparative structure of numerical data.
Option D can provide a useful provenance contract internally but does not determine how the executive briefing should
present heterogeneous content.
Option B risks creating a lowest-common-denominator representation that discards source-specific advantages. The
synthesis contract should preserve normalized facts and provenance internally while directing the report generator to select
presentation forms according to the content's semantic structure and the executive reader's needs.
Page 2
, https://www.passcert.com/CCAR-F.html
CLAUDE CERTIFIED ARCHITECT
Question 2
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 the built-in tools (Read, Write, Bash, Grep, Glob) and integrates with Model Context
Protocol (MCP) servers.
An engineer asks your agent to identify untested code paths in a legacy payment processing module
spanning 45 files. After reading the first 8 source files, the agent's responses are becoming noticeably less
accurate-it' s forgetting previously discussed code patterns and hasn't yet located all test files or traced
critical payment flows.
What's the most effective approach to complete this investigation?
A. Spawn subagents to investigate specific questions (e.g., "find all test files for payment processing," "trace
refund flow dependencies") while the main agent coordinates findings and preserves high-level
understanding.
B. Clear context with /clear, then selectively re-read only the most critical files discovered so far, writing key
findings to a scratchpad file that persists between context resets.
C. Switch to using Grep to search for specific function names instead of reading full files, reducing the
content loaded into context for remaining exploration.
D. Document all current findings in a summary report, clear context completely, then use that report as the
sole reference for continuing the investigation.
Answer: A
Explanation
The investigation contains several bounded research questions that can be delegated independently: locating the complete
test suite, tracing payment and refund flows, identifying conditional branches, and mapping external dependencies. Each
subagent can read the relevant files in its own context and return a focused summary to the coordinating agent.
Anthropic recommends subagents for codebase exploration because extensive file reading rapidly consumes the main
context window. Subagents isolate that volume and return only their conclusions, preserving the main conversation for
synthesis and implementation. (https://docs.anthropic.com/en/docs/claude-code /common-workflows) Anthropic also
describes parallel research as appropriate when separate investigation paths can proceed independently and the main agent
can synthesize the results afterward. (https://docs. anthropic.com/en/docs/claude-code/sub-agents)
Option B sacrifices the current conversational state and requires reconstruction after /clear.
Option C may reduce token usage, but isolated text matches cannot reliably reveal full execution paths, indirect calls, or test
coverage relationships.
Option D converts the current analysis into a single lossy summary and risks omitting details needed later.
Option A directly addresses the demonstrated context degradation while retaining a high-level coordinating thread. The
subagent prompts should be narrowly scoped and require concrete outputs such as file paths, uncovered branches,
call-chain evidence, and existing tests associated with each flow.
Official references/topics: Subagent Context Isolation; Parallel Research; Context Preservation; Coordinated Codebase
Analysis.
Page 3
23 questions selected from source version V9.02
CLAUDE CERTIFIED ARCHITECT
Question 1
You are building a multi-agent research system using the Claude Agent SDK. A coordinator agent delegates
to specialized subagents: one searches the web, one analyzes documents, one synthesizes findings, and
one generates reports. The system researches topics and produces comprehensive, cited reports.
A user expands the research system beyond its original web-search agent by adding specialized data
sources. A financial API agent returns structured JSON containing revenue, margins, and growth rates. A
news-monitoring agent returns prose summaries of recent developments. A patent-analysis agent returns
structured lists of technology areas. The synthesis agent combines these results into executive briefings.
Currently, it converts everything into bullet points, causing financial comparisons to lose tabular clarity and
news summaries to lose their narrative flow.
What change would most improve briefing quality?
A. Standardize all subagent outputs as prose summaries with inline citations.
B. Add a format-conversion layer that transforms every subagent output into a common intermediate
representation.
C. Update the synthesis agent to render each content type appropriately-for example, financial data as
tables, news as prose, and patent areas as structured lists.
D. Standardize all subagent outputs as JSON containing claim, evidence, source, and confidence fields.
Answer: C
Explanation
Option C preserves the information structure that makes each source useful. Financial metrics share comparable fields and
therefore benefit from rows, columns, aligned units, and reporting periods. News findings require connected prose to
preserve chronology and causal relationships, while patent technology areas are naturally represented as categorized lists.
Anthropic's output-consistency guidance recommends specifying the exact output format needed for the task rather than
relying on an unspecified default. Anthropic' s discussion of its multi-agent research system also recognizes specialized
output stages for reports, structured data, and visualizations because specialist prompts can produce better results than
generic coordinator processing.
Option A destroys the comparative structure of numerical data.
Option D can provide a useful provenance contract internally but does not determine how the executive briefing should
present heterogeneous content.
Option B risks creating a lowest-common-denominator representation that discards source-specific advantages. The
synthesis contract should preserve normalized facts and provenance internally while directing the report generator to select
presentation forms according to the content's semantic structure and the executive reader's needs.
Page 2
, https://www.passcert.com/CCAR-F.html
CLAUDE CERTIFIED ARCHITECT
Question 2
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 the built-in tools (Read, Write, Bash, Grep, Glob) and integrates with Model Context
Protocol (MCP) servers.
An engineer asks your agent to identify untested code paths in a legacy payment processing module
spanning 45 files. After reading the first 8 source files, the agent's responses are becoming noticeably less
accurate-it' s forgetting previously discussed code patterns and hasn't yet located all test files or traced
critical payment flows.
What's the most effective approach to complete this investigation?
A. Spawn subagents to investigate specific questions (e.g., "find all test files for payment processing," "trace
refund flow dependencies") while the main agent coordinates findings and preserves high-level
understanding.
B. Clear context with /clear, then selectively re-read only the most critical files discovered so far, writing key
findings to a scratchpad file that persists between context resets.
C. Switch to using Grep to search for specific function names instead of reading full files, reducing the
content loaded into context for remaining exploration.
D. Document all current findings in a summary report, clear context completely, then use that report as the
sole reference for continuing the investigation.
Answer: A
Explanation
The investigation contains several bounded research questions that can be delegated independently: locating the complete
test suite, tracing payment and refund flows, identifying conditional branches, and mapping external dependencies. Each
subagent can read the relevant files in its own context and return a focused summary to the coordinating agent.
Anthropic recommends subagents for codebase exploration because extensive file reading rapidly consumes the main
context window. Subagents isolate that volume and return only their conclusions, preserving the main conversation for
synthesis and implementation. (https://docs.anthropic.com/en/docs/claude-code /common-workflows) Anthropic also
describes parallel research as appropriate when separate investigation paths can proceed independently and the main agent
can synthesize the results afterward. (https://docs. anthropic.com/en/docs/claude-code/sub-agents)
Option B sacrifices the current conversational state and requires reconstruction after /clear.
Option C may reduce token usage, but isolated text matches cannot reliably reveal full execution paths, indirect calls, or test
coverage relationships.
Option D converts the current analysis into a single lossy summary and risks omitting details needed later.
Option A directly addresses the demonstrated context degradation while retaining a high-level coordinating thread. The
subagent prompts should be narrowly scoped and require concrete outputs such as file paths, uncovered branches,
call-chain evidence, and existing tests associated with each flow.
Official references/topics: Subagent Context Isolation; Parallel Research; Context Preservation; Coordinated Codebase
Analysis.
Page 3
