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Anthropic CCAR-F Exam Syllabus Topics:

Section	Weight	Objectives
Context Management & Reliability	15%	- Production deployment considerations - Evaluation and reliability strategies - Managing context windows and information flow
Tool Design & MCP Integration	18%	- Model Context Protocol (MCP) concepts and integration - Designing effective tools for Claude applications - Tool safety, reliability, and usability
Claude Code Configuration & Workflows	20%	- Integrating Claude Code into development processes - Developer productivity workflows - Claude Code usage and configuration
Prompt Engineering & Structured Output	20%	- Structured output generation and validation - Improving Claude response quality and consistency - Prompt design strategies
Agentic Architecture & Orchestration	27%	- Designing agentic systems and workflows - Agent coordination and orchestration patterns - Selecting appropriate Claude architectures

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Anthropic Claude Certified Architect - Foundations Sample Questions (Q180-Q185):

NEW QUESTION # 180
After deploying automated code review, developers report that approximately 35% of flagged findings are false positives falling into consistent patterns: style suggestions contradicting team conventions, security warnings for patterns that are safe in your deployment context, and performance suggestions that would degrade your specific use case. You want to reduce false positives while maintaining the ability to catch genuine issues. Which approach best enables the model to generalize its judgment to novel code patterns it has not seen before?

A. Add instructions to your system prompt to "be conservative," "only flag definite issues," and
"consider that some patterns may be intentional."
B. Include few-shot examples in your prompt showing annotated code snippets that distinguish acceptable patterns from genuine issues in each category.
C. Implement post-processing that uses keyword matching to filter out findings containing terms such as "convention," "context-dependent," or "trade-off."
D. Create a comprehensive written specification of all patterns that should not be flagged, and then include the full documentation in the system prompt.

Answer: B

Explanation:
Option B teaches Claude the actual decision boundaries behind the team's judgments.
Contrasting examples can demonstrate why one apparent security pattern is acceptable within the organization's deployment controls while a superficially similar implementation is genuinely unsafe. The same technique can distinguish established style conventions from deviations and intentional performance trade-offs from accidental inefficiencies.
Anthropic identifies relevant, diverse, and clearly structured examples as one of the most reliable ways to improve Claude's accuracy and consistency. Its official prompting guidance recommends examples that closely mirror the production task while covering important variations and edge cases. This encourages generalization based on demonstrated criteria instead of memorization of prohibited wording.

NEW QUESTION # 181
A company is building its first production Claude application. Which principle should guide the initial deployment?

A. Optimize only for response speed.
B. Start with a simple design and iteratively evaluate improvements.
C. Disable monitoring to reduce costs.
D. Maximize prompt complexity immediately.

Answer: B

Explanation:
Successful enterprise AI projects usually begin with a straightforward architecture that can be measured and refined over time. Iterative evaluation enables teams to improve prompts, retrieval, safety controls, and user experience using real-world feedback rather than unnecessary initial complexity.

NEW QUESTION # 182
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, and Glob--and integrates with Model Context Protocol (MCP) servers.
You are building a security-scanning workflow.
When engineers need to locate every occurrence of a dangerous function such as eval() across a large codebase, which tool should the agent use for content searching?

A. Read the project's main entry file and follow import statements to trace where eval() might be used.
B. Use Bash to run ls -R | grep eval and search the recursively listed filenames.
C. Use Grep to search for the regular-expression pattern eval\( across all files in the codebase.
D. Use Glob with a pattern such as **/eval* to locate files, and then read each matching file.

Answer: C

Explanation:
Option B uses the tool designed to search file contents. Anthropic's Claude Code tools reference distinguishes Grep from Glob: Grep searches lines inside files, whereas Glob matches filenames and paths. Grep is built on ripgrep and accepts regular-expression patterns, so the opening parenthesis should be escaped as eval\( when the intention is to match the literal function call.
The search can return matching files, line numbers, and surrounding context without loading every file into the model's context window.

NEW QUESTION # 183
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.
Production monitoring shows that the research phase takes longer than expected. Analysis reveals that the coordinator invokes the web-search subagent, waits for its response, and then invokes the document-analysis subagent. These tasks are independent; neither requires the other's output.
How should you modify the system to run these subagents concurrently?

A. Switch both subagents to a Haiku-tier model instead of Sonnet to reduce their individual execution times.
B. Structure the coordinator to emit both Agent tool calls--formerly Task tool calls--in a single response rather than across separate conversation turns.
C. Create an asynchronous orchestration layer that starts separate coordinator-subagent pairs in parallel and aggregates their results.
D. Add instructions explaining the performance benefits of parallel execution and requesting that the coordinator invoke both subagents simultaneously.

Answer: B

Explanation:
Option B expresses both independent delegations in the same orchestration turn, allowing the runtime to execute them concurrently and return their results together. The current Claude Agent SDK calls the subagent-spawning capability the Agent tool; Task is its former name. Anthropic's SDK subagent documentation explicitly identifies parallel analysis as a primary subagent use case. Claude tool responses can also contain multiple tool_use blocks, enabling independent calls to be handled as one parallel group rather than as serial model turns.

NEW QUESTION # 184
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, and Glob-and integrates with Model Context Protocol (MCP) servers.
An engineer sees the unfamiliar error message SYNC_CONFLICT: entity version mismatch detected in production logs but does not know which of the 12 services in the codebase generates it. The engineer asks the agent to locate the responsible source code.
What exploration approach will find the responsible code most efficiently?

A. Use Grep to search for distinctive text from the error message, such as SYNC_CONFLICT or entity version mismatch , and then read the matching files to understand the context.
B. Use Glob to find files in directories commonly associated with error handling, such as errors , exceptions , or handlers , and then read every matching file.
C. Use Grep to locate every file importing the project's error-handling module, and then read those files to find custom error definitions.
D. Read the project's README and service-configuration files, and then systematically read source files in every service directory.

Answer: A

Explanation:
Option A begins with the strongest available discriminator: the exact production error text. Anthropic's tools reference specifies that Grep searches file contents and returns matching lines, enabling the agent to locate a literal error constant or message without loading the full repository. Searching both the stable code SYNC_CONFLICT and the distinctive phrase protects against cases where the identifier and displayed message are defined separately. The agent can then read only the matching files and follow the surrounding call path, error construction, and service ownership. Option B assumes that errors are stored in conventionally named directories, which may not hold in a twelve-service repository. Option C consumes substantial context before using the most direct evidence. Option D narrows the search to consumers of a known shared error module, but the message may be locally defined, generated from configuration, or wrapped by another component. Grep followed by targeted Read operations follows Anthropic's broader context-engineering principle of retrieving relevant source material just in time instead of placing an entire codebase into the active context.

NEW QUESTION # 185
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