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Anthropic Claude Certified Architect - Foundations Sample Questions:

1. You are using Claude Code to accelerate software development. Your team uses it for code generation, refactoring, debugging, and documentation. You need to integrate it into your development workflow with custom slash commands, CLAUDE.md configurations, and understand when to use plan mode vs direct execution.
You've documented API error handling conventions in a CLAUDE.md file at your project root, specifying that endpoint handlers should use a custom ApiError class. After several sessions, you notice Claude Code sometimes follows these conventions and sometimes uses generic try/catch blocks with string messages. The inconsistency appears random across different coding sessions. What's the most efficient first diagnostic step?

A) Run /memory to check which memory files are loaded and verify your CLAUDE.md is included.
B) Create path-specific rules in .claude/rules/handlers.md with YAML frontmatter scoping the error handling instructions to your API handler files.
C) Add a more detailed code examples to your CLAUDE.md showing the exact ApiError usage pattern for different endpoint types.
D) Search for conflicting instructions in ~/.claude/CLauDe.md or ~/.claude/rules/ that might override your project conventions.

2. Why is evaluation important after deploying a Claude application?

A) It reduces network latency.
B) It permanently changes model parameters.
C) It measures response quality against defined objectives.
D) It increases context size.

3. You are integrating Claude Code into your Continuous Integration/Continuous Deployment (CI/CD) pipeline. The system runs automated code reviews, generates test cases, and provides feedback on pull requests. You need to design prompts that provide actionable feedback and minimize false positives.
Your automated reviewer uses a single prompt covering security issues, API design, and business-logic correctness. Your evaluation suite shows strong recall for API-design findings at
82% but poor recall for business-logic edge cases in quiz scoring at 34%. When you add few-shot examples of logic bugs to the prompt, logic recall improves to 41%, but API-design recall drops to
68%.
How should you address this trade-off to improve detection across both categories?

A) Split the review into separate focused prompts--one for security and API design and another for business logic--each with dedicated examples, and then consolidate the findings before posting.
B) Provide the full repository as context instead of only the changed files and surrounding code, giving the model deeper visibility into business-logic patterns.
C) Replace the few-shot examples with a detailed checklist of specific logic edge cases to verify, such as division by zero in score calculations and boundary conditions in grading thresholds.
D) Upgrade to a more capable model tier because its stronger reasoning will handle both concern types in one prompt and eliminate the recall trade-off.

4. 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.
Your agent needs to insert a new helper function into the middle of a 150-line utility module, between two existing functions. The Edit tool fails because its old_string parameter cannot find unique text to match - the file has repetitive docstrings, variable names, and structural patterns.
What's the most reliable way to complete this insertion?

A) Use Bash to append the function definition to the end of the file using heredoc syntax
B) Use Edit with an extremely long old_string capturing 30+ lines of context to guarantee uniqueness
C) Use Edit's replace_all parameter to target a common pattern and embed the new function in the replacement text
D) Use Read to load the file, add the function at the appropriate location, then Write the updated file

5. The synthesis agent receives summarized findings from the web search and document analysis agents, then passes a consolidated summary to the report generator. During testing, you discover the generated reports make factual claims without proper citations - the report generator cannot attribute statements to their original sources because that metadata was lost during the summarization steps. What's the most effective approach to ensure proper source attribution in the final reports?

A) Have each agent output structured data separating content summaries from source metadata (URLs, document names, page numbers).
B) Instruct the synthesis agent to embed source references inline within its summary text using a consistent citation format.
C) Have the report generator query the web search agent to re-locate sources for claims in the final report.
D) Skip summarization and pass full raw outputs from web search and document analysis directly to the report generator.

Solutions: