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Anthropic CCA-F Exam Syllabus Topics:

Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions:

1. Claude produces occasional factual inaccuracies despite clear prompts. Which architectural enhancement is MOST effective?

A) Add grounding through trusted retrieval.
B) Remove examples.
C) Increase temperature.
D) Shorten responses only.

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

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

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

4. Production reviews reveal inconsistent handling of uncertainty in final reports. Sometimes conflicting subagent findings are synthesized into a single confident statement (losing nuance), while other times reports over-hedge with excessive qualifications (becoming unhelpful). When the web search agent returns "industry analysts estimate $50B market size (methodology varies)" and the document analysis agent returns "peer-reviewed study estimates $35B (±$7B, 95% CI)," the coordinator either picks one arbitrarily or produces vague statements like "the market may be
$35B-$50B depending on factors." What systematic approach best addresses this?

A) Implement a confidence calibration layer that normalizes subagent uncertainty expressions to standardized probability scores (0.0-1.0), then weight-average findings by their calibrated confidence.
B) Add a verification subagent that cross-references findings across sources, only passing claims to synthesis that are corroborated by at least two independent sources.
C) Instruct the synthesis agent to structure reports with explicit sections distinguishing well- established findings from contested ones, preserving original source characterizations and methodological context.
D) Configure subagents to only report findings meeting a high-confidence threshold, filtering uncertain information before it reaches the coordinator.

5. Compliance requires that refunds exceeding $500 must automatically escalate to a human agent
- this rule cannot be left to model discretion. Despite clear system prompt instructions, production logs show the agent occasionally processes high-value refunds directly (3% failure rate). How should you achieve guaranteed compliance?

A) Modify the refund tool to return an error with message "Amount exceeds policy limit - please escalate" when threshold is exceeded.
B) Add few-shot examples to the prompt showing correct escalation behavior at various refund amounts ($400, $500, $600).
C) Strengthen the system prompt with emphatic language: "CRITICAL POLICY: Refunds over $500 MUST trigger human escalation. NEVER process these directly."
D) Implement a hook to intercept tool calls; when the refund process amount exceeds $500, block it and invoke human escalation.

Solutions: