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Anthropic CCAR-F Exam Syllabus Topics:

Anthropic Claude Certified Architect - Foundations Sample Questions:

Question 1

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 submits two requests:
- Request A: "Rename the getUserData function to fetchUserProfile
everywhere it's used."
- Request B: "Improve error handling throughout the data processing
module - add try/catch blocks, meaningful error messages, and ensure
failures don't silently corrupt data."
For which request does specifying an explicit multi-phase workflow (such as analyze -> propose
-> implement with review) most improve outcome quality?

A. Both requests benefit equally
B. Request A, the function rename task
C. Neither request benefits significantly
D. Request B, the error handling task

Question 2

You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JSON schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
After your daily batch of 10,000 documents completes, 300 documents (3%) fail with context_length_exceeded errors. The results file identifies each failure by custom_id.
What is the most cost-effective approach to process these failures?

A. Reprocess the entire batch with prompt caching enabled to reduce the cost of retrying requests with identical system prompts.
B. Resubmit the entire 10,000-document batch using a model tier with a larger context window.
C. Increase the max_tokens parameter for the 300 failed documents and resubmit them in a new batch.
D. Resubmit only the 300 failed documents after chunking them into smaller pieces, and then combine the partial extractions.

Question 3

The coordinator provides detailed step-by-step instructions to the web search subagent, specifying exact search queries, source priorities, and date filters. Production monitoring reveals three issues: (1) the subagent reports "insufficient results" rather than trying alternative approaches when pre-specified searches fail, (2) research quality drops for emerging topics that don't match expected patterns, and (3) the subagent rarely surfaces valuable tangential sources.
What's the most effective way to improve subagent adaptability?

A. Implement a topic classification step where the coordinator categorizes requests as "well-defined" or "exploratory" and uses different instruction styles for each category.
B. Remove procedural details entirely, delegating with simple goals like "research X thoroughly" and relying on the subagent's general capabilities.
C. Add explicit fallback directives to the detailed instructions: "If specified searches yield fewer than N results, attempt alternative query formulations before reporting failure."
D. Specify research goals and quality criteria (coverage breadth, source diversity, recency) rather than procedural steps, letting the subagent determine its search strategy.

Question 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. Add a verification subagent that cross-references findings across sources, only passing claims to synthesis that are corroborated by at least two independent sources.
B. 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.
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.

Question 5

A legal department requires Claude to answer only when sufficient evidence exists in retrieved documents. What should Claude do if evidence is insufficient?

A. Clearly indicate insufficient information.
B. Search social media automatically.
C. Increase creativity.
D. Invent a likely answer.

Solutions: