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Anthropic CCAR-F Exam Syllabus Topics:

Anthropic Claude Certified Architect - Foundations Sample Questions:

1. In production, final reports frequently contain claims without proper source attribution. Investigation shows that the web-search and document-analysis agents correctly attach citations to their outputs, but the synthesis agent loses track of which sources support which conclusions when combining findings. What is the most effective architectural change?

A) Have the coordinator insert source-identifier prefixes into prose before every handoff and parse those prefixes during report generation.
B) Add a verification step in which the report generator uses semantic-similarity matching against the original sources to reconstruct claim provenance.
C) Retain complete transcripts of every subagent interaction and add a citation-resolution agent that analyzes those logs before report generation.
D) Require every subagent to return structured claim-to-source mappings that the synthesis agent must preserve and merge when combining findings.

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, and Glob-and integrates with Model Context Protocol (MCP) servers.
An engineer who recently joined the team asks the agent to explain the authentication and authorization architecture before making security improvements. The codebase contains more than 800 files across multiple services.
What exploration strategy will most effectively build understanding while respecting context limits?

A) Use Grep to locate authentication entry points, read those files, and then follow imports and function calls incrementally to map the authentication flow.
B) Read all files containing auth , login , permission , or token in their filenames or contents.
C) Read all CLAUDE.md and README files first, and then ask the engineer to identify the 10-15 most important authentication files.
D) Launch parallel subagents to explore every service simultaneously, and then synthesize their findings into an architectural overview.

3. Your pipeline reviews approximately 200 database-migration scripts daily using the Message Batches API.
Each request includes a shared 8,000-token system prompt containing migration-review guidelines and schema documentation, followed by an individual migration script. You added cache_control breakpoints to the shared system prompt in every request, but monitoring shows cache-hit rates of only 32%, with misses concentrated among requests processed later in the batch window. Which change addresses the root cause without adding sequential-processing latency?

A) Configure the cache breakpoints to use the extended one-hour TTL instead of the default five-minute TTL.
B) Move the cache_control breakpoint from the shared system prompt to each migration script so similar code patterns can be reused.
C) Split the 200 requests into ten sequential batches of 20, submitting each batch only after the previous batch completes.
D) Add cache-prewarming requests with max_tokens: 0 at the beginning of every batch.

4. You are building a customer support resolution agent using the Claude Agent SDK. The agent handles high- ambiguity requests like returns, billing disputes, and account issues. It has access to your backend systems through custom Model Context Protocol (MCP) tools ( get_customer , lookup_order , process_refund , escalate_to_human ). Your target is 80%+ first-contact resolution while knowing when to escalate.
Anthropic's tool use documentation states: "Write instructive error messages. Instead of generic errors like
'failed', include what went wrong and what Claude should try next." A billing dispute agent uses lookup_order , which catches all exceptions and returns a tool_result with is_error: true and the message
"Tool execution failed". Monitoring shows two failure modes: the agent retries the identical call until hitting the turn limit, or it immediately calls escalate_to_human without trying alternative tools.
Which change follows the documented recommendation and gives Claude the information it needs to select the correct recovery action for each error type?

A) Add an error classification step in the agentic loop that intercepts tool errors before Claude sees them, then routes to hardcoded retry or escalation logic.
B) Remove is_error: true and return the error details as normal tool content, so Claude reasons about the response as data rather than treating it as a flagged failure condition that biases retry behavior.
C) Implement retry logic with exponential backoff inside each tool implementation so transient errors are resolved transparently within the tool before any failure result is surfaced to Claude in the agentic loop.
D) Return error-type-specific messages with is_error: true , e.g., "Order not found-try get_customer to search by phone" for data errors and "Database timeout (transient)-retry should succeed" for infrastructure errors.

5. 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 code review assistant needs to analyze pull requests and provide feedback on three aspects: code style compliance, potential security issues, and documentation completeness. Each aspect requires reading files, running analysis tools, and generating a report section. The review process follows the same three-step workflow for every PR.
Which task decomposition pattern is most appropriate for this workflow?

A) Routing-classify each PR by type (feature, bugfix, refactor) first, then route to different review prompts optimized for that category.
B) Prompt chaining-break the review into sequential steps where each aspect (style, security, documentation) is analyzed separately, with outputs combined in a final synthesis step.
C) Orchestrator-workers-have a central LLM analyze each PR to dynamically determine which checks are needed, then delegate to specialized worker LLMs for each identified subtask.
D) Single comprehensive prompt-include all three instructions in one prompt and let the model handle all three aspects simultaneously.

Solutions: