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Anthropic CCAR-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Agentic Architecture & Orchestration	27%	- Agent coordination and orchestration patterns - Selecting appropriate Claude architectures - Designing agentic systems and workflows
Topic 2: Tool Design & MCP Integration	18%	- Tool safety, reliability, and usability - Model Context Protocol (MCP) concepts and integration - Designing effective tools for Claude applications
Topic 3: Claude Code Configuration & Workflows	20%	- Claude Code usage and configuration - Developer productivity workflows - Integrating Claude Code into development processes
Topic 4: Prompt Engineering & Structured Output	20%	- Structured output generation and validation - Improving Claude response quality and consistency - Prompt design strategies
Topic 5: Context Management & Reliability	15%	- Production deployment considerations - Evaluation and reliability strategies - Managing context windows and information flow

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Anthropic Claude Certified Architect - Foundations Sample Questions (Q102-Q107):

NEW QUESTION # 102
An architect needs Claude to produce structured JSON that downstream software can parse automatically. Which prompting strategy is BEST?

A. Increase maximum output tokens only.
B. Ask Claude to "respond normally."
C. Use multiple unrelated examples.
D. Explicitly specify the required JSON schema and formatting rules.

Answer: D

Explanation:
Providing an explicit schema clearly communicates the expected output format. Claude follows structured instructions effectively, making JSON generation significantly more reliable when field names, nesting, and formatting expectations are precisely described.

NEW QUESTION # 103
Your automated review calls the Claude API for each pull request, using tool_use with a report_findings tool that returns a JSON array of finding objects. Each object contains file_path, line_number, severity, category, and description. During testing on a large pull request touching more than 30 files, the response reaches the max_tokens limit and is truncated in the middle of the JSON, causing your pipeline's parser to fail. What is the most effective way to handle this?

A. Switch from tool_use to prompting Claude to return findings as a Markdown list.
B. Add retry logic that detects truncated JSON and resends the request with instructions to report only critical- and high-severity findings.
C. Increase max_tokens to the model's maximum and instruct Claude to keep each finding description under 50 words.
D. Split the review into multiple API calls that each analyze a subset of the changed files, and then merge the resulting findings arrays.

Answer: D

Explanation:
Option A reduces the maximum output required from any single response while preserving the structured schema and complete severity range. The pipeline can partition files into coherent groups, execute bounded reviews, validate each returned array, and merge and deduplicate findings using stable fields such as file path, line number, category, and description.
Anthropic's stop-reason documentation confirms that max_tokens means generation reached the configured output limit and the response must be treated as incomplete. Structured output constraints can guarantee schema-valid generation when completion succeeds, but they cannot create unlimited output capacity. A large findings array can still exceed the available token budget.
Option B may postpone the failure but provides no durable guarantee for still-larger pull requests, and aggressively shortening descriptions may eliminate necessary evidence. Option C abandons machine- validated structure without reducing the amount of generated content. Option D deliberately suppresses medium- or low-severity findings and repeats an oversized request rather than addressing its scope.
Partitioning establishes predictable output bounds, supports targeted retries, retains every required finding category, and prevents a single truncated response from invalidating the complete review.

NEW QUESTION # 104
An organization wants predictable outputs for automated invoice classification. Which API parameter should generally be LOWER?

A. Temperature
B. Context window
C. Max tokens
D. Prompt length

Answer: A

Explanation:
Lower temperature reduces randomness during token selection, producing more consistent and repeatable outputs. This is especially valuable in structured enterprise workflows where deterministic behavior is preferred over creative variation.

NEW QUESTION # 105
Production logs reveal inconsistent error handling: when lookup_order fails, the agent sometimes retries 5+ times (wasteful when the order ID doesn't exist), sometimes escalates immediately (premature for temporary network issues), and sometimes asks users for clarification (inappropriate when the issue is a backend permission error). Investigation shows your MCP tool returns uniform error responses: {"isError": true, "content": [{"type": "text", "text": "Operation failed"}]}. The agent cannot distinguish between error types. What's the most effective improvement?

A. Add few-shot examples to the system prompt demonstrating how to interpret error message patterns and select appropriate responses for each.
B. Implement retry logic with exponential backoff in your MCP server for all errors, returning to the agent only after retries are exhausted.
C. Enhance error responses with structured metadata: include errorCategory (transient/validation/permission), isRetryable boolean, and a description of what caused the failure.
D. Create an analyze_error MCP tool the agent calls after any failure to determine the error category and recommended action.

Answer: C

Explanation:
Structured error metadata gives the agent the information needed to choose the right recovery path. By distinguishing transient, validation, and permission failures, and by explicitly indicating whether retry is appropriate, the agent can avoid wasteful retries, premature escalation, and unnecessary user clarification.

NEW QUESTION # 106
Your send_notification tool calls third-party messaging APIs. When these services time out during delivery, you cannot determine whether the message was actually sent. Currently, the tool returns is_error: true with a generic "Notification failed" message for all timeouts. Production monitoring reveals agents automatically retry these failures, frequently causing users to receive duplicate notifications. How should you modify the error response?

A. Return is_error: true with a message communicating uncertainty: "Timeout - status unknown.
Message may have been sent. Avoid retry."
B. Return is_error: true with a message encouraging retry: "Delivery service temporarily unavailable.
Please retry the notification."
C. Return is_error: true with the original message content echoed back.
D. Return is_error: true with a structured field retry_safe: true for timeouts, distinguishing them from permanent failures that should not be retried.

Answer: D

Explanation:
Adding a structured retry_safe flag distinguishes uncertain, potentially idempotent errors from permanent failures. This allows the agent to make informed decisions about automatic retries, preventing duplicate notifications while maintaining reliability for recoverable errors.

NEW QUESTION # 107
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