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Anthropic CCAR-F Exam Syllabus Topics:

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

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Anthropic Claude Certified Architect - Foundations Sample Questions (Q66-Q71):

NEW QUESTION # 66
Your pipeline includes a release-notes generation step that classifies and summarizes approximately 200 commits at the end of each weekly release cycle. Each commit is currently sent as a separate Messages API call using a Sonnet-tier Claude model. The release notes are not needed until the following morning, so results have approximately 12 hours of acceptable latency. Your team needs to reduce per-token API cost for this step while keeping the same model and prompts, with no change to the model tier or output quality. Which approach satisfies all these constraints?

A. Switch the summarization calls from the Sonnet-tier model to a Haiku-tier model to take advantage of Haiku's lower per-token rates.
B. Issue the 200 Messages API requests in parallel using concurrent connections, because concurrency lowers the per-token price charged by the API.
C. Concatenate all 200 commit messages into a single Messages API request and have the model return all summaries in one response, because fewer requests always reduce total token cost.
D. Submit the 200 requests to the Message Batches API with unique custom_id values and retrieve the results after the batch finishes, which applies a 50% discount to all input and output tokens.

Answer: D

Explanation:
Option C preserves each commit's existing model, prompt, and independent processing structure while applying Anthropic's reduced batch pricing. The official Message Batches documentation states that batch processing reduces input and output token costs by 50%. Requests are processed independently and asynchronously, and each response can be correlated with its original commit through a unique custom_id.
This workload is a strong batch candidate because it contains many independent requests and does not require an immediate response. Most batches complete within one hour, although processing can continue for up to
24 hours. The pipeline must therefore tolerate asynchronous completion and retrieve the results when processing finishes.
Option A changes the request architecture, increases the risk of context or output-limit failures, and makes individual retry and result association more difficult. Reducing request count does not inherently reduce per- token pricing. Option B improves throughput but concurrency does not change token rates. Option D would reduce cost but violates the explicit requirement to retain the same model tier. The Message Batches API is the only option satisfying every stated constraint.

NEW QUESTION # 67
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 CI pipeline performs security-focused code reviews on approximately 50 pull requests daily, currently costing $150 per day using the synchronous API. Reviews are non-blocking--developers merge after tests pass and address findings in follow-up commits. You are evaluating the Message Batches API for its 50% cost reduction.
What factor most determines whether batch processing is appropriate for this use case?

A. Whether you can structure each review as a single request without multi-turn refinement.
B. Whether review feedback arriving up to 24 hours after pull-request creation remains actionable.
C. Whether reducing per-review latency from 30?0 seconds to near-instant matters for your workflow.
D. Whether your result-processing system can handle reviews arriving in a different order from the order in which they were submitted.

Answer: B

Explanation:
Option C identifies the fundamental trade-off introduced by batch processing: lower cost in exchange for asynchronous completion and potentially substantial latency. Anthropic states that most Message Batches complete within one hour, but results may become available only when all requests finish or after 24 hours, whichever occurs first. Therefore, the workflow must remain useful even if security findings arrive considerably later than they would through synchronous requests.
The reviews are explicitly non-blocking, so batch processing can be suitable if developers can still act on delayed findings through follow-up commits.

NEW QUESTION # 68
Your agent is handling a billing dispute. After calling get_customer and lookup_order, it identifies that the dispute involves a promotional pricing error requiring manager approval - beyond the agent's authorization level. How should the workflow handle this mid-process escalation?

A. Persist the complete conversation and tool response history to a database, then call escalate_to_human with a reference ID.
B. Attempt the refund with process_refund anyway, escalating only if the system rejects the transaction.
C. Call escalate_to_human passing only the customer's original message.
D. Compile a structured handoff with customer details, order info, and the identified issue before calling escalate_to_human.

Answer: D

Explanation:
A structured handoff containing the customer details, order information, and the specific issue ensures the human agent has all relevant context to act immediately. This approach avoids delays or repeated clarification and preserves continuity when authority limits prevent the agent from completing the task.

NEW QUESTION # 69
You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JavaScript Object Notation (JSON) schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Your system has been running for 3 weeks and human reviewers have corrected 847 extractions. Analysis reveals a recurring pattern: when recipes use informal measurements like "a handful" or "a splash," the model either invents specific amounts or leaves fields empty-accounting for 23% of all corrections.
How should you use this feedback to improve extraction accuracy?

A. Add few-shot examples to your prompt demonstrating correct handling of informal measurements- extracting them verbatim rather than converting or omitting them.
B. Update your JSON schema to add a "measurement_type" enum field (precise/informal).
C. Implement a post-processing layer that uses pattern matching to detect informal measurement phrases in source text and automatically populate values when the extraction is empty.
D. Fine-tune the model on the 847 corrected extractions.

Answer: A

Explanation:
The reviewer corrections have exposed a narrow and repeatable interpretation failure. The desired policy is clear: informal measurements are valid source values and must be preserved verbatim rather than normalized into invented quantities or treated as missing. This behavior can be communicated efficiently through targeted few-shot examples.
Anthropic recommends examples for demonstrating expected behavior and improving consistency. Examples can pair source phrases such as "a handful of spinach," "a splash of vinegar," and "a pinch of salt" with outputs that retain handful , splash , and pinch exactly. Additional counterexamples can show that Claude must not convert these phrases into grams, millilitres, or estimated serving quantities. ( https://docs.anthropic.
com/en/docs/about-claude/use-case-guides/ticket-routing )
Option A introduces a substantially heavier training workflow for a problem that can be addressed directly through the prompt. Option C creates a parallel extraction mechanism based on pattern matching; it will be brittle across linguistic variations and may populate a value without understanding its relationship to the correct ingredient. Option D adds useful classification metadata, but it does not instruct Claude to preserve the original measurement instead of inventing or omitting it.
The revised prompt should combine an explicit verbatim-extraction rule with several varied examples derived from the corrected cases, followed by regression evaluation against the identified failure set.
Official references/topics: Few-Shot Examples, Feedback-Driven Prompt Improvement, Verbatim Extraction, Regression Evaluation.

NEW QUESTION # 70
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 asks your agent to add comprehensive tests to a legacy codebase with 200 files and minimal existing test coverage. The engineer hasn't specified which modules to prioritize.
How should the agent decompose this open-ended task?

A. Use Glob and Grep to map codebase structure, identify heavily-coupled modules, create a prioritized plan for high-impact areas, and revise as dependencies are discovered.
B. Create a fixed testing schedule upfront based on directory structure, allocating equal effort to each top-level directory regardless of code complexity or business importance.
C. Start writing tests for the first module alphabetically, using test failures and imports to discover related files organically.
D. Systematically read all 200 files to create a complete function inventory before writing any tests, ensuring the testing plan accounts for every function before beginning.

Answer: A

Explanation:
The task is open-ended because neither the critical modules nor the required testing sequence is known in advance. The agent should first use lightweight discovery tools to map the repository, locate existing tests, identify central modules, and determine which components have high fan-in, business significance, complex branching, or extensive external dependencies. It can then produce an initial risk-based testing plan and refine it as new dependency information appears.
Anthropic distinguishes predefined workflows from agents that dynamically control their processes and tool usage. Agents are appropriate when the required steps cannot be reliably hardcoded and must adapt to environmental evidence. During execution, they should obtain ground truth through tool results and use that feedback to determine subsequent actions.
Anthropic also identifies orchestrator-worker designs as suitable for complex coding and search tasks where the necessary subtasks depend on what the investigation reveals.

NEW QUESTION # 71
......

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