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Anthropic CCAR-F Exam Syllabus Topics:

Anthropic Claude Certified Architect - Foundations Sample Questions:

1. 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.
The automated review consistently flags patterns your team uses intentionally-force-unwrapping optionals in test files, using large coordinator classes that follow your established architecture, and importing internally maintained modules marked as deprecated in the public SDK. Developers dismiss approximately 30% of all findings as project-specific false positives.
Which approach prevents the model from generating these findings in the first place by supplying the project' s conventions as persistent context during every review?

A) Document the team's accepted patterns and intentional conventions in the project's CLAUDE.md file so the model receives this context during every review.
B) Configure the review to analyze only the changed lines in the diff without the surrounding file context, reducing the amount of code the model evaluates.
C) Build post-processing keyword filters that suppress findings containing terms such as "force unwrap,"
"large class," or "deprecated import" before results reach developers.
D) Have developers add inline suppression comments at flagged lines and preprocess diffs to exclude suppressed lines before sending code to the model.

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, 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) Single comprehensive prompt-include all three instructions in one prompt and let the model handle all three aspects simultaneously.
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) Routing-classify each PR by type (feature, bugfix, refactor) first, then route to different review prompts optimized for that category.
D) 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.

3. 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.
Your extraction system processes two document types: standard monthly reports, which are archived after processing, and urgent exception reports, which must trigger business alerts within 30 minutes of receipt.
Both use the same JSON schema. You want to minimize API costs while meeting the latency requirements.
How should you architect the processing pipeline?

A) Submit all documents to the real-time Messages API to ensure consistent processing latency across document types.
B) Queue all documents and submit hourly batches, flagging urgent documents for expedited handling when batch results return.
C) Route standard reports to the Message Batches API for 50% cost savings, and route urgent exception reports to the real-time Messages API.
D) Submit all documents to the Message Batches API with custom_id values for tracking. When results arrive, immediately process urgent documents and trigger delayed alerts for exceptions.

4. Your code-review prompts include both implementation changes and the corresponding test file, but the review comments fail to identify untested code paths. The model correctly flags functions that have no tests at all, but it fails to recognize when conditional branches or error-handling paths within tested functions lack coverage. What is the most effective way to improve branch-level gap detection without overcomplicating the pipeline?

A) Implement a two-pass pipeline in which one model call extracts all conditional branches and another cross-references them against test assertions.
B) Interleave the implementation and tests in the prompt, presenting each function immediately before its test cases.
C) Include few-shot examples showing code with an uncovered branch and the corresponding review comment identifying the missing test case.
D) Add explicit instructions requiring Claude to enumerate every conditional branch and exception path, then verify that each path has a corresponding test assertion.

5. 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 deployment, you find that 12% of extractions contain semantic errors that pass JSON Schema validation-for example, a duration such as "30 minutes" is incorrectly placed in an ingredient-quantity field.
Human reviewers have the capacity to check only 20% of extractions.
Which approach most effectively allocates reviewer attention?

A) Review all extractions from documents with formatting anomalies, such as unusual layouts or mixed content types.
B) Have the model output field-level confidence scores, and then calibrate review thresholds using a labeled validation set.
C) Prioritize the review of all extractions where required fields are empty or explicitly marked as not found.
D) Randomly sample 20% of extractions for review, using corrections to track accuracy and identify error patterns.

Solutions: