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Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions:
1. When configuring Claude Code for a new project, you want to establish team-wide TypeScript strict mode conventions, while keeping your personal Vim keybindings private. What is the correct architectural approach?
A) Option B
B) Option A
C) Option D
D) Option C
2. When utilizing Claude Code's built-in tools for codebase exploration and modification, which of the following usages represent correct tool selections? Choose 2 correct answers.
A) Using Grep to search for all occurrences of a deprecated function name across the src/ directory.
B) Using Write to fix a minor typo in an existing, heavily documented configuration file.
C) Using Glob to find all file paths that end in .test.tsx within the project.
D) Using Bash to execute find . "-name * .ts" to locate all TypeScript files.
3. To optimize a structured data extraction pipeline, you want to route only the most uncertain documents to your limited pool of human reviewers. Which method provides the most reliable calibration for this routing?
A) Escalating documents based solely on the aggregate accuracy metric of the entire pipeline.
B) Relying on the model's self-reported confidence score in the conversational text output.
C) Having the model output field-level confidence scores, and calibrating those thresholds using a labeled validation set.
D) Routing any document that triggers a validation-retry loop more than once.
4. Your automated extraction pipeline displays an impressive 96% aggregate accuracy on the dashboard. However, downstream teams complain about frequent errors when processing complex financial contracts. What monitoring methodology resolves this visibility gap?
A) Tracking accuracy per document type and field (stratified metrics) instead of relying solely on aggregate metrics.
B) Utilizing fork_session to isolate contract documents into separate evaluation workflows.
C) Increasing the overall sampling size randomly to capture a broader dataset.
D) Switching entirely to the Message Batches API to ensure complete asynchronous processing.
5. A pull request modifies 14 files across a stock tracking module. When analyzing all files in a single pass, Claude provides superficial comments, misses obvious bugs, and produces contradictory feedback. How should you redesign the multi-pass review architecture to resolve this?
A) Require developers to manually split large PRs into smaller submissions of 3-4 files before triggering the automated review.
B) Run three independent single-pass reviews on the full PR and only flag issues that achieve consensus across at least two runs.
C) Switch to a higher-tier model with a larger context window so all 14 files receive adequate attention in one pass.
D) Split the review into focused passes: analyze each file individually for local issues, followed by a separate integration-focused pass for cross-file data flow.
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: A,C | Question # 3 Answer: C | Question # 4 Answer: A | Question # 5 Answer: D |
