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Anthropic CCDV-F Exam Syllabus Topics:

Section	Weight	Objectives
Tools and Model Context Protocol (MCP)	10.6%	- MCP server development - Tool integration and usage
Model Selection and Optimization	16.8%	- Cost and token optimization - Latency and performance trade-offs - Claude model family characteristics
Security and Safety	8.1%	- AI application security - Guardrails and safety controls
Applications and Integration	33.1%	- SDK and third-party integration - Claude Messages API - Vision capabilities - Streaming and Batch API
Agents and Workflows	14.7%	- Workflow vs autonomous agents - Memory and context management - Claude Agent SDK usage - Agent architecture principles
Prompt and Context Engineering	11%	- Structured output handling - Prompt design and structuring - Context window management
Evaluation, Testing, and Debugging	2.6%	- Error handling and debugging - Output evaluation and validation
Claude Code	3.1%	- Claude Code configuration and usage

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Anthropic Claude Certified Developer-Foundations Sample Questions (Q62-Q67):

NEW QUESTION # 62
Your Claude application is hitting context window limits when processing long customer service transcripts.
A junior developer suggests increasing the temperature parameter to fix the issue.
How would you respond?

A. Increase the temperature parameter as the junior developer suggested and observe whether the context window issue resolves over the next several runs of the application in production.
B. Adjust the temperature parameter together with the max_tokens parameter, treating the combined adjustment as the team's mechanism for managing context window pressure during long-transcript processing.
C. Remove the system prompt entirely to make room for longer transcripts in each request, freeing up context window space the system prompt would otherwise consume.
D. Explain that temperature controls sampling randomness and is unrelated to context capacity, then address the context issue through summarization or chunking.

Answer: D

NEW QUESTION # 63
You are setting up a CI/CD pipeline for a new Claude application. The pipeline needs to run automated checks on every pull request before code can be merged.
The CI/CD checks would include...

A. Automated tests of the Claude integration only, with linting handled separately during local development on each developer's machine.
B. A full end-to-end production deployment on every pull request to catch all possible issues before any code is merged into the main branch.
C. Automated tests of the Claude integration, linting, and any other standard quality gates the team applies to its other services.
D. Automated linting and security scanning, with Claude integration testing handled manually during pre- release verification by a designated reviewer.

Answer: C

Explanation:
Option A treats a Claude application as production software subject to the same disciplined engineering controls as other services while adding tests for its model integration. CI should provide repeatable feedback before merge, including deterministic unit/integration tests, linting, type or static checks where applicable, security checks, and organization-specific quality gates.
Claude-specific behavior should also be tested systematically rather than reserved for manual release verification. Anthropic's evaluation guidance recommends task-specific test cases reflecting realistic inputs and edge cases, measurable success criteria, and automation whenever practical. Claude Code documentation also explicitly identifies CI automation, including GitHub Actions and GitLab CI/CD, as supported development workflows.
B is excessive because every pull request should not directly deploy to production merely to obtain end-to-end coverage; testing environments and staged deployment exist for that purpose. C makes linting dependent on individual developer discipline rather than a shared merge gate. D delays Claude integration verification until pre-release and makes a repeatable automated check manual.
Therefore, A provides comprehensive and consistent pull-request validation. Relevant Study Guide topics: CI
/CD, automated testing, integration testing, linting, evaluations, regression prevention, and quality gates.

NEW QUESTION # 64
Your team uses Claude Code across multiple repositories. You want the team's rules and general coding standards to apply to all repositories, and other rules to apply only to specific repositories. The team is currently duplicating instructions across every repository's CLAUDE.md file.
How would you address this?

A. Move all instructions to a separate documentation site that developers consult during Claude Code sessions across all repositories.
B. Stop using CLAUDE.md altogether and ask each developer to configure Claude Code manually for each project they work on.
C. Use a single repository's CLAUDE.md as the central source of truth and link to it from every other repository's CLAUDE.md file.
D. Use a CLAUDE.md hierarchy that scopes general standards broadly and project-specific context within each repository's local CLAUDE.md.

Answer: D

Explanation:
B is directly supported by both the supplied examination source and Claude Code's configuration model. The source marks the hierarchical CLAUDE.md approach as correct. Claude Code supports instructions at multiple scopes, allowing broadly applicable standards to be separated from project-specific context rather than duplicated across every repository.
Anthropic documents several CLAUDE.md scopes. Organization-managed instructions can apply broadly; user-level instructions in ~/.claude/CLAUDE.md apply across a user's projects; project instructions in .
/CLAUDE.md or ./.claude/CLAUDE.md provide repository-specific architecture, conventions, commands, and workflows. Claude Code loads applicable files according to the directory hierarchy, allowing broad instructions and more specific local instructions to coexist.
This arrangement improves maintainability because common coding standards are defined once at the appropriate scope, while each repository retains only the context unique to that project. A documentation website does not automatically inject rules into Claude Code context. C creates inconsistent manual configuration. D improperly couples unrelated repositories to one repository's configuration.
Relevant Claude Developer topics: Confia Management, CLAUDE.md hierarchy, organization scope, user scope, project scope, repository configuration, instruction inheritance, and configuration reuse .

NEW QUESTION # 65
Your team is debating how to manage the prompts used in your Claude application. Some prompts are checked into the code repository, some live in a separate configuration file, and some are constructed inline at runtime. The result is inconsistent, and a recent prompt change went out without code review.
What steps would you take?

A. Move all prompts out of version control to a separate spreadsheet that team members can edit freely as the application evolves over time.
B. Move all prompts to inline runtime construction so the team can update them quickly through a streamlined process outside the standard code review workflow.
C. Establish a single source of truth for prompts but keep change review optional, allowing developers to update prompts directly when changes are urgent.
D. Establish a single source of truth for prompts, version-control them alongside code, and require code review for prompt changes.

Answer: D

Explanation:
Option D treats prompts as production configuration and executable application behavior rather than informal text. A prompt modification can change output format, tool-selection behavior, policy enforcement, downstream compatibility, token usage, or model quality just as materially as an ordinary source-code change. Consequently, prompt changes need traceability, review, testing, and rollback.
A single source of truth prevents different application paths from silently using inconsistent prompt versions.
Version control provides an immutable history showing who changed the prompt, what changed, and which application release used it. Pull-request review enables peer assessment and automated evaluation before deployment. The same configuration-as-code principle appears throughout Claude Code's project configuration model, where shared project configuration is designed to live with version-controlled project artifacts.
A removes governance and creates an uncontrolled external state. B increases hidden runtime behavior and bypasses normal review. C solves fragmentation but leaves the exact governance failure described in the scenario-the ability to ship prompt changes without review.
Therefore, D provides reproducibility and controlled change management. Relevant Study Guide topics:
prompt versioning, configuration management, source control, change review, reproducibility, regression testing, and rollback.

NEW QUESTION # 66
Your Claude application validates structured output but has been treating validation failures as terminal errors. Each validation failure causes the entire user request to fail. The team wants to handle validation failures more gracefully.
How would you handle the validation failures?

A. Pass validation failures directly to downstream systems and let each downstream system decide how to handle the malformed output.
B. Treat validation failures as a recognized error path that triggers retry, repair, or fallback logic before failing the user request.
C. Tell users that validation failures are unavoidable and instruct them to perform manual accuracy checks before relying on outputs.
D. Disable output validation until the underlying cause of validation failures has been identified and addressed in a future release.

Answer: B

Explanation:
C converts validation failure from an uncontrolled terminal condition into a first-class recoverable error path . The supplied exam item identifies C as correct. If structured output does not meet the application's contract, it should never be forwarded as though valid, but immediate user-visible failure is also unnecessary when bounded recovery is possible.
A robust flow can retry generation, ask Claude to repair the malformed structure using the validation error as feedback, switch to an approved fallback path, or ultimately return a controlled failure if the retry budget is exhausted. The application must cap these recovery attempts to avoid unbounded loops.
Anthropic's Structured Outputs documentation explains that unconstrained model generation can produce parsing errors, missing fields, inconsistent types, or schema violations that otherwise require error handling and retries. Current Structured Outputs can eliminate many schema-level failures through constrained decoding, although exceptional conditions such as refusal or output truncation still require explicit handling.
A violates the validation boundary. B removes a protective control. D transfers an engineering reliability responsibility to end users.
Relevant Claude Developer topics: Claude App Design, structured output, validation, retries, repair loops, fallback logic, bounded recovery, error paths, and resilient downstream integration .

NEW QUESTION # 67
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