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Anthropic CCDV-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Agents and Workflows	14.7%	- Agent architecture principles - Memory and context management - Workflow vs autonomous agents - Claude Agent SDK usage
Topic 2: Tools and Model Context Protocol (MCP)	10.6%	- Tool integration and usage - MCP server development
Topic 3: Security and Safety	8.1%	- AI application security - Guardrails and safety controls
Topic 4: Claude Code	3.1%	- Claude Code configuration and usage
Topic 5: Evaluation, Testing, and Debugging	2.6%	- Error handling and debugging - Output evaluation and validation
Topic 6: Applications and Integration	33.1%	- SDK and third-party integration - Vision capabilities - Streaming and Batch API - Claude Messages API
Topic 7: Model Selection and Optimization	16.8%	- Claude model family characteristics - Cost and token optimization - Latency and performance trade-offs
Topic 8: Prompt and Context Engineering	11%	- Prompt design and structuring - Context window management - Structured output handling

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Anthropic Claude Certified Developer-Foundations Sample Questions (Q93-Q98):

NEW QUESTION # 93
Your team is preparing a new Claude application for production, and the product team has asked for a cost projection. The team needs to estimate the cost based on expected request volume, average input length, and average output length. How would you build the projection?

A. Build a cost model that combines expected request volume, average input tokens, average output tokens, the chosen model's pricing, and any caching benefits.
B. Build a cost model that combines expected request volume and average input tokens, treating output tokens as a small enough share of cost to leave out of the projection.
C. Build a cost model that uses the average per-request cost from a similar Claude application the team built last year, scaling that figure by expected request volume.
D. Build a cost model based on expected request volume and the chosen model's pricing, treating average input and output token counts as variables to be estimated post-launch.

Answer: A

Explanation:
Option B is correct because Claude API cost is fundamentally driven by usage volume multiplied by the token economics of the selected model and features. A credible projection must therefore estimate requests, average input tokens per request, average output tokens per request, applicable input/output prices, and expected prompt-cache behavior. Anthropic's pricing documentation treats input, output, cache writes, and cache reads as distinct billable categories, with cache hits priced below standard input processing.
A practical forecast can be modeled as: request volume × expected per-request input cost plus request volume
× expected per-request output cost, adjusted for cache-write/read rates and any other applicable pricing modifiers. The model should also include ranges for variance rather than only one point estimate, because token lengths and cache-hit rates will fluctuate in production.
Option A assumes a historical application has the same token profile and pricing, which may be false. Option C omits output tokens even though output pricing can be a material share of cost. Option D postpones the very token estimates the product team needs before launch.
Therefore, B is the complete projection method. Relevant Study Guide topics: token accounting, model pricing, prompt caching, cost forecasting, workload sizing, and production economics.

NEW QUESTION # 94
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. 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.
B. Automated tests of the Claude integration only, with linting handled separately during local development on each developer's machine.
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 # 95
Your Claude agent's hooks are currently triggered for every action, which slows down the agent significantly even when actions pose no risk. The team wants to scope hooks more carefully.
How would you scope the hooks?

A. Disable all hooks while the team re-scopes them, treating the period of no hook enforcement as a temporary state during the re-scoping work.
B. Disable the agent during peak hours so the hook overhead does not slow the application down during the busiest periods of the day across the application's operation.
C. Replace hooks with system prompt instructions on the grounds that prompt instructions can produce the same enforcement effect that hooks produce on the agent's actions.
D. Scope hooks to only the high-risk actions, such as destructive operations or sensitive data access, and remove hooks from low-risk actions to balance safety with performance.

Answer: D

Explanation:
Option A correctly applies selective enforcement. Claude Code hooks can execute automatically at lifecycle events such as PreToolUse, and matchers or conditions can narrow exactly which operations trigger a hook.
Anthropic's hook reference demonstrates this pattern by applying a PreToolUse hook specifically to destructive shell operations rather than indiscriminately processing every command. The documentation notes that if the matcher or conditional expression does not match, the handler is skipped, avoiding unnecessary process-spawn overhead.
That architecture is particularly appropriate for costly or security-sensitive checks. High-risk events- destructive file operations, privileged commands, production changes, or access to sensitive resources-can receive deterministic pre-execution enforcement while routine low-risk actions proceed without additional hook latency.
B creates an avoidable period in which safeguards disappear entirely. C reduces application availability without addressing the actual source of overhead. D is technically weaker because system-prompt instructions influence model behavior but are not equivalent to deterministic lifecycle interception capable of blocking execution.
Therefore, hooks should be scoped using event types, matchers, and conditions according to risk. Relevant Claude Developer topics are Claude Code hooks, agent construction, tool governance, deterministic controls, permission boundaries, safety/performance tradeoffs, and lifecycle interception.

NEW QUESTION # 96
Your Claude application's error handling currently logs every API error with the same severity level. The team wants to differentiate between errors that should page an on-call engineer and errors that should be logged for later review. How would you structure the error handling?

A. Disable logging for any error that does not page, treating non-paging errors as not worth recording for later review either.
B. Page on every error, on the grounds that paging guarantees that no error is missed by the team during normal operation across the application's lifecycle.
C. Categorize errors by severity based on impact and recoverability, then route each category to the appropriate channel for paging or logging.
D. Log every error with the same severity, on the grounds that differentiating severity adds complexity that does not pay off in most application setups over time.

Answer: C

Explanation:
Option C is correct because operational error handling should distinguish failures by impact, urgency, and recoverability. Anthropic's API documentation already separates error conditions by type and documents different recovery behaviors. For example, transient connection failures, rate limits, and many 5xx errors are automatically retried by official SDKs with exponential backoff, while other failures require application correction or investigation. Treating all of those conditions as the same operational severity creates either alert fatigue or missed incidents.
A production design should classify errors using criteria such as user impact, data integrity risk, security exposure, persistence after retry, affected request volume, and whether automatic recovery succeeds. High- severity failures that threaten service availability or correctness can page the on-call engineer. Recoverable or isolated failures can be logged with structured metadata, request IDs, and metrics for later review.
Option A pages on everything and quickly makes alerts noisy. Option B destroys observability for non-paging errors. Option D preserves the original defect by assigning the same severity to every condition. Therefore, C follows sound Claude application operations. Relevant Study Guide topics: API errors, retries, observability, incident response, severity classification, structured logging, and production operations.

NEW QUESTION # 97
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. Disable output validation until the underlying cause of validation failures has been identified and addressed in a future release.
D. Tell users that validation failures are unavoidable and instruct them to perform manual accuracy checks before relying on outputs.

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 # 98
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