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Anthropic CCDV-F Exam Syllabus Topics:

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

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Anthropic Claude Certified Developer-Foundations Sample Questions (Q63-Q68):

NEW QUESTION # 63
A new Claude model release includes performance improvements for several reasoning tasks but has changed the format of its responses to system prompts that use multi-section instructions. Your application uses multi- section system prompts heavily. Initial evaluation on the application's actual workload shows the new model performs 8 percent better on reasoning tasks but produces malformed output on roughly 3 percent of requests because of the format change. The team is debating whether to upgrade.
How would you decide?

A. Upgrade immediately, because the 8 percent reasoning improvement outweighs the 3 percent malformed output rate across the application's typical request distribution.
B. Adapt the application's system prompt to the new model's format expectations and re-evaluate, then upgrade only if the adapted prompt eliminates the malformed output while preserving the reasoning improvements.
C. Upgrade and add a downstream validation step that catches the 3 percent malformed output before it reaches users, treating the validation step as the team's mitigation for the format change.
D. Stay on the previous model permanently to avoid the malformed output rate and any future format changes that subsequent model releases might introduce in the application.

Answer: B

Explanation:
The supplied examination page marks B . The scenario already demonstrates why model upgrades must be treated as evaluated software changes rather than automatic replacements: the new model improves one metric while introducing a regression in another.
Anthropic's official model-selection guidance recommends creating benchmark tests specific to the application's use case, testing models with the application's actual prompts and data, comparing response quality and edge-case performance, and weighing performance against operational tradeoffs. Therefore, the correct action is to adapt the multi-section system prompt to the new model's behavior and repeat the evaluation. Only after the formatting regression is eliminated-or reduced below an explicitly acceptable threshold-should the upgrade proceed.
A incorrectly assumes that an 8% reasoning improvement numerically compensates for a 3% malformed- output rate; these metrics measure different consequences and cannot simply be subtracted. C treats the known incompatibility only downstream instead of first correcting the prompt/model interaction. D permanently rejects future improvement and is inconsistent with controlled lifecycle evolution.
The engineering principle is migration through regression testing and adaptation , not blind upgrading or permanent version avoidance.
Relevant Claude Developer topics: Systems Life Cycle, model migration, regression evaluation, prompt adaptation, compatibility testing, deployment gates, and continuous evolution .

NEW QUESTION # 64
You are setting up a Claude application that requires API keys for several external services.
What is the best way to store the keys?

A. Put the keys in the application's configuration file and check the configuration file into the team's repository alongside the rest of the source code.
B. Email the keys to each developer as needed and have each developer paste the keys into their local environment when they begin working on the application's code.
C. Use a single shared key across all external services, so any developer working on the application can find the keys easily during development.
D. Store the keys in a secrets manager or environment-specific configuration that is not checked into source code, and load them at runtime.

Answer: D

Explanation:
Option B follows standard secrets-management practice and Anthropic's explicit guidance for API credentials.
API keys are authentication secrets and should not be embedded in source code or committed to repositories.
Anthropic's authentication documentation explicitly recommends storing API keys in a secrets manager, rotating them periodically, and revoking credentials suspected of compromise. Anthropic SDKs can load Claude credentials from environment variables such as ANTHROPIC_API_KEY, allowing the secret to be injected at runtime instead of compiled into the application.
The same principle applies to external service credentials used by a Claude application. Development, staging, and production should normally receive independently scoped credentials through the deployment environment or secret-management infrastructure.
A creates a high-probability credential leak because repository history can retain secrets even after a later deletion. C violates isolation and least privilege by sharing credentials across services and users. D uses email as an uncontrolled secret-distribution channel and creates inconsistent manual configuration.
Therefore, B supplies credentials only at runtime while keeping them outside source control and enabling rotation and environment-specific access. Relevant Study Guide topics: API-key management, secrets managers, environment configuration, credential rotation, least privilege, and secure configuration.

NEW QUESTION # 65
Your team uses several plugins across multiple Claude applications, and a recent plugin update introduced a regression. The team had not been tracking plugin versions, so the team cannot easily identify which version was previously working. How would you address this?

A. Add explicit plugin version tracking to the project's configuration so the team can identify, pin, and upgrade plugin versions deliberately.
B. Upgrade every plugin to the latest version on a regular cadence to keep version drift small, on the grounds that drift contributes to regression risk.
C. Stop using all plugins until the team can rebuild equivalent functionality directly into the application code, treating plugin avoidance as a way to remove version-related risk.
D. Treat plugins as untrackable third-party code and rely on plugin authors to communicate breaking changes when they happen, with no internal version tracking.

Answer: A

Explanation:
Option B is correct because plugin dependencies and releases must be versioned deliberately if the team wants reproducible behavior and a reliable rollback path. Claude Code's plugin documentation supports explicit semantic versions in plugin manifests and marketplace entries, and the plugin system uses the resolved version as part of update detection and caching. Version constraints can also keep dependent plugins on a tested compatible range until the team intentionally upgrades.
The incident happened because the team could not determine which plugin version had previously worked.
Explicit tracking solves that directly: record the version in project configuration, pin known-good releases where stability matters, test upgrades, and change versions through reviewed configuration updates. This creates traceability and makes regression isolation much faster.
Option A removes useful extension functionality instead of managing it. Option C treats third-party change as uncontrollable even though the platform provides version-management mechanisms. Option D automatically moving everything to the latest release increases change frequency and can reproduce the same regression problem.
Therefore, B is the correct configuration-management response. Relevant Study Guide topics: Claude Code plugins, semantic versioning, dependency constraints, reproducible configuration, controlled upgrades, rollback, and configuration management.

NEW QUESTION # 66
Your Claude agent has access to a tool that retrieves customer records. A teammate has noticed that the agent occasionally calls the tool with arguments the schema does not declare, and the tool's downstream service returns an error each time. The teammate proposes loosening the schema so the tool accepts whatever arguments the model produces.
How would you respond?

A. Loosen the schema as the teammate proposed so the downstream service receives every call the agent makes during normal operation.
B. Keep the schema strict, validate arguments before dispatching, and return a structured error so the agent can retry.
C. Remove the schema entirely and rely on the downstream service to reject invalid calls, treating the downstream service as the team's primary enforcement layer.
D. Add a system prompt instruction telling the model to produce schema-conforming arguments, treating the prompt instruction as the primary mechanism for keeping the agent's tool calls valid.

Answer: B

Explanation:
The supplied question identifies B as the intended answer. Tool schemas are contracts between Claude and executable application code. When the downstream service accepts only a defined set of arguments, relaxing that schema merely shifts invalid data farther into the system and increases runtime failures.
Anthropic's current tooling provides an even stronger implementation of this principle through strict tool use
. Setting strict: true constrains tool inputs to the declared JSON Schema, preventing undeclared properties, missing required values, and incompatible parameter types where the supported schema subset is used.
Anthropic explicitly recommends strict tool use for validated parameters, type-safe function calls, and reliable agentic workflows.
In a non-strict or legacy implementation, the application should still validate arguments before dispatch and convert invalid calls into structured tool errors that Claude can interpret and potentially correct. A prompt instruction can reinforce behavior, but it should not replace deterministic validation. C and D weaken the system boundary and knowingly send invalid calls downstream.
Therefore, maintain the contract rather than adapting the contract to malformed model output.
Relevant Claude Developer topics: Agent Construction, tool schemas, strict tool use, JSON Schema, parameter validation, structured errors, retry behavior, and defensive execution boundaries .

NEW QUESTION # 67
The Anthropic API deprecated a request parameter that your Claude application uses in approximately 40 places across the codebase. The deprecation notice gives a six-month window before the parameter is removed and recommends a replacement parameter with slightly different semantics.
You would respond to the deprecation by...

A. Swapping all 40 call sites in a single change right away to prevent drawn-out migration work that will delay ongoing functioning.
B. Migrating all 40 call sites in a single change near the removal date to ensure the deprecated parameter continues to work as long as possible.
C. Adding regression tests to cover the parameter's behavior, then migrating call sites in batches that you validate against regression tests.
D. Keeping the deprecated parameter in place while writing a wrapper function around it to insulate the rest of the codebase from the eventual change.

Answer: C

Explanation:
The supplied examination source selects C . Because the replacement parameter has different semantics , this is not a mechanical rename. The application must establish what existing behavior is important, encode that behavior in regression tests, and migrate incrementally so deviations can be detected and isolated.
Anthropic's deprecation guidance follows the same lifecycle principle. Deprecated components remain temporarily available but receive a retirement deadline and a recommended replacement. Anthropic advises migrating before retirement and thoroughly testing applications against replacements well in advance of the cutoff. Its API versioning documentation also emphasizes compatibility contracts while acknowledging that APIs evolve and deprecated versions eventually become unavailable.
Batch migration reduces blast radius. If one migrated group fails regression tests, the team can diagnose the semantic difference before changing remaining call sites. It also avoids concentrating all migration risk near the retirement deadline.
A delays risk until the worst possible time. B only hides the dependency and does not complete migration. D changes all 40 usages simultaneously, making regression diagnosis and rollback substantially harder.
Relevant Claude Developer topics: API lifecycle, deprecation management, regression testing, incremental migration, compatibility, technical debt, and controlled change management .

NEW QUESTION # 68
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