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Anthropic CCDV-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Tools and MCPs	10.6%	- Tool Use and Tool Schemas - Model Context Protocol - Building Custom Tools and MCP Servers
Topic 2: Applications and Integration	33.1%	- Claude API and Client SDKs - Message Batches and Prompt Caching - API Integration and Application Development - Streaming, Error Handling and Reliability - Software Engineering Fundamentals - Multimodal and Structured Outputs
Topic 3: Agents and Workflows	14.7%	- Agent Construction with Claude - Agent Architecture - Agent Patterns and Frameworks
Topic 4: Eval, Testing, and Debugging	2.6%	- Evaluation, Testing, and Debugging
Topic 5: Claude Code	3.1%	- Claude Code Configuration and Extensibility
Topic 6: Security and Safety	8.1%	- Secure Tool Use and Guardrails - Prompt Injection and Untrusted Content - Safety and Responsible Development - Application Security
Topic 7: Prompt and Context Engineering	11%	- Context Engineering - Context Management and Long-Context Techniques - Prompt Engineering
Topic 8: Model Selection and Optimization	16.8%	- Performance Optimization - Model Capabilities and Trade-offs - Model Selection - Cost and Latency Optimization

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Anthropic Claude Certified Developer-Foundations Sample Questions (Q84-Q89):

NEW QUESTION # 84
Your Claude application's API keys are stored in a secrets manager. The team is debating whether the same key should be used in development, staging, and production environments.
How would you handle the keys across environments?

A. Use distinct keys for each environment so a compromise in one environment does not affect the others during normal operation across the application's lifecycle.
B. Use a single development key everywhere on the grounds that production keys are too risky to deploy across the application's three environments during normal operation.
C. Rotate the same key across environments at random intervals on the grounds that random rotation provides isolation between environments without requiring distinct keys.
D. Use the same key across all environments for simplicity and treat the propagation of any compromise as a known operational tradeoff for the team's key management approach.

Answer: A

Explanation:
Option A provides proper environment isolation and credential blast-radius control. Development, staging, and production represent different trust boundaries and should not share the same API credential. If a development machine, CI job, or staging service is compromised, a distinct credential prevents the attacker from automatically gaining the same access to production.
Anthropic's official workspace documentation explicitly recommends using separate workspaces for development, staging, and production. Workspaces can have their own API keys, members, resource limits, and usage tracking, and keys can be scoped to a specific workspace. Anthropic's authentication guidance also instructs developers to store API keys in a secrets manager, rotate them periodically, revoke suspected compromised keys, and use workspaces to scope credentials by project or environment.
B confuses rotation with isolation: rotating a credential that remains shared across all environments does not create independent security boundaries. C deliberately increases blast radius. D merely substitutes one globally shared key for another and therefore has the same architectural flaw.
The supplied exam source marks A as correct. Relevant topics: Confia Management, secrets management, API-key scoping, environment isolation, credential rotation, workspaces, least privilege, and production security.

NEW QUESTION # 85
Your Claude application returns confident-sounding answers, but occasionally those answers contain factual errors that downstream systems treat as ground truth. The team is concerned about the application's confidence-versus-accuracy gap.
How would you address the gap?

A. Add a disclaimer to every output telling users to verify the accuracy of the output and treat the disclaimer as the primary mechanism for managing the confidence-versus-accuracy gap.
B. Reject every response the application produces until a manual accuracy review is conducted on each response by a human reviewer before any downstream system uses it.
C. Lower the model's temperature so the model's responses sound less confident and downstream systems are less likely to treat the responses as ground truth in normal operation.
D. Apply skepticism toward confident output by adding validation steps, sourcing requirements, or confidence calibration before treating outputs as ground truth.

Answer: D

Explanation:
Option B establishes the correct trust boundary. Fluent or confident language is not evidence that a generated claim is factually correct. If downstream systems treat output as authoritative data, the application must independently establish whether the output meets its correctness requirements before accepting it.
Validation can take several forms depending on the workload: compare generated facts against authoritative records, require citations or source references, constrain output to retrieved evidence, apply deterministic business rules, or use separate evaluation/classification stages. Anthropic's agent engineering guidance repeatedly emphasizes explicit evaluation criteria and validation rather than relying on apparent confidence.
A confuses sampling behavior with factual reliability. Lowering temperature does not establish factual correctness and may only make an incorrect answer more repeatable. C supplies maximum oversight but is unnecessarily expensive and removes useful automation even for low-risk, easily validated cases. D communicates uncertainty to users but does not protect downstream systems that automatically consume the response.
Therefore, B treats model output as untrusted until verified to the level required by the application. Relevant Study Guide topics: output validation, grounding, factuality, confidence calibration, source verification, trust boundaries, and downstream safety.

NEW QUESTION # 86
Your application uses the Messages API to handle multi-turn conversations. Each new turn resends the entire conversation history, and your token costs are growing as conversations get longer. You suspect there is a more efficient approach.
How would you address this?

A. Truncate every conversation to the last two messages so that token usage stays low and costs remain predictable across the application's normal operation.
B. Summarize each conversation after every turn and replace the full history with the summary on the next request, reducing token count at the cost of fidelity.
C. Switch to the Batch API for every turn so the per-call cost is reduced, treating the batch as the team's primary cost-control mechanism for multi-turn work.
D. Use prompt caching to reuse the static portions of the conversation context across turns instead of paying for them at the normal input-token rate on every request the application sends.

Answer: D

Explanation:
Option A is the technically verified answer. Anthropic documents that multi-turn agentic requests resend the growing conversation context on subsequent turns. Prompt caching allows repeated prompt prefixes-such as system instructions, tool definitions, and prior unchanged conversation content-to be reused at the lower cache-read rate rather than repeatedly charged as ordinary uncached input. Anthropic specifically identifies caching repeated context as a major production cost optimization.
B is incorrect even though the supplied PDF contains a duplicated screenshot in which B appears selected.
The Batch API is designed for asynchronous workloads that can tolerate delayed completion; it is not the appropriate mechanism for every interactive turn in a multi-turn conversation. Because the user requested verified answers rather than blindly reproducing marked selections, A is retained.
C discards potentially essential conversation state arbitrarily. D is a valid context-management technique in some long-running workflows, but summarizing after every turn creates additional model work and loses detail; it does not exploit repeated-prefix caching.
Therefore, A directly addresses the stated cost pattern while preserving conversation fidelity. Relevant Study Guide topics: Messages API, stateless conversation history, prompt caching, cached input tokens, multi-turn applications, token economics, and cost optimization.

NEW QUESTION # 87
A teammate has asked how to extend Claude Code with a custom Skill that the team can invoke during sessions. The Skill consists of a set of instructions and a few support scripts the team wants Claude to be able to call when the Skill is loaded.
Where is the right place to define the Skill?

A. Define the Skill inside the application's source code as a regular library module and call it from the application code instead of from Claude Code.
B. Define the Skill in a personal scratch directory on each developer's machine and load it manually before each Claude Code session that needs it.
C. Define the Skill as a long inline instruction at the top of every CLAUDE.md file in the team's repositories so Claude has access to it on every session.
D. Define the Skill in a Skills directory recognized by Claude Code, where Claude can discover and load it during sessions for the team's repositories.

Answer: D

Explanation:
Option C matches Claude Code's documented Skill architecture. Agent Skills are filesystem-based extension artifacts rather than ordinary application modules or repeated prompt fragments. A Skill is represented by a directory containing a required SKILL.md file and can include optional supporting scripts, templates, examples, and reference material.
Anthropic documents project Skills under .claude/skills/ < skill-name > /SKILL.md. Project-level Skills can be shared through Git and automatically discovered when Claude Code loads project settings. Supporting scripts can reside alongside the Skill and be referenced from SKILL.md.
A incorrectly embeds reusable procedural material into every CLAUDE.md file, creating duplication and loading instructions even when they are irrelevant. B creates a conventional source-code library but does not register a Claude Code Skill. D makes the capability dependent on undocumented, developer-specific setup and undermines team reuse.
Therefore, C uses the extension mechanism specifically designed for discoverable, reusable Claude capabilities. Relevant Study Guide topics: Agent Skills, .claude/skills, SKILL.md, supporting resources, filesystem discovery, project-level configuration, and reusable Claude Code capabilities.

NEW QUESTION # 88
Your Claude application's content policy specifies categories of content it should not produce under any circumstance. The application currently has no mechanism to enforce this policy, and content matching these categories is appearing in the application's output.
How would you enforce the content policy?

A. Move enforcement to users by asking them to report content policy violations after the violating content has already reached them in the application's responses.
B. Enhance the system prompt to contain explicit instructions for the categories to avoid, complete with examples of each category. Treat the strengthened prompt as the primary enforcement mechanism for the application's content policy across all responses.
C. Remove the content policy entirely and let any output reach users during normal operation, accepting whatever content the application produces in response to incoming traffic.
D. Add deterministic output filtering that checks responses against the content policy before they reach users.

Answer: D

Explanation:
Option D is the strongest enforcement design because an unconditional content policy requires an application- level control between model generation and user delivery. Prompt instructions are valuable for steering Claude, but they are probabilistic controls and should not be treated as the sole enforcement mechanism when prohibited categories must never be exposed.
Anthropic's guardrail guidance recommends layered safeguards including screening, validation, monitoring, and filtering rather than relying exclusively on prompts. Its prompt-leak guidance specifically recommends output screening and post-processing, including deterministic techniques such as keyword matching, regular expressions, or other text-processing mechanisms where appropriate.
A improves the probability of policy compliance but cannot guarantee that every generated response will satisfy an externally defined application policy. B explicitly abandons the requirement. C detects violations only after exposure, which is unsuitable when the content must not reach users.
A production architecture can combine system instructions, structured classification, policy engines, deterministic rules, and model-based moderation, but the decisive requirement is enforcement before output delivery. Relevant Claude Developer topics are guardrails, output filtering, content moderation, deterministic enforcement, defense in depth, safe application boundaries, and production Claude application design.
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NEW QUESTION # 89
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