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Anthropic CCDV-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Security and Safety	8.1%	- Safety and Guardrails 1. Use hooks and other mechanisms to enforce application controls 2. Implement safety controls and guardrails - Secure Application Design 1. Protect sensitive data and manage access appropriately 2. Apply secure-by-design practices to Claude-powered applications
Topic 2: Model Selection and Optimization	16.8%	- Model Selection 1. Select appropriate Claude models for task requirements 2. Evaluate quality, latency, capability, and cost tradeoffs - Performance and Cost Optimization 1. Use batching and other approaches to improve efficiency 2. Apply prompt caching and other cost optimization techniques
Topic 3: Agents and Workflows	14.7%	- Subagents and Agentic Frameworks 1. Use subagents and coordinate multi-agent workflows 2. Apply appropriate agentic frameworks and orchestration patterns - Claude Agent SDK and Agent Loops 1. Implement and manage custom agent loops 2. Build and configure agents using the Claude Agent SDK - Agent Architecture and Tradeoffs 1. Select appropriate agent architectures and patterns 2. Evaluate tradeoffs between agentic approaches and traditional workflows
Topic 4: Tools and MCPs	10.6%	- Model Context Protocol 1. Apply MCP concepts and patterns for connecting models to external capabilities 2. Build and integrate MCP servers - Tool Development and Integration 1. Handle tool schemas, invocation, and tool-use results 2. Design and implement custom tools for Claude applications and agents
Topic 5: Prompt and Context Engineering	11%	- Context Engineering 1. Apply context management strategies for agents and long-running workflows 2. Manage context windows and application context - Prompt Engineering 1. Design prompts appropriate to application requirements 2. Apply prompting techniques to improve reliability and output quality
Topic 6: Applications and Integration	33.1%	- Claude API and Client SDKs 1. Implement streaming and handle API errors 2. Integrate applications with the Claude API and supported client SDKs 3. Construct and process API requests and responses - Application Development and Integration 1. Handle multimodal and structured application inputs and outputs 2. Integrate Claude capabilities into existing software systems and workflows 3. Build and ship production-grade Claude-powered applications
Topic 7: Eval, Testing, and Debugging	2.6%	- Testing and Debugging 1. Test Claude integrations and agentic systems 2. Diagnose and debug application, agent, and integration issues - Evaluation 1. Interpret evaluation results and improve application quality 2. Design and run evaluations for Claude-powered applications
Topic 8: Claude Code	3.1%	- Claude Code Configuration and Usage 1. Use CLAUDE.md and project configuration 2. Configure settings and development environments 3. Use Skills, plugins, and Claude Code capabilities effectively

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

NEW QUESTION # 93
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. Apply skepticism toward confident output by adding validation steps, sourcing requirements, or confidence calibration before treating outputs as ground truth.
C. 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.
D. 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.

Answer: B

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 # 94
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 to inline runtime construction so the team can update them quickly through a streamlined process outside the standard code review workflow.
B. Move all prompts out of version control to a separate spreadsheet that team members can edit freely as the application evolves over time.
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 # 95
You are extending a Claude agent with a capability that needs to be reusable across multiple teams in the organization, with each team able to invoke and use it independently.
How would you build the capability?

A. As a wrapper around an existing built-in tool that adds the missing functionality, on the grounds that built-in tools cover the reuse pattern when extended carefully.
B. As a custom tool embedded in this team's agent only, with other teams able to copy the implementation into their own agents when they need the capability.
C. As a Skill or MCP server because both are purpose-built for cross-team reuse independently by each consuming team.
D. As a shared library that each team imports into its own Claude application code, with each team responsible for keeping the library up to date in its integration.

Answer: C

Explanation:
Option C is correct because Skills and MCP are first-class Claude extension mechanisms intended to make capabilities reusable rather than embedding one-off logic inside a single agent. Claude documentation describes Skills as reusable knowledge, instructions, and workflows that can be loaded when relevant. It describes MCP as the standard mechanism for connecting Claude to external services, tools, prompts, and resources. Claude Code plugins can package Skills and MCP servers for distribution across projects and teams.
Option A tightly couples the capability to one team's agent and requires copying code, creating version drift and duplicated maintenance. Option B is technically reusable software, but it leaves every consuming Claude application responsible for its own integration and does not expose the capability through Claude's native extension interfaces. Option D assumes an existing built-in tool is the correct abstraction even though the scenario specifically requires an independently reusable capability.
The precise choice between Skill and MCP depends on what is being reused: use a Skill for reusable instructions/workflows; use MCP when the capability exposes external data or actions. Therefore, C best reflects the Claude Developer extension model. Relevant topics: Agent Skills, MCP, plugins, tool integration, reuse, and cross-team capability distribution.

NEW QUESTION # 96
A team has deployed a multi-agent system in which a primary agent decomposes user requests and delegates subtasks to three specialized subagents: one for data retrieval, one for analysis, and one for report generation.
In production, the team observes that subagents are making redundant tool calls, occasionally exceeding token budgets, and sometimes producing outputs that contradict each other - all of which the primary agent passes along without catching.
What is the most appropriate way to address these failures?

A. Add retry logic to each subagent so that when a tool call fails, the subagent retries automatically before escalating - and configure each subagent to log its tool calls and outputs to a shared trace so the team can audit redundancy and contradictions after the fact.
B. Give each subagent read access to the other subagents' outputs so they can identify and resolve contradictions without routing back through the primary agent.
C. Strengthen the primary agent's management layer to enforce per-subagent tool budgets, validate outputs against a defined schema before passing them forward, and establish explicit handoff contracts between stages.
D. Collapse the three subagents into a single large-context model call that handles retrieval, analysis, and generation in one pass.

Answer: C

Explanation:
C addresses the failures at the correct architectural layer: orchestration and supervision . The supplied exam item identifies C as correct. The primary agent is responsible not merely for forwarding subordinate output but for governing delegation, resource usage, stage boundaries, and result quality.
Anthropic's current guidance recommends explicit control over subagent use because excessive delegation multiplies latency and cost. Its documentation specifically supports deterministic limits on subagent spawning and SDK budget controls such as max_budget_usd. Tool contracts can also use defined input schemas and strict validation so malformed data cannot silently propagate between processing stages.
Explicit handoff contracts are equally important. Retrieval should provide an agreed structure to analysis; analysis should provide validated findings to report generation; and the manager should reject, retry, reconcile, or escalate outputs that violate those contracts.
A improves observability but mainly detects problems after they occur. B destroys context isolation and can create additional coupling. D abandons useful specialization instead of correcting deficient supervision.
Relevant Claude Developer topics: Agent Patterns, orchestrator/subagent architecture, delegation, budgets, handoff contracts, schema validation, output reconciliation, and multi-agent governance .

NEW QUESTION # 97
Your team is integrating Claude into an existing REST API service. The service handles concurrent requests, and you are deciding how to structure the Claude API calls within the existing async codebase.
How would you structure the Claude calls?

A. Replace the existing async REST service with a synchronous service to match the call style of synchronous Claude SDK clients.
B. Use the Claude SDK's async client and structure Claude calls as awaitable operations alongside the rest of the service's async code.
C. Run Claude API calls in a separate thread pool isolated from the rest of the service so the main event loop is not affected.
D. Make synchronous Claude API calls and block the async event loop until each call completes before processing the next request.

Answer: B

Explanation:
Option C is the natural integration model for an application that already uses asynchronous I/O. Claude API calls are network-bound operations, so blocking the application's event loop while waiting for responses would reduce concurrency and impair throughput. The Anthropic Python SDK explicitly provides AsyncAnthropic, and its documented usage awaits client.messages.create() directly. It also supports an aiohttp backend when improved asynchronous concurrency is desired.
This means Claude requests can participate in the same cooperative asynchronous execution model as other database, HTTP, or service calls. While one request waits for remote I/O, the runtime can continue serving other work rather than dedicating the event loop to an idle blocking operation.
A can be used when integrating unavoidable blocking libraries into async software, but it adds thread-pool management when an official asynchronous client already exists. B needlessly redesigns a concurrent REST service around a synchronous integration. D is specifically harmful because blocking the event loop prevents normal concurrent request processing.
Therefore, C uses the abstraction provided for exactly this architecture. Relevant Study Guide topics: Claude SDK clients, asynchronous APIs, awaitable I/O, concurrency, REST-service integration, and scalable application architecture.

NEW QUESTION # 98
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