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Anthropic CCDV-F Exam Syllabus Topics:

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

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

NEW QUESTION # 93
Your Claude application has been running for several conversation turns, and you notice the model occasionally references information that was discussed many turns ago but is no longer relevant. You suspect context drift is causing the model to weight stale content too heavily.
How would you address the drift?

A. Increase the context window size so all turns of the conversation remain visible to the model in full detail.
B. Apply compaction to summarize older portions of the conversation so the gist remains while the specifics carry less weight.
C. Reset the conversation after every turn so the model loses all prior turns when generating a response.
D. Truncate the conversation so the model sees only the most recent turn during each subsequent response.

Answer: B

Explanation:
Option C applies the appropriate context-management technique. The issue is not simply whether the older conversation can physically fit into the context window; it is that stale details remain prominent enough to interfere with the model's current task. Effective context engineering optimizes signal quality, not merely maximum token retention.
Anthropic's context-editing documentation describes compaction as summarizing accumulated history and replacing the full history with a structured summary when context becomes large. This preserves important task state while substantially reducing low-value detail that can distract later reasoning.
A retains precisely the stale material producing the problem and therefore does not address context drift. B destroys all useful continuity between turns, including valid goals, decisions, and intermediate state. D is similarly excessive because limiting Claude to only the latest turn discards information that may still be required.
Compaction provides the correct middle ground: retain durable conclusions, current objectives, unresolved issues, and other high-signal state while compressing obsolete conversational detail. Relevant Study Guide topics: context engineering, compaction, conversation history, long-running agents, stale context, context drift, and high-signal state preservation.

NEW QUESTION # 94
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. 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.
B. Loosen the schema as the teammate proposed so the downstream service receives every call the agent makes during normal operation.
C. 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.
D. Keep the schema strict, validate arguments before dispatching, and return a structured error so the agent can retry.

Answer: D

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 # 95
You are running Claude Code as part of an automated continuous integration pipeline. The pipeline needs Claude Code to execute a set of well-defined tasks without prompting for confirmation, and the output needs to be captured for downstream processing.
How would you configure the pipeline?

A. Disable Claude Code's confirmation prompts globally across all environments so the pipeline runs without interruption from any prompt.
B. Replace Claude Code with a different tool that does not require any configuration to operate without confirmation prompts in the pipeline.
C. Run Claude Code in headless mode with the required permissions configured in settings.json and capture its output for downstream processing.
D. Run Claude Code in interactive mode and have a developer manually approve every confirmation prompt while the pipeline executes its tasks.

Answer: C

Explanation:
Option B is the correct automation pattern. A CI pipeline must operate non-interactively, so Claude Code should be invoked in its programmatic/headless execution mode and given only the permissions required for the predefined workflow. Output can then be captured by the pipeline and supplied to subsequent build, testing, reporting, or deployment stages.
Claude Code's configuration system supports explicit permission rules in JSON settings. Anthropic documents that settings.json controls, among other behavior, what Claude Code can execute without asking.
For example, permissions.allow can authorize specific commands while permissions.deny prevents sensitive operations. Project-level .claude/settings.json can be version-controlled so CI behavior is reproducible across runners.
This is substantially safer than C, which disables confirmation behavior globally and expands the blast radius beyond the CI workload. D defeats automation because a human must remain present. A is unnecessary because Claude Code is explicitly configurable for automation scenarios.
The principle is least-privilege non-interactive configuration: authorize only the commands and resources required by the pipeline and capture machine-readable output for downstream processing. Relevant Claude Developer topics are Claude Code automation, configuration management, permissions, CI/CD integration, headless execution, and reproducible project settings.

NEW QUESTION # 96
You are building an agent that needs to call several internal APIs and a database in a structured, repeatable way. Your team has decided to use the Claude Agent SDK rather than build a custom loop. You are setting up the agent's tool definitions and execution loop.
How would you set up the tools and execution loop?

A. Call the Messages API directly and let the model format its tool calls in plain text.
B. Use the SDK's tool interface and write the loop and history layer in the team's own code.
C. Use the SDK's tool interface and loop, with conversation history stored in a separate team database.
D. Use the SDK's tool interface and let the SDK handle the loop, dispatch, and history.

Answer: D

Explanation:
The supplied Claude Developer examination source selects A . The purpose of choosing an agent SDK rather than implementing a custom Messages API loop is to consume the SDK's higher-level abstractions. Re- implementing dispatch, iteration, and state handling would discard much of the value provided by the SDK.
Anthropic's current documentation distinguishes low-level tool-use loops from higher-level SDK-managed abstractions. With a manually implemented Messages API workflow, application code must inspect stop_reason, execute requested client tools, append tool_result blocks, preserve conversation history, and repeat until Claude completes the turn. Anthropic's higher-level tooling can instead encapsulate this repetitive control flow. Current migration guidance also confirms that Agent SDK @tool functions are automatically dispatched by the SDK and that agents, tools, and sessions are first-class SDK concepts.
B is incorrect because structured tool use should not be replaced with informal plain-text calls. C introduces an unnecessary custom state dependency when no external persistence requirement exists. D duplicates control-loop functionality despite the explicit decision to use the SDK.
Relevant Claude Developer topics: Agent SDK, tool definitions, dispatch, agentic loops, conversation state, tool execution, and abstraction selection .

NEW QUESTION # 97
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. Stop using CLAUDE.md altogether and ask each developer to configure Claude Code manually for each project they work on.
B. 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.
C. Move all instructions to a separate documentation site that developers consult during Claude Code sessions across all repositories.
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 # 98
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The software keeps track of the previous Claude Certified Developer-Foundations (CCDV-F) practice exam attempts and shows the changes of each attempt. You don't need to wait days or weeks to get your performance report. The software displays the result of the Anthropic CCDV-F Practice Test immediately, which is an excellent way to understand which area needs more attention.

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