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Anthropic CCDV-F Exam Syllabus Topics:

Section	Weight	Objectives
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
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
Security and Safety	8.1%	- Secure Application Design 1. Protect sensitive data and manage access appropriately 2. Apply secure-by-design practices to Claude-powered applications - Safety and Guardrails 1. Implement safety controls and guardrails 2. Use hooks and other mechanisms to enforce application controls
Prompt and Context Engineering	11%	- Context Engineering 1. Manage context windows and application context 2. Apply context management strategies for agents and long-running workflows - Prompt Engineering 1. Apply prompting techniques to improve reliability and output quality 2. Design prompts appropriate to application requirements
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 - 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. Evaluate tradeoffs between agentic approaches and traditional workflows 2. Select appropriate agent architectures and patterns
Eval, Testing, and Debugging	2.6%	- Testing and Debugging 1. Diagnose and debug application, agent, and integration issues 2. Test Claude integrations and agentic systems - Evaluation 1. Design and run evaluations for Claude-powered applications 2. Interpret evaluation results and improve application quality
Tools and MCPs	10.6%	- Tool Development and Integration 1. Handle tool schemas, invocation, and tool-use results 2. Design and implement custom tools for Claude applications and agents - Model Context Protocol 1. Apply MCP concepts and patterns for connecting models to external capabilities 2. Build and integrate MCP servers
Applications and Integration	33.1%	- Claude API and Client SDKs 1. Construct and process API requests and responses 2. Integrate applications with the Claude API and supported client SDKs 3. Implement streaming and handle API errors - Application Development and Integration 1. Handle multimodal and structured application inputs and outputs 2. Build and ship production-grade Claude-powered applications 3. Integrate Claude capabilities into existing software systems and workflows

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Anthropic Claude Certified Developer-Foundations Sample Questions (Q73-Q78):

NEW QUESTION # 73
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. 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.
B. 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.
C. 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.
D. 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.

Answer: C

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 # 74
A teammate has asked why your Claude application sometimes produces a response that includes the prompt text repeated back, and other times produces a response with the prompt text rephrased. They suspect a bug in the application's request construction.
How would you respond?

A. Explain that LLMs generate output token by token, and variation in how prompt content appears in output is a property of generation, not a bug in request construction.
B. Confirm that the variation is a bug in the application's request construction and start investigating which part of the application is producing the inconsistent prompt text.
C. Tell the teammate that the variation is caused by the application sending two different prompts on different runs and propose a code change that pins the system prompt to a single version.
D. Tell the teammate that the variation depends on which Claude model serves the request, and recommend pinning the application to a single model version to make the output behavior consistent.

Answer: A

Explanation:
Option B reflects the fundamental generative behavior of large language models. Claude does not retrieve a fixed response template for a given prompt. Instead, it generates the response incrementally, selecting subsequent tokens according to the probability distribution produced by the model. Consequently, semantically equivalent runs can differ in wording, ordering, whether prompt language is quoted, and whether it is paraphrased.
Anthropic's API documentation explicitly describes sampling parameters as controlling randomness in subsequent-token selection and also states that even configurations intended to minimize randomness do not make output completely deterministic. Current Claude model generations increasingly manage sampling internally, but the underlying principle remains: generative output is not guaranteed to be textually identical across equivalent calls.
Therefore, seeing the original prompt echoed in one response and paraphrased in another does not, by itself, demonstrate malformed request construction. A real request-construction problem should instead be established through traces showing different system instructions, messages, parameters, or context.
A, C, and D incorrectly infer specific implementation faults without supporting evidence.
The supplied question identifies B as the correct response. Relevant topics: SW Eng Foundations, token generation, sampling, nondeterminism, debugging, and model behavior.

NEW QUESTION # 75
You are designing an agent that handles a multi-step research task. You want the agent to break the task into smaller pieces, hand each piece to a focused subagent, and consolidate the results.
The agent pattern you would apply is...

A. A single tool-use loop that includes every tool the agent might need across all subtasks.
B. A memory pattern that stores the entire research history in advance, before any subtask begins execution.
C. An orchestrator and subagent pattern with specialized subagents assigned to each subtask.
D. A context-window pruning pattern that drops each subtask's content after the agent moves on.

Answer: C

Explanation:
The supplied Claude Developer source explicitly marks A . The scenario contains the defining elements of an orchestrator/subagent architecture : decomposition of a larger objective, delegation of independent subtasks to specialized workers, and aggregation of their outputs by a coordinating agent.
This architecture is appropriate when subtasks can be performed with focused context or specialized tools.
Instead of forcing one agent to carry every intermediate detail, the orchestrator can formulate assignments, launch suitable subagents, receive condensed results, identify missing information, and synthesize the final research product. This also enables context isolation and potentially parallel execution.
Anthropic's published multi-agent architecture uses this orchestrator-worker approach for research workloads:
a lead agent decomposes a query, delegates work to specialized subagents, and then integrates their findings.
This is particularly useful where exploration is broad and individual subtasks benefit from independent context windows. The broader model-selection guidance also identifies orchestrator strategies as useful where work can be partitioned among worker models.
B describes storage rather than delegation. C is a context-management technique, not a decomposition pattern.
D centralizes all responsibilities in a single loop.
Relevant Claude Developer topics: Agent Patterns, orchestration, subagents, task decomposition, specialization, delegation, context isolation, and result consolidation .

NEW QUESTION # 76
Your Claude application's token costs have grown faster than expected. The team has not been tracking token usage by feature, so the team cannot identify which features are driving cost. The team is debating how to respond.
How would you respond?

A. Add token usage tracking by feature to the application's logging so the team can identify which features drive cost before recommending changes.
B. Switch every feature to the smallest model to cut cost broadly across the application during normal operation.
C. Reduce token usage uniformly across all features by half, applying the cut evenly across the application during normal operation.
D. Tell the team that cost growth is unavoidable as the application scales and that no investigation will change the trajectory of the application's cost over the next several quarters.

Answer: A

Explanation:
Option D follows the fundamental optimization rule of measuring before changing. Without feature-level usage telemetry, the team cannot know whether cost growth comes from request volume, long prompts, excessive outputs, low cache-hit rates, expensive models, agent loops, tool results, or one particularly inefficient workflow.
Anthropic's Usage and Cost API exists specifically to provide granular historical usage and cost information.
It supports token tracking and breakdowns by dimensions including model, workspace, service tier, API key, context window, and other usage characteristics. Anthropic describes this data as useful for monitoring, cost reconciliation, optimization, and determining whether system changes improve efficiency.
B applies a model downgrade indiscriminately and may damage features whose quality requirements genuinely demand a stronger model. C similarly imposes an arbitrary token reduction without identifying where waste exists. A abandons cost management entirely.
The correct lifecycle is instrument, establish a baseline, identify high-cost features, analyze why they are expensive, apply targeted optimizations, and measure again. Relevant Study Guide topics: observability, usage telemetry, cost attribution, token accounting, production monitoring, capacity planning, and evidence- driven optimization.

NEW QUESTION # 77
A new agent your team built handles customer support tickets, but it routinely gets confused when a single ticket spans billing, shipping, and product issues. The agent often loses track of which sub-issue it has already addressed and revisits the same one. The team is considering architectural changes.
What architectural change would you recommend?

A. Add detailed prompting that instructs the agent to track which sub-issues have been resolved and which remain.
B. Introduce an orchestrator agent that delegates billing, shipping, and product sub-issues to dedicated subagents.
C. Increase the size of the agent's context window so it can hold the full ticket history at once.
D. Switch to a deterministic workflow that handles billing, shipping, and product issues in a fixed sequence.

Answer: B

NEW QUESTION # 78
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