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Anthropic CCDV-F Exam Syllabus Topics:

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

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Anthropic Claude Certified Developer-Foundations Sample Questions (Q38-Q43):

NEW QUESTION # 38
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. Collapse the three subagents into a single large-context model call that handles retrieval, analysis, and generation in one pass.
C. Give each subagent read access to the other subagents' outputs so they can identify and resolve contradictions without routing back through the primary agent.
D. 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.

Answer: D

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 # 39
A teammate is reviewing the team's threat model for a Claude application and has asked you to identify the categories of AI-specific threats that the model should cover. The teammate has already listed traditional web application threats and wants to know what additional categories apply to a Claude application.
Which AI-specific threat categories would you add?

A. Network-level denial of service and physical infrastructure attacks, because these categories cover the threats most likely to affect any Claude application in production.
B. Supply chain attacks on the Claude SDK because the SDK itself is the only point of vulnerability that a Claude application introduces beyond traditional web application threats.
C. Prompt injection, data leakage from prompts or context, jailbreak attempts, and unsafe model output that bypasses application controls.
D. Cross-site scripting and SQL injection, because these traditional web application threats apply with equal weight to any application that uses Claude in any way.

Answer: C

Explanation:
Option C correctly identifies threat categories introduced or significantly amplified by LLM-based application architecture. Prompt injection attempts to manipulate Claude into following adversarial instructions.
Jailbreaks seek to circumvent behavioral or application safeguards. Data leakage can expose confidential information contained in system prompts, retrieved context, tool results, or conversation state. Unsafe model output becomes particularly serious when output is consumed by downstream systems or translated into tool actions.
Anthropic's official guardrail documentation explicitly distinguishes jailbreaks and direct prompt injection from indirect prompt injection. It recommends input screening, hardened system prompts, structured handling of untrusted tool content, least-privilege access, output screening, and continuous monitoring. Anthropic also warns that sensitive prompt/context data can leak and recommends post-processing and output filtering where required.
Traditional threats such as SQL injection or XSS remain relevant to the surrounding application, but they are not the additional AI-specific categories requested. B similarly describes conventional infrastructure threats.
D is far too narrow because the SDK is only one component of the attack surface.
The supplied exam source explicitly marks C. Relevant topics: Claude App Design, threat modeling, prompt injection, jailbreaks, data leakage, output safety, least privilege, and defense in depth.

NEW QUESTION # 40
Your Claude agent performs database operations. A recent incident occurred where the agent ran a destructive query that affected production data. The team wants to add deterministic controls to prevent similar incidents.
How would you prevent similar incidents?

A. Add a system prompt instruction telling the agent to be careful with database operations on every request the application handles during normal operation across all incoming traffic.
B. Switch to a higher-capability Claude model on the grounds that a more capable model is less likely to run destructive queries during normal operation across all requests.
C. Run the agent only during business hours when humans are available to monitor its activity, treating the schedule as the primary control mechanism for destructive operations.
D. Add Claude hooks that intercept database operations and apply deterministic checks, such as blocking destructive queries or requiring approval, before the queries execute.

Answer: D

Explanation:
Option B is correct because destructive production operations require deterministic enforcement outside the model's probabilistic reasoning. Claude Code hooks can intercept lifecycle events before tool execution and explicitly allow, deny, or request further handling based on concrete rules.
Anthropic's hooks documentation provides this exact security pattern. A PreToolUse hook can inspect a proposed command before execution and return a blocking decision. Anthropic's example demonstrates blocking destructive operations such as drop table, while other commands proceed normally.
That mechanism can be adapted to database controls: block DROP, destructive DELETE, unauthorized schema modifications, or production writes; require explicit approval for high-risk operations; and allow read- only or known-safe queries automatically.
A merely increases the probability that someone might notice an unsafe operation and does not prevent execution. C assumes model capability can replace access controls, which is an unacceptable safety boundary.
D is useful behavioral guidance but remains probabilistic and cannot guarantee prevention.
Therefore, B creates a deterministic control between model intent and side-effect execution. Relevant Study Guide topics: Claude hooks, PreToolUse, tool governance, deterministic enforcement, approval gates, least privilege, and destructive-operation protection.

NEW QUESTION # 41
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 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.
B. 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.
C. 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.
D. 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.

Answer: C

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 # 42
A Claude application is occasionally refusing to answer questions that should be in scope, including questions the application has answered correctly in the past. You want to investigate.
What is the first step of your investigation?

A. Assess whether the refusals cluster around specific input patterns, question types, or time periods by analyzing the distribution of refused requests in the logs.
B. Identify which questions have been refused and compare them against previously answered questions to determine what changed between the successful and refused interactions.
C. Test whether removing or adjusting those instructions resolves the behavior.
D. Examine traces of the refused requests to identify what triggers the refusal, whether input patterns, system prompt content, or other context.

Answer: D

Explanation:
The supplied exam source marks B as correct. The first debugging action should be evidence collection rather than immediately modifying prompts or configuration. A trace exposes the complete conditions under which the refusal occurred: user input, system instructions, accumulated conversation state, tool results, parameters, and resulting model output.
This is important because a refusal can originate from several interacting causes. A particular input pattern may trigger it; system instructions may unintentionally conflict with the requested task; prior conversational context may alter Claude's interpretation; or contextual information may make an otherwise acceptable request appear out of scope. Without examining the failing execution path, changing instructions is speculative.
Option A jumps directly to remediation before identifying the cause. C provides useful aggregate analysis but is more appropriate after individual failing executions have been inspected. D is also valuable for comparative debugging, but determining what changed requires the detailed execution data that traces provide.
The correct troubleshooting sequence is therefore: capture or inspect representative failed traces, determine the trigger, compare against successful interactions, formulate a hypothesis, make a targeted change, and re- evaluate.
Relevant Claude Developer topics: Systems Life Cycle, observability, production debugging, tracing, failure analysis, refusals, regression investigation, and evaluation-driven remediation .

NEW QUESTION # 43
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