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Anthropic CCDV-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Model Selection and Optimization	16.8%	- Cost and token optimization - Claude model family characteristics - Latency and performance trade-offs
Topic 2: Claude Code	3.1%	- Claude Code configuration and usage
Topic 3: Tools and Model Context Protocol (MCP)	10.6%	- Tool integration and usage - MCP server development
Topic 4: Applications and Integration	33.1%	- SDK and third-party integration - Claude Messages API - Vision capabilities - Streaming and Batch API
Topic 5: Evaluation, Testing, and Debugging	2.6%	- Output evaluation and validation - Error handling and debugging
Topic 6: Prompt and Context Engineering	11%	- Context window management - Prompt design and structuring - Structured output handling
Topic 7: Agents and Workflows	14.7%	- Memory and context management - Workflow vs autonomous agents - Agent architecture principles - Claude Agent SDK usage
Topic 8: Security and Safety	8.1%	- AI application security - Guardrails and safety controls

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Anthropic Claude Certified Developer-Foundations Sample Questions (Q74-Q79):

NEW QUESTION # 74
Your Claude application requests structured JSON output from the model. Most of the time the JSON is well- formed, but occasionally Claude returns malformed JSON that breaks downstream processing.
How would you handle the malformed output?

A. Manually inspect every response before downstream processing so a human reviewer catches any malformed JSON before the application passes the response to downstream systems.
B. Switch to free-form text output so the application no longer depends on JSON parsing for any of the responses it sends to downstream systems during normal operation.
C. Add output validation that parses Claude's response against the expected schema and treats malformed output as a recognized error path with retry or fallback handling.
D. Retry the same request repeatedly until valid JSON appears in the model's response, with the retry loop adding delay to the application's response time on affected requests.

Answer: C

Explanation:
Option B establishes a controlled boundary between probabilistic model output and deterministic downstream code. When structured data is machine-consumed, malformed JSON must be recognized as an explicit application error rather than allowed to propagate into parsers, databases, or other services.
Anthropic's Structured Outputs documentation identifies malformed JSON, missing fields, inconsistent types, and schema violations as exactly the kinds of failures that can break downstream systems when unconstrained output is used. Current Claude capabilities can constrain responses using JSON Schema, and SDK helpers can provide parsing and validation.
Even when an application uses an older or unconstrained generation path, it should parse against the expected schema, record validation failure, and enter a bounded recovery path such as retry, repair, fallback, or controlled rejection. A human review of every request is unscalable. C removes a useful structured interface instead of making it reliable. D performs uncontrolled blind retries and provides no schema-aware error handling or bounded fallback strategy.
Therefore, B gives the application explicit failure semantics and protects downstream systems. Relevant Study Guide topics: structured output, JSON validation, schema enforcement, retries, fallback handling, defensive parsing, and downstream reliability.

NEW QUESTION # 75
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 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.
C. As a Skill or MCP server because both are purpose-built for cross-team reuse independently by each consuming team.
D. 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.

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 # 76
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. Add Claude hooks that intercept database operations and apply deterministic checks, such as blocking destructive queries or requiring approval, before the queries execute.
C. 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.
D. 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.

Answer: B

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 # 77
Your Claude application runs long agentic workflows where the agent makes many tool calls, and the conversation history grows quickly. After about 20 tool calls, you notice the agent's responses become less focused and sometimes ignore earlier task constraints.
How would you address this?

A. Restart the agent every five tool calls to prevent any drift, with the agent losing all task state at each restart point during the workflow.
B. Remove tool calling from the workflow entirely so the agent operates as a single text-generation step with no tool outputs accumulating in the context window.
C. Increase the model's context window so the agent can hold every tool output at full detail across the entire workflow no matter how many tool calls it accumulates.
D. Apply context engineering techniques such as tool output pruning or compaction to keep the active task state visible while reducing the volume of older content.

Answer: D

Explanation:
Option B addresses the actual architectural failure: low-value historical material is crowding out the high- signal information required for current reasoning. A larger context window does not guarantee better attention to important constraints. Effective agent architecture actively manages what remains in context as the workflow progresses.
Anthropic's Context Editing documentation explicitly provides tool-result clearing for agentic workflows with heavy tool usage. Once Claude has processed an older result, verbose file contents, search output, or API responses may no longer need to remain in full. Older results can therefore be removed while recent and important state remains available. Anthropic also documents compaction, where accumulated history is summarized and replaced with a smaller representation that preserves important task state.
A removes an important agent capability rather than solving context growth. C retains all accumulated noise and merely gives it more space. D repeatedly destroys valuable task state and creates artificial workflow boundaries.
Therefore, B preserves goals, decisions, unresolved constraints, and necessary results while reducing irrelevant historical content. Relevant Study Guide topics: context engineering, tool-result clearing, compaction, long-running agents, context quality, and task-state preservation.

NEW QUESTION # 78
Your Claude application's outputs are inconsistent in format. The team's instructions are scattered across the system prompt and user messages, with some instructions stated only once and others repeated.
How would you fix the inconsistency?

A. Consolidate behavioral and format instructions in the system prompt and keep user messages focused on the user's input.
B. Repeat all instructions in both the system prompt and the user message so the model sees each instruction more than once.
C. Place each instruction wherever the developer who first wrote it found most natural, retaining the original intent.
D. Move all instructions into the user message so the application's behavior is controlled at the input point.

Answer: A

Explanation:
Option B creates a clear separation between persistent application instructions and per-request user content.
Stable behavioral rules-role, output conventions, formatting constraints, policies, and other instructions intended to apply across requests-belong in the system-level instruction layer. User messages should primarily carry task-specific input and requirements that vary from interaction to interaction.
Anthropic's Messages API defines the system prompt specifically as the mechanism for providing overarching context and instructions to Claude. Current guidance likewise recommends the top-level system field for instructions that should apply from the beginning of the conversation, while more transient information belongs in conversation messages.
A mixes persistent application behavior with user-controlled content and weakens separation of concerns. C preserves the inconsistent architecture responsible for the problem. D duplicates instructions unnecessarily, wastes context tokens, and increases the possibility that future edits create conflicting versions of the same rule.
Consolidating stable instructions also makes prompts easier to version, evaluate, review, and maintain.
Therefore, B is the correct design. Relevant Study Guide topics: system prompts, instruction hierarchy, prompt organization, formatting consistency, separation of concerns, and maintainable prompt construction.

NEW QUESTION # 79
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