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Anthropic CCDV-F Exam Syllabus Topics:

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

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Anthropic Claude Certified Developer-Foundations Sample Questions (Q67-Q72):

NEW QUESTION # 67
You are reviewing an architectural diagram for a Claude-powered travel-booking system. The diagram shows a top-level component that interprets user requests and three subordinate components that handle flights, hotels, and ground transportation. The top-level component is responsible for routing each request, sequencing the subordinate components, and reconciling their outputs into a final itinerary. The diagram also shows that each subordinate component has its own tool list and own short conversation history that is not shared with the others.
Which architectural pattern does this diagram most closely describe?

A. A manager and supervisor pattern with isolated context per subagent, where the top-level agent coordinates specialized subagents that each maintain their own conversation history and tool list.
B. A manager and supervisor pattern with shared context, where the top-level agent and the subagents all share a single conversation history that grows as the request flows through the system.
C. A pipeline pattern, where each component processes the user request in sequence and passes the full conversation history along with the request to the next component in the pipeline.
D. A retrieval-augmented pattern, where the top-level component retrieves relevant context from the subordinate components' indexed data stores before generating each response.

Answer: A

Explanation:
The architecture is a manager/supervisor-or orchestrator/subagent-pattern with isolated subagent context.
The defining characteristics are a coordinating top-level agent, specialized subordinate agents, delegated responsibilities, and distinct tool/context boundaries. The supervisor determines which specialist should act, sequences work where necessary, and integrates the specialists' outputs into the final result.
Anthropic's published multi-agent research architecture uses this same structural principle. Its Research system employs an orchestrator-worker pattern in which a lead agent analyzes the task, creates specialized subagents, delegates separate research responsibilities, and consolidates their findings. Anthropic emphasizes that good delegation requires each subagent to receive a specific objective, task boundaries, expected output, and appropriate tools.
The question additionally specifies that each travel specialist has its own conversation history and tool list.
That eliminates B because context is explicitly not shared globally. C would involve fixed sequential processing rather than supervisor-directed specialization. D describes retrieval augmentation, not autonomous subordinate agents.
Therefore, A precisely matches both the hierarchy and context-isolation characteristics described.
The supplied examination set also marks A as the intended answer. Relevant topics: Agent Patterns, manager
/supervisor architecture, orchestrator-worker systems, subagents, delegation, specialized tools, and context isolation.

NEW QUESTION # 68
You are designing a Claude application that will require structured JSON output for downstream processing.
The output schema is well-defined, and downstream systems will reject malformed JSON.

A. Define a clear schema and structure the prompt to request output in that schema, with downstream systems handling any validation needed.
B. Structure the prompt to request output in a schema that is described in plain English, with downstream systems parsing whatever shape Claude produces.
C. Avoid structured output and use free-form text everywhere instead, on the grounds that free-form text is more flexible and handles edge cases better than structured schemas.
D. Define a clear schema, structure the prompt to request output in that schema, and validate Claude's output against the schema before passing it downstream.

Answer: D

Explanation:
Option C establishes the strongest application boundary between probabilistic model generation and deterministic downstream processing. When another component requires JSON with a known contract, the application should explicitly define the expected structure and ensure that model output conforms to it before downstream execution. Anthropic's current Structured Outputs guidance states that structured outputs constrain responses to a specific schema and are intended to provide valid, parseable data for downstream processing. The current Claude API supports JSON Schema through output_config.format, while SDK helpers can additionally parse and validate returned data.
The underlying engineering principle remains the same even when structured-output enforcement is unavailable: never allow unvalidated model-generated structures to become trusted machine input. Option A provides insufficient contractual control. Option B defines the schema but pushes validation too late, increasing the probability that malformed or semantically invalid data reaches dependent components. Option D sacrifices machine reliability entirely.
Therefore, C correctly combines schema specification, explicit format guidance, and validation. This corresponds to Claude Developer topics covering structured outputs, defensive application design, schema validation, and reliable model-to-system interfaces. The question and options are reproduced from the supplied examination set.

NEW QUESTION # 69
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. Apply skepticism toward confident output by adding validation steps, sourcing requirements, or confidence calibration before treating outputs as ground truth.
B. 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.
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: A

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 # 70
Your Claude application receives untrusted input from external sources. The team is establishing how the application should treat this untrusted input.
Untrusted input would be...

A. Validated and sanitized before being incorporated into prompts, and treated as data the model should not interpret as instructions.
B. Blocked entirely so the application accepts only trusted input, with no path for legitimate external input the application might process.
C. Routed through a separate Claude application with more lax security controls so the original application does not handle the untrusted input directly.
D. Treated identically to trusted input from internal sources, on the grounds that all input the application receives can be handled the same way.

Answer: A

Explanation:
Option A is the appropriate trust-boundary treatment for external content. Untrusted text can contain malformed data, adversarial instructions, prompt-injection attempts, or content deliberately constructed to alter the agent's behavior. It should therefore be validated or screened before inclusion and clearly represented as untrusted data rather than authoritative application instructions.
Anthropic's prompt-injection guidance distinguishes direct attacks from indirect prompt injection, where Claude processes third-party content such as webpages, emails, documents, or tool output containing hostile instructions. Anthropic recommends input validation and screening, least-privilege access, safe treatment of untrusted tool content, and screening content before Claude acts on it. Importantly, Anthropic notes that tool- result content is treated as untrusted data rather than as a reliable place for application instructions.
B erases the trust distinction and exposes the application to instruction/data confusion. C introduces a deliberately weaker security boundary and does not sanitize the data. D prevents legitimate use cases unnecessarily; untrusted does not mean unusable-it means the data must be handled defensively.
Therefore, A correctly combines validation with explicit trust separation. Relevant Study Guide topics:
prompt injection, untrusted content, input validation, sanitization, data/instruction separation, least privilege, and defense in depth.

NEW QUESTION # 71
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. 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.
C. 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.
D. 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.

Answer: D

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 # 72
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