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Anthropic CCDV-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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 2: Agents and Workflows	14.7%	- Agent Architecture and Tradeoffs 1. Evaluate tradeoffs between agentic approaches and traditional workflows 2. Select appropriate agent architectures and patterns - Claude Agent SDK and Agent Loops 1. Implement and manage custom agent loops 2. Build and configure agents using the Claude Agent SDK - Subagents and Agentic Frameworks 1. Use subagents and coordinate multi-agent workflows 2. Apply appropriate agentic frameworks and orchestration patterns
Topic 3: Security and Safety	8.1%	- Secure Application Design 1. Apply secure-by-design practices to Claude-powered applications 2. Protect sensitive data and manage access appropriately - Safety and Guardrails 1. Implement safety controls and guardrails 2. Use hooks and other mechanisms to enforce application controls
Topic 4: Tools and MCPs	10.6%	- Tool Development and Integration 1. Design and implement custom tools for Claude applications and agents 2. Handle tool schemas, invocation, and tool-use results - Model Context Protocol 1. Build and integrate MCP servers 2. Apply MCP concepts and patterns for connecting models to external capabilities
Topic 5: Model Selection and Optimization	16.8%	- Model Selection 1. Evaluate quality, latency, capability, and cost tradeoffs 2. Select appropriate Claude models for task requirements - Performance and Cost Optimization 1. Apply prompt caching and other cost optimization techniques 2. Use batching and other approaches to improve efficiency
Topic 6: 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
Topic 7: Claude Code	3.1%	- Claude Code Configuration and Usage 1. Use Skills, plugins, and Claude Code capabilities effectively 2. Use CLAUDE.md and project configuration 3. Configure settings and development environments
Topic 8: 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. Apply prompting techniques to improve reliability and output quality 2. Design prompts appropriate to application requirements

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Anthropic Claude Certified Developer-Foundations Sample Questions (Q86-Q91):

NEW QUESTION # 86
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. Apply compaction to summarize older portions of the conversation so the gist remains while the specifics carry less weight.
B. Reset the conversation after every turn so the model loses all prior turns when generating a response.
C. Increase the context window size so all turns of the conversation remain visible to the model in full detail.
D. Truncate the conversation so the model sees only the most recent turn during each subsequent response.

Answer: A

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 # 87
You are designing a Claude application that helps medical researchers analyze multi-step clinical case studies.
The application must work through differential diagnoses by considering symptom patterns, weighing evidence across competing hypotheses, and showing intermediate reasoning steps before producing a final recommendation. The team is choosing among Claude's available model options.
The model option best suited to this use case is...

A. A smaller model with a tighter context window, which encourages the model to focus its limited capacity on the task.
B. Fast mode, which prioritizes the lowest possible latency at the expense of reasoning depth on complex tasks.
C. Zero-shot prompting alone with no model option adjustments, which keeps the application's configuration as simple as possible.
D. Extended thinking, which lets the model reason through the differential diagnosis steps before producing the final recommendation.

Answer: D

Explanation:
Option B is the best answer because the task requires deliberate multi-step reasoning over competing hypotheses before a final recommendation. Anthropic's thinking documentation explains that enabling thinking gives Claude additional reasoning capacity before producing the final response, and larger thinking budgets can improve performance on complex analytical tasks. That maps directly to a differential-diagnosis workflow involving evidence comparison, hypothesis elimination, and multi-stage synthesis.
Option A uses ordinary zero-shot prompting and does not specifically allocate additional reasoning capacity.
Option C explicitly optimizes for minimum latency at the expense of reasoning depth, which conflicts with the complexity requirement. Option D reduces model and context capacity and therefore has no principled relationship to better differential reasoning.
One current-platform nuance is important: manual "extended thinking" is the legacy configuration on supported Claude 4.5/4.6 models, while newer models use adaptive thinking. That does not change the question's underlying answer: among the listed choices, B is the only option that intentionally provides a reasoning phase for complex analysis. Relevant Study Guide topics: thinking, reasoning budgets, complex- task model configuration, latency-quality tradeoffs, and model selection.

NEW QUESTION # 88
Your Claude application is hitting context window limits when processing long customer service transcripts.
A junior developer suggests increasing the temperature parameter to fix the issue.
How would you respond?

A. Explain that temperature controls sampling randomness and is unrelated to context capacity, then address the context issue through summarization or chunking.
B. Adjust the temperature parameter together with the max_tokens parameter, treating the combined adjustment as the team's mechanism for managing context window pressure during long-transcript processing.
C. Increase the temperature parameter as the junior developer suggested and observe whether the context window issue resolves over the next several runs of the application in production.
D. Remove the system prompt entirely to make room for longer transcripts in each request, freeing up context window space the system prompt would otherwise consume.

Answer: A

NEW QUESTION # 89
You are building a Claude application that needs to maintain a persistent connection to a service that streams real-time updates. The team is unsure what communication pattern to use.
Which communication pattern would you use?

A. File-based communication where the service writes new updates to disk and the application polls the file system for changes.
B. A WebSocket, because WebSockets are designed for bidirectional, persistent, real-time communication between the client and the streaming service.
C. Repeated short-lived HTTP polling requests, where the application opens a new HTTP connection each time it checks for updates.
D. A single long HTTP request the server holds open indefinitely, with no standard WebSocket framing on the connection.

Answer: B

Explanation:
Option B is the appropriate software-engineering communication pattern when the application requires a persistent, low-latency, bidirectional channel. WebSockets establish a connection using an HTTP Upgrade handshake and then maintain a TCP-based communication channel in which either side can send messages independently. This eliminates the repeated connection setup and request overhead associated with conventional polling.
RFC 6455 defines WebSocket specifically as a protocol enabling two-way communication and explains that it provides a single TCP connection as an alternative to HTTP polling for interactive communication.
Option A can work for infrequent updates, but repeatedly opening HTTP requests adds latency, headers, and server/client overhead and is unsuitable when continuous real-time communication is the stated requirement.
C resembles long polling or an ad-hoc streaming connection but lacks the standardized framing, lifecycle behavior, and interoperability provided by WebSocket. D introduces filesystem polling and is not an appropriate network-streaming architecture.
The important certification principle is selecting a communication mechanism based on application requirements rather than merely choosing an available protocol. For persistent two-way streaming, WebSocket provides the intended abstraction. Relevant Claude Developer topics are software engineering foundations, client-server communication, persistent connections, HTTP versus WebSocket patterns, streaming, and real-time application architecture.

NEW QUESTION # 90
You are designing a Claude application that will process customer support tickets in two stages: a triage stage that classifies tickets and a response stage that drafts replies. The team is debating whether to use a single Claude call that handles both stages or separate Claude calls for each stage.
How would you structure the application?

A. Use multiple Claude calls in parallel that each draft a complete ticket reply, then have a fourth Claude call select the best one to send to the customer.
B. Use a single Claude call for triage and then use a non-Claude rule-based system for response generation, on the grounds that rule-based systems are more reliable for drafting replies.
C. Use a single Claude call for both stages, on the grounds that a single call is cheaper than multiple calls in any production Claude application setup.
D. Use separate Claude calls for triage and response, because each stage has distinct inputs, outputs, and success criteria that benefit from focused prompts.

Answer: D

Explanation:
Option C follows a prompt-chaining/workflow decomposition pattern. Ticket classification and reply generation are materially different tasks. Triage may require a constrained category, priority, or routing decision, while response generation requires richer context, tone, policy compliance, and customer-facing language. Splitting the stages allows each Claude call to use a prompt, context set, model configuration, output format, and evaluation criterion appropriate to that specific responsibility.
Anthropic's guidance on effective agentic systems distinguishes predictable workflows from open-ended agents and recommends using the simplest architecture that meets requirements. Predefined workflows are particularly appropriate where work can be divided into clear processing stages.
This decomposition also creates useful engineering boundaries: the triage output can be validated before becoming input to the drafting stage; triage accuracy and response quality can be evaluated independently; and either stage can later be optimized without rewriting the entire process.
A unnecessarily restricts the second stage to rules. B introduces expensive parallel generation and a selection step without a stated requirement. D optimizes solely for call count while sacrificing task separation and observability.
The supplied exam source marks C. Relevant topics: Agent Patterns, prompt chaining, workflow decomposition, focused prompts, stage-level evaluation, and modular Claude application design.

NEW QUESTION # 91
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