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Anthropic CCDV-F Exam Syllabus Topics:

Section	Weight	Objectives
Claude Code	3.1%	- Claude Code Configuration and Usage 1. Configure settings and development environments 2. Use Skills, plugins, and Claude Code capabilities effectively 3. Use CLAUDE.md and project configuration
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. Use batching and other approaches to improve efficiency 2. Apply prompt caching and other cost optimization techniques
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. Build and configure agents using the Claude Agent SDK 2. Implement and manage custom agent loops - Subagents and Agentic Frameworks 1. Apply appropriate agentic frameworks and orchestration patterns 2. Use subagents and coordinate multi-agent workflows
Security and Safety	8.1%	- Safety and Guardrails 1. Use hooks and other mechanisms to enforce application controls 2. Implement safety controls and guardrails - Secure Application Design 1. Apply secure-by-design practices to Claude-powered applications 2. Protect sensitive data and manage access appropriately
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. Test Claude integrations and agentic systems 2. Diagnose and debug application, agent, and integration issues
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. Design prompts appropriate to application requirements 2. Apply prompting techniques to improve reliability and output quality
Applications and Integration	33.1%	- Claude API and Client SDKs 1. Construct and process API requests and responses 2. Implement streaming and handle API errors 3. Integrate applications with the Claude API and supported client SDKs - 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
Tools and MCPs	10.6%	- Model Context Protocol 1. Apply MCP concepts and patterns for connecting models to external capabilities 2. Build and integrate MCP servers - Tool Development and Integration 1. Handle tool schemas, invocation, and tool-use results 2. Design and implement custom tools for Claude applications and agents

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Anthropic Claude Certified Developer-Foundations Sample Questions (Q53-Q58):

NEW QUESTION # 53
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. 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.
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. Explain that temperature controls sampling randomness and is unrelated to context capacity, then address the context issue through summarization or chunking.
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: C

NEW QUESTION # 54
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. A single long HTTP request the server holds open indefinitely, with no standard WebSocket framing on the connection.
B. Repeated short-lived HTTP polling requests, where the application opens a new HTTP connection each time it checks for updates.
C. File-based communication where the service writes new updates to disk and the application polls the file system for changes.
D. A WebSocket, because WebSockets are designed for bidirectional, persistent, real-time communication between the client and the streaming service.

Answer: D

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 # 55
Your Claude application produces good responses for typical inputs but struggles with edge cases. You have several labeled examples of edge-case inputs and the desired response for each. You want to use these examples to improve the model's handling of edge cases.
What is the best way to use these examples?

A. Train a custom model on the edge-case examples and deploy that custom model in place of the team's current Claude integration.
B. Add the labeled edge-case examples to the prompt as few-shot examples so the model can learn the pattern.
C. Embed the examples in a database for the model to find during inference.
D. Tell users to avoid submitting the edge-case inputs to the application by adding warnings in the application's user interface.

Answer: B

Explanation:
Option B applies few-shot, or multishot, prompting, one of Anthropic's recommended techniques for steering Claude when examples of desired behavior are available. Labeled input/output pairs give Claude concrete demonstrations of how it should respond, which is particularly valuable when edge cases are difficult to express completely through abstract rules.
Anthropic states that examples are among the most reliable mechanisms for steering output format, tone, and structure. Its prompting guidance recommends relevant, diverse examples that cover edge cases while avoiding accidental patterns. For best results, examples should be clearly separated from the main instructions, such as by using < example > and < examples > tags.
A retrieval database could be useful if a very large or dynamically selected example collection were required, but that adds unnecessary complexity for the small labeled set described. C is disproportionate: a few examples do not justify replacing the application's Claude integration with custom model training. D avoids rather than solves the identified failure mode.
Therefore, B directly uses the available supervision at inference time and allows rapid iteration through evaluation. Relevant Claude Developer topics are prompt construction, few-shot prompting, edge-case handling, example selection, evaluation-driven iteration, and behavioral steering.

NEW QUESTION # 56
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. Identify which questions have been refused and compare them against previously answered questions to determine what changed between the successful and refused interactions.
B. Assess whether the refusals cluster around specific input patterns, question types, or time periods by analyzing the distribution of refused requests in the logs.
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 # 57
The team is debating whether to integrate with the Claude API directly or through a third-party abstraction layer that supports multiple LLM providers. The team has identified that all current and projected use cases run on Claude, no internal customer has requested LLM portability, and the team's product roadmap does not mention multi-provider support over the next two years. The third-party abstraction would add roughly 15 percent overhead in code complexity and introduce one additional dependency.
Which integration approach would you recommend?

A. The third-party abstraction layer, on the grounds that multi-provider support is valuable for any application as a matter of long-term flexibility across vendors.
B. Both integration paths in parallel, where the application uses each path on different runs to compare which performs better in production over time.
C. Direct integration with Claude through its SDK, because no multi-provider need exists and abstraction would add complexity that does not pay off.
D. A custom multi-provider abstraction layer the team builds in-house so that the team controls every part of the abstraction the application uses for its API calls.

Answer: C

Explanation:
D follows the principle of choosing the simplest architecture that satisfies demonstrated requirements .
The supplied examination item explicitly marks D. In this scenario, portability is neither a present functional requirement nor a foreseeable roadmap requirement. Adding an abstraction therefore creates measurable complexity without delivering an identified product capability.
Anthropic provides official general-purpose SDKs for Claude in multiple languages. These expose the Messages API directly while adding idiomatic interfaces, type safety, streaming support, retries, and error handling. Anthropic separately describes compatibility layers and framework-specific libraries as alternative integration surfaces rather than requirements for ordinary Claude development.
A third-party abstraction becomes rational when there is a genuine requirement for multiple providers, vendor switching, standardized cross-model interfaces, or an existing architectural platform that mandates it. None exists here. Building that flexibility speculatively increases dependencies, debugging surface, version- compatibility work, and the possibility that provider-specific Claude capabilities are hidden behind a lowest- common-denominator API.
A optimizes for hypothetical future requirements. B doubles operational complexity. C creates an even larger maintenance burden.
Relevant Claude Developer topics: Claude App Design, SDK selection, abstraction boundaries, dependency management, YAGNI, integration architecture, and provider portability requirements .

NEW QUESTION # 58
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