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| Section | Weight | Objectives |
|---|---|---|
| Security and Safety | 8.1% | - Prompt Injection and Untrusted Content - Secure Tool Use and Guardrails - Application Security - Safety and Responsible Development |
| Claude Code | 3.1% | - Claude Code Configuration and Extensibility |
| Tools and MCPs | 10.6% | - Model Context Protocol - Building Custom Tools and MCP Servers - Tool Use and Tool Schemas |
| Applications and Integration | 33.1% | - Claude API and Client SDKs - Software Engineering Fundamentals - Multimodal and Structured Outputs - Streaming, Error Handling and Reliability - Message Batches and Prompt Caching - API Integration and Application Development |
| Eval, Testing, and Debugging | 2.6% | - Evaluation, Testing, and Debugging |
| Prompt and Context Engineering | 11% | - Prompt Engineering - Context Management and Long-Context Techniques - Context Engineering |
| Model Selection and Optimization | 16.8% | - Cost and Latency Optimization - Performance Optimization - Model Capabilities and Trade-offs - Model Selection |
| Agents and Workflows | 14.7% | - Agent Patterns and Frameworks - Agent Architecture - Agent Construction with Claude |
All the Anthropic CCDV-F questions given in the product are based on actual examination topics. Prep4SureReview provides three months of free updates if you purchase the CCDV-F questions and the content of the examination changes after that. Prep4SureReview CCDV-F PDF Questions: The Claude Certified Developer-Foundations (CCDV-F) PDF dumps are suitable for smartphones, tablets, and laptops as well. So you can study actual Anthropic CCDV-F questions in PDF easily anywhere. Prep4SureReview updates Claude Certified Developer-Foundations (CCDV-F) PDF dumps timely as per adjustments in the content of the actual CCDV-F exam.
NEW QUESTION # 35
A new agent your team built handles customer support tickets, but it routinely gets confused when a single ticket spans billing, shipping, and product issues. The agent often loses track of which sub-issue it has already addressed and revisits the same one. The team is considering architectural changes.
What architectural change would you recommend?
Answer: B
NEW QUESTION # 36
Your Claude application makes high-volume API calls during business hours and very few calls overnight.
The team is concerned about staying within rate limits during peak hours and wants to understand how the Claude API enforces those limits.
How would you proceed?
Answer: B
Explanation:
The supplied question marks C , and Anthropic's API documentation directly supports it. Claude API rate limits are enforced using dimensions such as requests per minute (RPM), input tokens per minute (ITPM), and output tokens per minute (OTPM) . Anthropic also notes that short traffic bursts can exceed effective limits even when a longer-term average appears acceptable.
Applications should therefore determine the organization's actual configured limits, model peak traffic against those boundaries, throttle or queue work as necessary, and handle 429 responses correctly. Rate-limit responses include a retry-after value indicating when another request should be attempted. Anthropic's official SDKs automatically retry transient connection failures, rate-limit errors, and server errors with exponential backoff by default.
Streaming does not exempt requests from rate limits, making A technically incorrect. Consolidating payloads in B may reduce request count but could increase token consumption and does not by itself address all rate- limit dimensions. D may smooth traffic, but it is only one optimization and does not substitute for rate-limit- aware application logic.
Relevant Claude Developer topics: Claude API Mechanics, rate limits, RPM, ITPM, OTPM, HTTP 429, retry-after, exponential backoff, throttling, and capacity planning .
NEW QUESTION # 37
You are designing a Claude application that processes user-submitted text. Some of that text could include sensitive information such as account numbers or passwords that the application should not send to Claude.
How would you design the application?
Answer: C
Explanation:
Option A is correct because sensitive-data protection must occur before prohibited information crosses the application's trust boundary. If account numbers, passwords, or other secrets must not be sent to Claude, asking Claude to "ignore" those values after transmission does not satisfy the requirement. The application must determine which data is permitted to leave its controlled environment and transform or reject prohibited content before constructing the Claude request.
This principle is consistent with Anthropic's defense-in-depth guidance. Anthropic recommends input screening, limiting Claude's access to sensitive information, and applying least privilege so the model receives only information necessary for the task.
B is fundamentally too late: the sensitive information has already been transmitted to the model context before the system instruction can influence behavior. C is worse because logging can create an additional store containing the sensitive information and only detects exposure after it occurs. D implements some boundary filtering but deliberately leaves coverage incomplete until production incidents reveal additional patterns.
Therefore, A requires the boundary and its permitted data flows to be defined first, then enforced comprehensively through filtering, redaction, tokenization, or rejection. Relevant Study Guide topics:
application boundaries, data minimization, sensitive-data handling, filtering, redaction, least privilege, and privacy-preserving design.
NEW QUESTION # 38
You are designing an agent that processes vendor invoices. The work involves a small number of well- understood steps, but occasionally an invoice arrives in an unexpected format that requires the system to decide between rerouting, requesting clarification, or flagging for human review.
The most appropriate architecture for this system is...
Answer: D
Explanation:
Option D applies the correct hybrid pattern. Anthropic distinguishes workflows-where LLMs and tools execute along predefined code paths-from agents, where the model dynamically determines how to proceed.
Anthropic recommends choosing the simplest architecture that meets the requirement: workflows provide predictability and consistency for well-understood tasks, while agents are valuable where flexible model- driven decisions are necessary.
Standard invoice processing is explicitly described as a small number of known steps. That makes a deterministic workflow the appropriate default because the path can be tested, monitored, and reproduced.
The unexpected-format branch is different: the system must reason among rerouting, requesting clarification, or human escalation. That decision point is where agentic flexibility adds value.
A makes the entire process autonomous even though most steps do not require autonomy, increasing cost, latency, and unpredictability. B introduces multiple specialized agents despite no demonstrated need for that complexity. C collapses deterministic processing and exception handling into one monolithic model call, making validation and debugging harder.
Therefore, D combines workflow predictability with targeted agentic reasoning. Relevant Study Guide topics:
workflows versus agents, routing, exception handling, hybrid agentic architecture, escalation, and complexity minimization.
NEW QUESTION # 39
Your agent makes 10 to 15 tool calls per task, and you have noticed it sometimes loses track of earlier results by the time it reaches later steps. The agent's context window is large enough to hold all the messages, but the relevant information appears to get buried as the conversation grows.
How would you address this?
Answer: C
Explanation:
Option D is correct because the problem is not insufficient nominal context capacity; it is degraded signal quality as the working context grows. Anthropic's context-window guidance explicitly states that more context is not automatically better. As conversations expand, recall and accuracy can degrade through
"context rot," so long-running agents need active context management rather than simply retaining every prior tool result.
A good pattern preserves the active task state while compacting, summarizing, or pruning stale intermediate outputs. Anthropic documents server-side compaction for long-running conversations and context-editing
/pruning approaches that clear old tool results when they no longer contribute useful information. This keeps high-value constraints and conclusions visible without carrying every verbose response forward.
Option A changes frameworks without fixing the architectural cause. Option B may reduce call count but also creates larger multi-purpose tools and does not guarantee better context quality. Option C increases capacity while preserving the same low-signal accumulation, so the relevant facts can remain buried.
Therefore, D best implements context engineering for agentic workloads. Relevant Study Guide topics:
context engineering, context rot, compaction, tool-result pruning, long-running agents, state preservation, and context-window optimization.
NEW QUESTION # 40
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