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| Section | Weight | Objectives |
|---|---|---|
| Prompt and Context Engineering | 11% | - Context Management and Long-Context Techniques - Prompt Engineering - Context Engineering |
| Security and Safety | 8.1% | - Secure Tool Use and Guardrails - Prompt Injection and Untrusted Content - Safety and Responsible Development - Application Security |
| Agents and Workflows | 14.7% | - Agent Construction with Claude - Agent Architecture - Agent Patterns and Frameworks |
| Applications and Integration | 33.1% | - API Integration and Application Development - Software Engineering Fundamentals - Claude API and Client SDKs - Streaming, Error Handling and Reliability - Multimodal and Structured Outputs - Message Batches and Prompt Caching |
| Tools and MCPs | 10.6% | - Building Custom Tools and MCP Servers - Model Context Protocol - Tool Use and Tool Schemas |
| Claude Code | 3.1% | - Claude Code Configuration and Extensibility |
| Eval, Testing, and Debugging | 2.6% | - Evaluation, Testing, and Debugging |
| Model Selection and Optimization | 16.8% | - Model Selection - Performance Optimization - Cost and Latency Optimization - Model Capabilities and Trade-offs |
>> Examcollection Anthropic CCDV-F Vce <<
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NEW QUESTION # 16
A teammate has submitted a pull request that adds a Claude-powered feature to your service. The code works, but the prompt and model selection are hard-coded inline, error handling is missing, and there are no tests for the integration.
What would you request during code review?
Answer: C
Explanation:
D is the only option that addresses all three identified production-readiness defects. The supplied Claude Developer item explicitly selects D. A functioning happy path is insufficient for a maintainable Claude integration.
Prompt and model selection are configuration concerns that will change as prompts are evaluated, model versions evolve, or environments require different behavior. They should therefore be separated from unrelated business logic rather than scattered as inline constants. Claude API failures must also be handled deliberately. Anthropic documents typed SDK exceptions and defined HTTP error categories, including invalid requests, authentication failures, rate limits, server errors, overload, and timeouts. The official SDKs additionally retry appropriate transient failures.
Tests are required to verify the integration boundary, including successful behavior, malformed or unexpected responses, API error handling, and critical user workflows. Approving code without those controls pushes known reliability debt directly into production.
A and B knowingly merge incomplete production behavior. C improves configuration and test coverage but leaves a known API-failure path unhandled.
Relevant Claude Developer topics: SW Eng Foundations, code review, separation of configuration, error handling, integration testing, API resilience, maintainability, and production readiness .
NEW QUESTION # 17
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 # 18
Your Claude application's API keys are stored in a secrets manager. The team is debating whether the same key should be used in development, staging, and production environments.
How would you handle the keys across environments?
Answer: D
Explanation:
Option A provides proper environment isolation and credential blast-radius control. Development, staging, and production represent different trust boundaries and should not share the same API credential. If a development machine, CI job, or staging service is compromised, a distinct credential prevents the attacker from automatically gaining the same access to production.
Anthropic's official workspace documentation explicitly recommends using separate workspaces for development, staging, and production. Workspaces can have their own API keys, members, resource limits, and usage tracking, and keys can be scoped to a specific workspace. Anthropic's authentication guidance also instructs developers to store API keys in a secrets manager, rotate them periodically, revoke suspected compromised keys, and use workspaces to scope credentials by project or environment.
B confuses rotation with isolation: rotating a credential that remains shared across all environments does not create independent security boundaries. C deliberately increases blast radius. D merely substitutes one globally shared key for another and therefore has the same architectural flaw.
The supplied exam source marks A as correct. Relevant topics: Confia Management, secrets management, API-key scoping, environment isolation, credential rotation, workspaces, least privilege, and production security.
NEW QUESTION # 19
You are choosing a Claude model for a high-volume classification task. Each classification is straightforward, latency requirements are tight, and per-request cost matters at scale.
Which model would you choose?
Answer: C
Explanation:
Option C matches Anthropic's efficiency-first model-selection guidance. For a straightforward, high-volume workload where latency and unit cost are explicit constraints, the correct starting point is a faster, economical model that can meet the task's quality threshold. Anthropic specifically lists high-volume straightforward tasks, tight latency requirements, and cost-sensitive implementations as cases where an efficiency-first model choice is appropriate.
The crucial qualification is that "smaller" does not mean accepting inadequate quality. The team should evaluate the candidate against representative classification examples and defined accuracy criteria. If it passes, moving to a larger model adds cost and often latency without delivering required business value.
A selects a mid-tier model by convention rather than workload evidence. B multiplies inference calls, generally increasing both latency and cost for a simple classification problem. D optimizes maximum capability even though the task does not require frontier-level reasoning.
Therefore, C is the appropriate initial model choice, followed by workload-specific validation. Relevant Study Guide topics: Claude model selection, efficiency-first design, classification workloads, throughput, latency, per-request economics, evaluation, and quality/cost tradeoffs.
NEW QUESTION # 20
Your Claude agent has access to a tool that retrieves customer records. A teammate has noticed that the agent occasionally calls the tool with arguments the schema does not declare, and the tool's downstream service returns an error each time. The teammate proposes loosening the schema so the tool accepts whatever arguments the model produces.
How would you respond?
Answer: C
Explanation:
The supplied question identifies B as the intended answer. Tool schemas are contracts between Claude and executable application code. When the downstream service accepts only a defined set of arguments, relaxing that schema merely shifts invalid data farther into the system and increases runtime failures.
Anthropic's current tooling provides an even stronger implementation of this principle through strict tool use
. Setting strict: true constrains tool inputs to the declared JSON Schema, preventing undeclared properties, missing required values, and incompatible parameter types where the supported schema subset is used.
Anthropic explicitly recommends strict tool use for validated parameters, type-safe function calls, and reliable agentic workflows.
In a non-strict or legacy implementation, the application should still validate arguments before dispatch and convert invalid calls into structured tool errors that Claude can interpret and potentially correct. A prompt instruction can reinforce behavior, but it should not replace deterministic validation. C and D weaken the system boundary and knowingly send invalid calls downstream.
Therefore, maintain the contract rather than adapting the contract to malformed model output.
Relevant Claude Developer topics: Agent Construction, tool schemas, strict tool use, JSON Schema, parameter validation, structured errors, retry behavior, and defensive execution boundaries .
NEW QUESTION # 21
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