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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Evaluation, Testing & Optimization | 16% | - Implement iterative improvement pipelines - Test accuracy, reliability, latency, and cost - Define evaluation metrics and success criteria - Optimize performance, prompting, and model selection |
| Topic 2: Integration | 19% | - Design authentication, authorization, and observability - Integrate with data pipelines and RAG systems - Integrate Claude with enterprise systems, APIs, and tools - Implement Model Context Protocol (MCP) integrations |
| Topic 3: Developer Productivity & Operational Enablement | 7% | - Improve developer workflows with AI-assisted tooling - Support debugging, monitoring, and operational resolution - Configure Claude tools and environments for teams |
| Topic 4: Solution Design & Architecture | 17% | - Select architectural patterns: workflow, agentic, augmented LLM - Align solutions to business value pillars - Design end-to-end architectures and feedback loops - Design multi-agent systems and orchestration strategies - Translate business problems into Claude-based AI solutions |
| Topic 5: Claude Models, Prompting & Context Engineering | 13% | - Apply context engineering and context management techniques - Design system prompts, templates, and guardrails - Select appropriate Claude models based on trade-offs - Mitigate prompt injection, leaks, and jailbreak risks |
| Topic 6: Stakeholder Communication & Lifecycle Management | 14% | - Manage stakeholder feedback and expectation alignment - Document architectures and support full lifecycle phases - Communicate architectural decisions and trade-offs - Conduct structured discovery and requirement gathering |
| Topic 7: Governance, Safety & Risk Management | 14% | - Address ethical AI considerations and bias mitigation - Implement guardrails and safety controls - Ensure regulatory compliance (GDPR, HIPAA, etc.) - Manage data privacy and security compliance |
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NEW QUESTION # 80
You are classifying token-management tactics by where each tactic applies in the request lifecycle: "Input Preparation," "Prompt Construction," or "Output Handling."
Answer:
Explanation:
Explanation:
* Persist the validated output for downstream consumption and audit - Output Handling
* Order prompt sections so cacheable content appears before per-request content - Prompt Construction
* Summarize prior conversation history when full history is no longer needed - Input Preparation
* Validate the model's structured output against the expected schema - Output Handling
* Move stable repeated content into a cacheable prefix at the beginning of the prompt - Prompt Construction
* Trim retrieved passages to spans relevant to the user's question - Input Preparation Input preparation determines what evidence and history should enter the request, so summarization and retrieval trimming belong there. Prompt construction determines ordering and cache boundaries; stable repeated instructions must precede dynamic per-request content to maximize cache reuse. Output handling begins after generation and includes schema validation, persistence, auditing, and downstream delivery.
Mixing these responsibilities creates inefficient prompts and weak validation boundaries. Anthropic's context guidance emphasizes curating the smallest useful context because unnecessary tokens can reduce recall and accuracy. Prompt caching similarly depends on a stable reusable prefix, while structured outputs or schema validation protect downstream systems from malformed responses. Context windows; prompt caching
NEW QUESTION # 81
A managed agent deployment for claims triage has grown from 6 tools to 34 tools over 18 months as product teams added capabilities. Triage accuracy has declined from 91 percent to 78 percent, and average tool- selection latency has increased by 2.3 seconds. A junior engineer has proposed adding a tool-router agent in front of the current agent to filter the tool list per request.
Which two findings should you present to justify capability decomposition before adding the router? (Select two.) Each correct answer presents part of the solution.
Answer: D,E
Explanation:
Four distinct workflow domains indicate that the agent's responsibilities have expanded beyond a cohesive capability boundary. Decomposing the deployment into domain-focused agents reduces each agent's tool- selection space and produces clearer prompts, permissions, evaluations, and ownership. Option E strengthens this conclusion because placing a model-based router before the existing overloaded agent adds latency, cost, and another failure point without correcting the underlying capability sprawl. Overlapping descriptions should be clarified, while unused tools may simply be removed; neither finding alone proves that separate domain agents are required. Public documentation of a pattern is not evidence that it fits this workload. Anthropic emphasizes simple composable patterns, clearly differentiated tools, and empirical evaluation before adding orchestration complexity. Building Effective Agents
NEW QUESTION # 82
You are compiling team-setup practices for a Claude Code rollout across an engineering organization.
Which two practices belong on the list? (Select two.)
Each correct answer presents a complete solution.
Answer: C,D
Explanation:
Project scope is designed for repository-specific configuration shared through version control, including MCP server definitions, project permissions, hooks, and team instructions. This gives every contributor a consistent baseline while keeping changes reviewable. Managed configuration is appropriate for mandatory security and compliance controls because administrators can deploy it centrally and prevent lower scopes from overriding it. Local scope is machine-specific and unsuitable for organization-wide enforcement. User scope follows one engineer across projects, so it cannot guarantee that an entire team receives identical MCP definitions.
Personal editor preferences belong in user or local configuration, not project scope, unless the team has explicitly standardized them. Anthropic's settings hierarchy distinguishes managed, project, local, and user responsibilities. Claude Code settings
NEW QUESTION # 83
You are selecting a model for a new production workload and must complete the upstream steps before testing candidate models empirically.
Which two steps must be completed BEFORE running a representative sample on a candidate model? (Select two.) Each correct answer presents part of the solution.
Answer: A,D
Explanation:
Empirical model testing requires a defined decision frame. Option D must occur first because the quality threshold, acceptable latency, expected request volume, and associated cost envelope determine what constitutes a successful result. Without these criteria, a representative-sample test can produce measurements but cannot support a defensible selection.
Option C is the next upstream step. Known model capabilities and the workload profile are used to identify a plausible candidate rather than testing models arbitrarily. Anthropic's model-selection guidance recommends evaluating capability, speed, cost, and effort requirements before narrowing the candidate set. Choosing the Right Model Option A occurs after empirical testing because the architect cannot know which lightest model consistently meets the quality bar until sample results have been evaluated. Option B belongs to ongoing lifecycle management after a new version becomes available. Option E is a deployment-governance activity that follows successful model selection, security review, and readiness validation.
The correct sequence is: define workload criteria, shortlist candidate models, run a representative evaluation, compare quality and operational metrics, select the least costly model that reliably satisfies all gates, and periodically retest the decision.
Study Guide references/topics: Requirements-driven model selection; representative sampling; quality thresholds; latency and volume requirements; capability-cost trade-offs; model lifecycle reassessment.
NEW QUESTION # 84
You are compiling continuity practices that span the deployment lifecycle.
Which two practices belong on the list? (Select two.)
Each correct answer presents a complete solution.
Answer: B,E
Explanation:
Lifecycle continuity depends on preserving validated knowledge and feeding operational learning back into subsequent phases. Carrying the evaluation framework and reference set forward, as stated in Option B, creates a stable baseline across prompt changes, model migrations, retrieval adjustments, and architectural revisions. Rebuilding the evaluation system each time would undermine longitudinal comparison because changes in the test framework could be mistaken for changes in solution performance.
Option D establishes the second essential continuity mechanism: lessons from discovery, design, implementation, deployment, and production monitoring become explicit inputs to the next phase. This closes the feedback loop and prevents recurring defects, invalid assumptions, and operational findings from being lost at organizational handoffs.
Option A is overly mechanical. Stakeholders should receive communications relevant to their responsibilities and decision rights, not indiscriminate notifications at every transition. Option C confuses comprehensive archival with lifecycle continuity; retention must follow business, regulatory, security, and records- management requirements rather than an unconditional "archive everything" policy. Option E is directly contrary to iterative architecture. Decisions should be documented and governed, but material evidence or changed requirements must be allowed to reopen them.
Study Guide references/topics: Lifecycle phases; evaluation continuity; reference datasets; feedback loops; lessons learned; decision records; iterative architecture governance.
NEW QUESTION # 85
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