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| Section | Weight | Objectives |
|---|---|---|
| Solution Design & Architecture | 17% | - Design end-to-end architectures and feedback loops - Translate business problems into Claude-based AI solutions - Select architectural patterns: workflow, agentic, augmented LLM - Design multi-agent systems and orchestration strategies - Align solutions to business value pillars |
| Claude Models, Prompting & Context Engineering | 13% | - Apply context engineering and context management techniques - Mitigate prompt injection, leaks, and jailbreak risks - Design system prompts, templates, and guardrails - Select appropriate Claude models based on trade-offs |
| Evaluation, Testing & Optimization | 16% | - Test accuracy, reliability, latency, and cost - Implement iterative improvement pipelines - Optimize performance, prompting, and model selection - Define evaluation metrics and success criteria |
| 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 |
| Governance, Safety & Risk Management | 14% | - Address ethical AI considerations and bias mitigation - Manage data privacy and security compliance - Implement guardrails and safety controls - Ensure regulatory compliance (GDPR, HIPAA, etc.) |
| Developer Productivity & Operational Enablement | 7% | - Configure Claude tools and environments for teams - Support debugging, monitoring, and operational resolution - Improve developer workflows with AI-assisted tooling |
| Stakeholder Communication & Lifecycle Management | 14% | - Manage stakeholder feedback and expectation alignment - Document architectures and support full lifecycle phases - Conduct structured discovery and requirement gathering - Communicate architectural decisions and trade-offs |
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NEW QUESTION # 74
You are rolling out monitoring for a Claude-based deployment and must complete the specification steps before instrumenting the deployment.
Which two steps must be completed BEFORE instrumenting the deployment to emit metrics and traces?
(Select two.)
Each correct answer presents part of the solution.
Answer: D,E
Explanation:
Instrumentation must be driven by an explicit measurement specification. The team must first define the metrics and evaluation slices required to detect performance, cost, quality, safety, or reliability failures. Slices should include representative traffic, adversarial inputs, user segments, tools, and relevant content categories.
SLOs and error budgets must also be established before instrumentation because they determine required measurements, aggregation windows, and acceptable failure levels. Alert thresholds cannot be tuned until normal-state data has been collected. Dashboards and on-call documentation depend on the final metric definitions and observed operational behavior. Anthropic supports metrics, event, and trace export through OpenTelemetry, but telemetry is useful only when tied to measurable success and operating objectives.
Claude Code monitoring
NEW QUESTION # 75
You are compiling guardrail tactics for a customer-facing assistant.
Which two tactics belong on the guardrail list?
Each correct answer presents a complete solution.
Answer: A,C
Explanation:
Option C introduces a deterministic validation boundary between probabilistic model output and downstream execution. Structured validation can reject missing fields, invalid types, unauthorized actions, malformed arguments, and values outside approved enumerations before any tool or business process acts on the response. Anthropic recommends Structured Outputs when guaranteed schema conformance is required.
Increase Output Consistency
Option D implements defense in depth. Prompt-level instructions establish intended behavior, while runtime classifiers, content filters, policy engines, output validators, tool permissions, and human confirmation gates provide controls outside the model. Anthropic's guardrail guidance recommends combining safeguards rather than depending on one mechanism. Mitigate Jailbreaks and Prompt Injections Option A creates a bypass mechanism that adversaries may discover or imitate. Option B may alter output variability but is not a security boundary and cannot guarantee policy compliance. Option E places complete reliance on a system prompt, which remains vulnerable to ambiguity, conflicting inputs, model errors, and indirect prompt injection.
Guardrails should be risk-based, independently enforceable where possible, observable, tested against adversarial cases, and proportional to the consequences of downstream actions.
Study Guide references/topics: Layered guardrails; structured validation; runtime content checks; prompt injection; deterministic enforcement; defense in depth.
NEW QUESTION # 76
A Claude architect is leading the discovery phase for a new AI-powered customer service solution.
Which two activities are characteristic of structured discovery and requirement gathering for a Claude-based deployment? (Select two.)
Answer: B,D
Explanation:
Structured discovery begins by eliciting constraints from the people who own the business outcome, operate the process, manage risk, and will use or support the deployed system. Stakeholder workshops in Option A identify measurable expectations concerning accuracy, latency, throughput, cost, privacy, compliance, escalation, and acceptable failure behavior before the solution is scoped.
Option D converts those expectations into production gates. Success criteria must be explicit enough to evaluate empirically, while failure thresholds must determine when the design requires remediation, human review, or rejection. Anthropic's evaluation guidance states that successful LLM applications begin with defined success criteria and evaluations that measure performance against them. It characterizes strong criteria as specific, measurable, achievable, and relevant. Define Success Criteria and Build Evaluations Option B prematurely selects a model before the workload profile is known. Option C can supplement discovery, but reaction-driven prototyping cannot replace documented requirements and acceptance criteria.
Option E postpones essential constraints until architectural commitments may already have been made.
The discovery output should create traceability from stakeholder objectives to requirements, evaluation metrics, architecture decisions, and deployment gates.
Study Guide references/topics: Structured discovery; stakeholder workshops; nonfunctional requirements; success criteria; failure thresholds; production readiness gates.
NEW QUESTION # 77
A technical team is debating whether to implement a new capability for generating regulatory filings from internal data as a Claude Skill or as an MCP server. The capability requires a fixed authoring procedure with embedded examples, does not need to call live external systems, and must be portable across three Claude deployments: claude.ai, an internal API integration, and Claude Code.
Which factor most strongly favors implementing the capability as a Skill?
Answer: D
Explanation:
A Skill is the appropriate packaging mechanism for reusable procedural knowledge, instructions, templates, examples, and supporting resources. The filing capability follows a defined authoring method and requires no live external-system interaction, so an MCP server would introduce unnecessary transport, authentication, hosting, and operational complexity. MCP becomes appropriate when Claude must invoke live databases, APIs, or enterprise services. Command-line availability does not independently determine the design because Skills can be used across supported Claude environments. Direct identity-provider authentication would favor an integration or MCP service rather than a self-contained Skill. Anthropic describes Agent Skills as portable packages that provide agents with specialized procedural and organizational expertise. Agent Skills
NEW QUESTION # 78
You are configuring tool permissions for a Claude-based assistant. The assistant's defined responsibilities require read access to a knowledge base and write access to a draft queue, and nothing else.
Which scoping design best applies least privilege?
Answer: B
Explanation:
Least privilege means granting the exact capabilities required for the defined responsibility-no more and no less. Option B permits the assistant to read the approved knowledge base and write only to the draft queue.
Enforcing the allow-list at the orchestration layer creates a deterministic authorization boundary outside the model's prompt and reasoning.
The rule should also be role-specific so different users or operational roles receive only the tools and data scopes they require. The knowledge-base tool should remain read-only, while the draft-queue tool should be restricted to draft creation rather than external sending, approval, or production publication.
Option A exposes every globally readable tool, creating unnecessary access to unrelated information. Option C relies on voluntary model behavior instead of enforceable authorization. A mistaken tool selection or successful prompt injection could therefore invoke an inappropriate capability. Option D minimizes permissions by eliminating the assistant's required functionality and is not a viable least-privilege implementation.
Anthropic's permission guidance distinguishes allow, ask, and deny rules and confirms that these controls are enforced by Claude Code rather than by model instructions. Configure Permissions Study Guide references/topics: Least privilege; per-role authorization; orchestration-layer enforcement; capability scoping; read/write separation; tool allow-lists.
NEW QUESTION # 79
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