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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Evaluation, Testing & Optimization | 16% | - Evaluation metrics and datasets - A/B testing - System issue diagnosis - Evaluation framework design - Cost and performance optimization - Production monitoring and optimization |
| Topic 2: Governance, Safety & Risk Management | 14% | - Security and risk management - Regulatory and compliance requirements - AI safety and guardrails - Human-in-the-loop validation - Ethical AI considerations |
| Topic 3: Solution Design & Architecture | 17% | - Decomposition techniques for complex problem solving - Alignment with business value, cost, performance, and SLAs - End-to-end architecture design - Translating business problems into Claude-based AI solutions - Multi-agent systems and orchestration - Architectural patterns
|
| Topic 4: Claude Models, Prompting & Context Engineering | 13% | - Context window optimization - Prompt reuse and context engineering strategies - Claude model selection and trade-offs - System prompts and prompt templates - Guardrails |
| Topic 5: Developer Productivity & Operational Enablement | 7% | - Developer enablement - AI-assisted developer workflows - Claude tooling configuration for teams - Debugging and operational issue resolution |
| Topic 6: Integration | 19% | - Enterprise system integration - Authentication and authorization analysis - Claude integration mechanisms
|
| Topic 7: Stakeholder Communication & Lifecycle Management | 14% | - Discovery and requirements gathering - Solution lifecycle management - Architecture documentation - Communicating architectural decisions - Stakeholder management - Service-level agreements |
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NEW QUESTION # 31
You are selecting a protocol for a single low-latency stateless tool call from a Claude-based assistant to an internal pricing service that already exposes a stable HTTP API.
Which integration mechanism is the most appropriate?
Answer: B
Explanation:
A direct call to the existing stable HTTP endpoint is the simplest mechanism that satisfies the stated requirements. The interaction is stateless, requires only one tool invocation, and has a strict latency objective.
Adding session management, message-bus translation, or another model-mediated agent would introduce unnecessary network hops, operational dependencies, failure modes, and processing latency.
The assistant's tool implementation should validate input parameters, authenticate through a server-side credential mechanism, apply narrowly scoped authorization, set explicit timeouts, and validate the pricing response before returning it to Claude. Credentials must never be supplied by or exposed to the model.
Appropriate logging should capture request attribution, endpoint outcome, and timing without unnecessarily recording sensitive data.
Anthropic describes the Claude API as a RESTful interface and supports custom tools in which the application executes the requested function or API operation. Claude API Overview , Tool Use Overview MCP could be justified when standardized discovery or reuse across clients is required, but the scenario already supplies a stable API and does not establish that additional requirement.
Study Guide references/topics: Protocol selection; direct API integration; stateless calls; latency minimization; scoped credentials; avoiding unnecessary agentic complexity.
NEW QUESTION # 32
You are integrating Claude Code into a workflow that runs against a production database.
Which guardrail design most directly preserves safety on data-modifying operations?
Answer: C
Explanation:
Option D applies three complementary controls. First, a read-only database credential creates an authorization boundary outside the model; prompt instructions alone cannot convert that credential into write access.
Second, restricting the subagent's tool list reduces capability exposure by preventing the agent from selecting unrelated or unnecessarily privileged operations. Third, explicit human confirmation creates a deliberate approval gate before any exceptional data-changing action is executed.
This is defense in depth. If Claude misinterprets a request or processes malicious instructions from untrusted content, the restricted credential and tool configuration limit the available action surface. Human review then protects operations with potentially irreversible production consequences. Logging and audit trails should remain enabled to record the actor, request, tool call, approval, affected records, and outcome.
Anthropic documents that Claude Code begins with read-only permissions in Manual mode and requests approval for actions that modify the environment. Its permission system also supports granular allow, ask, and deny controls for MCP tools and subagents. Claude Code Security , Configure Permissions Options A and B eliminate least privilege and approval boundaries. Option C removes essential detection and forensic evidence.
Study Guide references/topics: Production database safety; least privilege; read-only defaults; MCP permissions; subagent scoping; human confirmation; auditability.
NEW QUESTION # 33
Engineering leadership wants to roll out Claude Skills to 280 developers across 14 teams. Skills will encode internal coding standards, code-review checklists, and incident-postmortem templates. Leadership has asked how to govern Skill authorship so that Skills remain trustworthy without bottlenecking on a single central team.
Which governance model should you recommend?
Answer: D
Explanation:
Federated authorship allows each domain team to maintain the procedural knowledge it understands while preserving organization-wide trust through a central publication gate. Team authors can update coding standards, review practices, and postmortem procedures without waiting for a single platform group to write every change. Central reviewers can validate ownership, security, tool permissions, duplication, versioning, testing evidence, and compliance before publication. Per-developer or fully decentralized publication creates inconsistent and potentially unsafe Skills. Fully centralized authorship becomes a scalability bottleneck and separates content maintenance from domain expertise. Anthropic describes Skills as packages of instructions, scripts, and resources and warns that repository Skills may grant broad tool access, making formal review essential. Claude Code Skills
NEW QUESTION # 34
You are reviewing a peer's end-to-end design for a Claude-based platform expected to scale to thousands of concurrent users.
For each statement, indicate Yes if it reflects sound architectural practice. Otherwise, select No.
Answer:
Explanation:
Explanation:
* Authentication and authorization run before retrieval so retrieval can filter by identity - Yes
* An asynchronous queue absorbs bursty traffic between intake and the model-invocation layer - Yes
* Tax computation is encoded directly in the system prompt rather than in code - No
* Conversation logs include unredacted government identifiers to maximize tuning signal - No Authentication and authorization must precede retrieval so unauthorized information never enters model context. An asynchronous queue is appropriate for burst absorption, backpressure, controlled concurrency, and retry management at scale. Deterministic tax computation should be implemented in validated code or a controlled calculation tool, not delegated to probabilistic prompt interpretation. Including unredacted government identifiers in conversation logs violates data-minimization principles and creates unnecessary privacy, security, and regulatory exposure. Sensitive fields should be removed, tokenized, or redacted before logging or model use unless specifically required and authorized. The resulting design separates deterministic computation, access control, scalable orchestration, and language reasoning into appropriate architectural layers.
NEW QUESTION # 35
A Claude-based research assistant begins producing responses that confidently contradict its retrieved source documents despite no change to the retrieval pipeline.
Which two diagnostic actions most directly identify the root cause of this behavior? (Select two.)
Answer: B,E
Explanation:
The retrieval pipeline is stated to be unchanged, so the first investigation should isolate changes in generation behavior and grounding instructions. Replaying the failing cases on the previous model version determines whether the regression follows the model upgrade or current model-prompt combination. Inspecting the system prompt establishes whether source-use rules, uncertainty behavior, citation requirements, or prohibitions against unsupported claims were removed or weakened. Increasing context or replacing the embedding model changes a component for which no failure evidence exists and may introduce additional noise. Lowering temperature may reduce variation but does not explain systematic contradiction of retrieved evidence. The investigation should compare identical requests, retrieved passages, prompts, and model versions before applying a fix. Define success criteria and evaluations
NEW QUESTION # 36
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