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| Section | Weight | Objectives |
|---|---|---|
| Claude Models, Prompting & Context Engineering | 13% | - Claude model selection and trade-offs - Context window optimization - Guardrails - Prompt reuse and context engineering strategies - System prompts and prompt templates |
| Stakeholder Communication & Lifecycle Management | 14% | - Discovery and requirements gathering - Service-level agreements - Architecture documentation - Stakeholder management - Solution lifecycle management - Communicating architectural decisions |
| Developer Productivity & Operational Enablement | 7% | - Debugging and operational issue resolution - AI-assisted developer workflows - Claude tooling configuration for teams - Developer enablement |
| Integration | 19% | - RAG pipeline design
- Enterprise system integration - Claude integration mechanisms
|
| Solution Design & Architecture | 17% | - End-to-end architecture design - Decomposition techniques for complex problem solving - Multi-agent systems and orchestration - Translating business problems into Claude-based AI solutions - Alignment with business value, cost, performance, and SLAs - Architectural patterns
|
| Evaluation, Testing & Optimization | 16% | - System issue diagnosis - Evaluation framework design - Cost and performance optimization - Production monitoring and optimization - Evaluation metrics and datasets - A/B testing |
| Governance, Safety & Risk Management | 14% | - Human-in-the-loop validation - Ethical AI considerations - Security and risk management - AI safety and guardrails - Regulatory and compliance requirements |
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NEW QUESTION # 62
You are defining when to introduce a project subagent versus relying on Claude Code's general capabilities.
Which scenario most directly justifies a dedicated subagent?
Answer: A
Explanation:
A dedicated subagent is justified when specialization is recurring and operationally valuable. Option A allows the team to encode a database-review role once, including its system prompt, triggering description, permitted tools, model, and review criteria. This improves consistency across sessions, isolates specialized context, and applies least privilege by withholding unrelated capabilities. Anthropic's custom subagent documentation supports task-specific prompts, model selection, tool allowlists, permission modes, hooks, skills, and persistent memory. A one-time question does not justify the maintenance overhead of a reusable agent definition, while Options C and D explicitly provide no specialization requirement. The subagent should also have clear invocation conditions, evaluation cases, ownership, and lifecycle review.
Study Guide references/topics: Claude Code subagents; task specialization; context isolation; model routing; scoped permissions; reusable automation.
NEW QUESTION # 63
A technical team is cataloging risks specific to Claude's use in a document-grounded Q & A system.
Which two items represent failure modes that are intrinsic to LLM-based systems rather than generic software defects? (Select two.)
Answer: B,D
Explanation:
Options A and B describe model-specific behavioral risks. An overly broad safety response is a false positive caused by how the language model interprets contextual or surface-level signals. A plausible but unsupported answer is a hallucination: the generated statement appears credible but lacks grounding in the retrieved evidence. Anthropic recommends grounding factual responses in source material, requiring citations, allowing Claude to express uncertainty, and verifying generated claims. Options C, D, and E are conventional infrastructure or application failures involving certificates, routing, and database connectivity. They can occur in systems that do not use an LLM and should be addressed through standard monitoring, configuration management, retry policies, and operational testing. Anthropic: Reduce hallucinations
NEW QUESTION # 64
You are responding to an adversarial input pattern in which users include text claiming admin authority and instructing the model to bypass safety restrictions.
Which combination of controls most effectively mitigates this attack pattern?
Answer: B
Explanation:
Self-declared administrative authority inside a prompt is untrusted data, not authenticated identity or authorization evidence. Option B correctly combines independent controls across the model, runtime, authorization, and monitoring layers.
Prompt instructions establish that user content cannot override system policy. Runtime classifiers detect known and generalized attempts to bypass controls. Tool permissions are enforced outside the model and must derive from authenticated identity, role, and approved scope-not statements contained in the conversation. Audit logging records the actor, attempted override, classifier result, tool requests, and final disposition for investigation and control improvement.
Anthropic recommends input screening, hardened system prompts, safe handling of untrusted content, narrowly scoped permissions, red-team testing, and continuous monitoring. Mitigate Jailbreaks and Prompt Injections Option A depends entirely on model behavior and provides no containment if the model fails. Option C removes the protections the attacker is attempting to defeat. Option D commits a fundamental authorization error by accepting an unverified claim as privilege elevation.
The strongest design also rate-limits repeated attempts, escalates suspicious activity, validates outputs, and requires human confirmation for consequential actions.
Study Guide references/topics: Direct prompt injection; untrusted user content; runtime classifiers; non-model authorization; scoped tools; audit logging; defense in depth.
NEW QUESTION # 65
You are evaluating model-selection claims used by a peer team.
For each statement, select yes if the statement is generally true. Otherwise, select no.
Answer:
Explanation:
Explanation:
Yes, Yes, No, Yes, No
More capable reasoning configurations generally require greater computation, produce higher latency, and may consume larger token or rate-limit allocations than lighter alternatives. This makes the first statement generally accurate, although actual performance must be measured under the organization's specific provider and service tier.
Representative workload testing is essential because generic benchmark rankings do not establish performance on the organization's prompts, data, edge cases, tools, or required output format. Anthropic recommends benchmark tests specific to the use case, using actual prompts and data, followed by comparison of accuracy, quality, edge-case behavior, and cost. Choosing the Right Model The strongest model is not automatically the correct production choice. Model selection must balance capability, speed, cost, throughput, and operational constraints. Tiered routing implements this principle by serving predictable requests with an efficient model and escalating difficult cases to a more capable model.
Anthropic documents multi-model strategies in which lower-cost workers handle most traffic while harder decisions are escalated.
Recency is also not a valid selection criterion. A newly announced model must pass the workload's existing evaluation, security, latency, and cost gates before replacing a validated production model.
Study Guide references/topics: Model trade-offs; representative evaluations; tiered routing; escalation architectures; cost-capability optimization; model migration validation.
NEW QUESTION # 66
A customer support team has proposed delegating customer refund decisions to a Claude-driven workflow with no human review for refunds under 50 USD. The team's reasoning is that small refunds are low-risk and human review would erase the efficiency gain.
Which Delegation-competency principle should guide your response?
Answer: D
Explanation:
Transaction value is only one component of risk. Even a small refund can create fraud exposure, discriminatory outcomes, contractual violations, account compromise, policy inconsistency, or cumulative financial loss. Delegation should therefore depend on the nature, detectability, reversibility, and aggregate impact of failure-not a monetary threshold alone. Option B supports risk-stratified automation: routine, well- evidenced cases may proceed automatically, while anomalous, identity-sensitive, disputed, or policy-edge cases require review or escalation. Anthropic's trustworthy-agent principles emphasize maintaining meaningful human control rather than imposing universal review or unrestricted autonomy. Option A is unnecessarily absolute; Option C prioritizes efficiency over governance; and Option D prohibits potentially safe, controlled automation without risk analysis.
Study Guide references/topics: Delegation competency; human control; risk classification; reversibility; cumulative exposure; escalation design.
NEW QUESTION # 67
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