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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Solution Design & Architecture | 17% | - Multi-agent systems and orchestration - Architectural patterns
- End-to-end architecture design - Translating business problems into Claude-based AI solutions - Decomposition techniques for complex problem solving |
| Topic 2: Developer Productivity & Operational Enablement | 7% | - Developer enablement - Claude tooling configuration for teams - AI-assisted developer workflows - Debugging and operational issue resolution |
| Topic 3: Integration | 19% | - RAG pipeline design
- Enterprise system integration |
| Topic 4: Claude Models, Prompting & Context Engineering | 13% | - Prompt reuse and context engineering strategies - Context window optimization - Guardrails - Claude model selection and trade-offs - System prompts and prompt templates |
| Topic 5: Stakeholder Communication & Lifecycle Management | 14% | - Discovery and requirements gathering - Architecture documentation - Communicating architectural decisions - Solution lifecycle management - Stakeholder management - Service-level agreements |
| Topic 6: Evaluation, Testing & Optimization | 16% | - Cost and performance optimization - System issue diagnosis - Evaluation framework design - Evaluation metrics and datasets - Production monitoring and optimization - A/B testing |
| Topic 7: Governance, Safety & Risk Management | 14% | - Human-in-the-loop validation - Security and risk management - AI safety and guardrails - Ethical AI considerations - Regulatory and compliance requirements |
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NEW QUESTION # 41
The compliance team at a firm has approved a Claude Skill that generates client-facing investment summaries. The Skill includes the firm's required disclaimers and prohibited-language list. A product manager has asked whether additional guardrails are needed at the application layer or whether the Skill alone is sufficient.
Which two guardrail responsibilities should remain at the application layer rather than the Skill? (Select two.) Each correct answer presents part of the solution.
Answer: A,D
Explanation:
Authorization and compliance logging must remain enforceable application-layer controls. Option B requires the application to authenticate the requester, resolve the user's role and entitlements, and deny the operation when the user lacks authority. A Skill can instruct Claude how to behave, but it is not a reliable security boundary and cannot replace identity-based access control enforced by the surrounding system.
Option A similarly belongs at the application layer. The application must create an authoritative audit event containing the requester, timestamp, Skill and model version, relevant input or content identifiers, generated output, review status, and retention classification. Audit evidence cannot depend on whether the model remembers or chooses to invoke a logging instruction.
Options C, D, and E describe reusable generation behavior that appropriately belongs within the approved Skill: house style, the standardized disclaimer, and the prohibited-language policy. The application may still validate their presence after generation, but the Skill remains the primary reusable content package.
The correct architecture separates behavioral guidance from deterministic enforcement. Skills supply domain instructions and templates; the application enforces identity, authorization, audit retention, data access, output validation, and consequential-action controls.
Study Guide references/topics: Skills versus application controls; authentication; authorization; compliance logging; deterministic guardrails; policy enforcement boundaries.
NEW QUESTION # 42
An operations engineer reports that a Claude-based pipeline began returning malformed JSON responses after a scheduled maintenance window, causing downstream processing failures.
Which two investigative steps most directly isolate the root cause? (Select two.)
Answer: A,D
Explanation:
The strongest investigation combines configuration comparison with controlled replay. Comparing the current system prompt and output-schema settings against the last known good version identifies maintenance-related configuration drift. Relevant differences include removed formatting instructions, altered schema requirements, incompatible parser expectations, changed stop sequences, or a disabled structured-output mechanism.
Replaying known premaintenance requests against the current configuration creates a controlled test set.
Inspecting the raw response before parsing then establishes whether malformed JSON originates in model generation or is introduced by streaming assembly, transport handling, post-processing, or the downstream parser. If the raw response is valid but the pipeline rejects it, the fault lies after generation. If the raw response is malformed, the prompt, schema enforcement, stop reason, and request configuration become the primary investigation targets.
Clearing the prompt cache assumes cache corruption without evidence. Changing model tiers introduces another variable and weakens causal attribution. Raising `max_tokens` is appropriate only when raw responses show truncation and the returned stop reason indicates the token limit was reached; it is not the most direct initial test.
Study Guide references/topics: [Increasing output consistency](https://docs.anthropic.com/en/docs/test-and- evaluate/strengthen-guardrails/increase-consistency); [agent evaluation methodology] (https://www.anthropic.
com/engineering/demystifying-evals-for-ai-agents); regression replay; last-known-good comparison; raw- output inspection.
NEW QUESTION # 43
You are evaluating retrieval-strategy claims used by a peer team.
For each claim, select yes if the statement is generally accurate. Otherwise, select no.
Answer:
Explanation:
Explanation:
Yes, Yes, Yes, No, No
Dense vector retrieval represents semantic similarity, making it suitable for matching paraphrases and conceptually related language even when the query and source do not share identical words. Sparse lexical retrieval, including BM25-style matching, retains strong sensitivity to exact tokens and is therefore valuable for identifiers, product codes, technical names, and uncommon terminology.
Structured query retrieval is appropriate when the required operation depends on explicit fields, predicates, joins, counts, grouping, or aggregation over a relational schema. In that situation, generating or invoking a constrained database query is more precise than approximating the operation through semantic similarity.
Hybrid retrieval is not identical to dense retrieval. It combines semantic and lexical candidate sets, typically followed by rank fusion or reranking. Anthropic's contextual-retrieval guidance explains that semantic search captures meaning and paraphrases, while BM25 captures exact terminology; combining them improves coverage. Contextual Retrieval, Contextual Retrieval Cookbook Random sampling is not a relevance strategy. It provides no systematic relationship between the query and selected evidence, producing unstable coverage and preventable hallucination risk. Production Q & A requires deterministic or evaluated relevance mechanisms, access filters, suitable indexes, and measurable retrieval metrics such as recall at k.
Study Guide references/topics: Dense retrieval; sparse retrieval; structured queries; hybrid search; rank fusion; retrieval evaluation; production RAG design.
NEW QUESTION # 44
You are operating an interactive assistant whose dominant performance constraint is per-turn latency. Quality on routine turns is already acceptable.
Which configuration adjustment most directly improves latency without disproportionately damaging quality?
Answer: D
Explanation:
Option B targets two major contributors to per-turn latency: retrieved-context size and repeated prompt processing. Reducing retrieval to an empirically validated top- k limits the tokens Claude must process while preserving the passages that historically provide sufficient answer coverage. Caching the stable system- prompt prefix avoids repeatedly processing identical instructions.
Anthropic's latency guidance recommends choosing an appropriate model and reducing unnecessary input and output tokens. Its prompt-caching documentation explains that caching reusable prompt prefixes can reduce both repeated processing cost and latency. Reducing Latency , Prompt Caching The retrieval depth must be selected through evaluation rather than arbitrary truncation. The team should compare answer quality, retrieval recall, latency, and failure rates across candidate values and retain escalation logic for complex queries requiring deeper retrieval.
Option A removes an applicable latency optimization. Option C maximizes context consumption regardless of demonstrated benefit. Option D deliberately selects the slowest model class even though routine-turn quality already meets requirements.
Study Guide references/topics: Latency optimization; top- k retrieval; prompt caching; token reduction; quality-latency evaluation; adaptive retrieval.
NEW QUESTION # 45
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,C
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 # 46
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