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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Governance, Safety & Risk Management | 14% | - Manage data privacy and security compliance - Implement guardrails and safety controls - Address ethical AI considerations and bias mitigation - Ensure regulatory compliance (GDPR, HIPAA, etc.) |
| Topic 2: Integration | 19% | - Integrate with data pipelines and RAG systems - Design authentication, authorization, and observability - Implement Model Context Protocol (MCP) integrations - Integrate Claude with enterprise systems, APIs, and tools |
| Topic 3: Claude Models, Prompting & Context Engineering | 13% | - Apply context engineering and context management techniques - Mitigate prompt injection, leaks, and jailbreak risks - Select appropriate Claude models based on trade-offs - Design system prompts, templates, and guardrails |
| Topic 4: Stakeholder Communication & Lifecycle Management | 14% | - Communicate architectural decisions and trade-offs - Document architectures and support full lifecycle phases - Conduct structured discovery and requirement gathering - Manage stakeholder feedback and expectation alignment |
| Topic 5: Solution Design & Architecture | 17% | - Design end-to-end architectures and feedback loops - Align solutions to business value pillars - Translate business problems into Claude-based AI solutions - Select architectural patterns: workflow, agentic, augmented LLM - Design multi-agent systems and orchestration strategies |
| Topic 6: 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 7: Developer Productivity & Operational Enablement | 7% | - Support debugging, monitoring, and operational resolution - Configure Claude tools and environments for teams - Improve developer workflows with AI-assisted tooling |
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NEW QUESTION # 71
You are assessing a Claude-based system whose dominant risk is silent quality drift on safety-relevant outputs after a model-version upgrade.
Which assessment activity most directly addresses this risk?
Answer: A
Explanation:
A stable adversarial regression set provides a controlled basis for detecting behavioral change between model versions. Version-attributed results show whether the proposed upgrade improves, preserves, or degrades performance on the same safety-relevant cases. The evaluation must run before promotion so a regression can block deployment rather than becoming a production incident. Randomly replacing the test inputs removes comparability and makes score changes difficult to attribute. Disabling evaluation or relying on complaints converts a preventable release-control failure into reactive incident handling. Anthropic characterizes regression evaluations as tests of whether an agent still handles tasks it previously completed and recommends maintaining them continuously to detect backsliding. Demystifying evals for AI agents
NEW QUESTION # 72
An architect at Trenova Systems, Inc. is reviewing two draft communication packages for the same architectural decision. Package 1 contains a threat model, residual-risk register, and control-mapping table.
Package 2 contains a list of capabilities delivered and feature-level roadmap dependencies.
Which two audiences are correctly matched to these packages? (Select two.)
Answer: A,D
Explanation:
Package 1 is designed for security and compliance review because its threat model identifies relevant threats and attack paths, its residual-risk register documents the risks remaining after controls, and its control- mapping table connects safeguards to organizational or regulatory requirements. Package 2 is designed for the product manager because delivered capabilities and roadmap dependencies directly support prioritization, release planning, sequencing, and lifecycle ownership. An engineering implementation team would ordinarily need component diagrams, interface contracts, configuration schemas, deployment instructions, and operational runbooks rather than primarily risk-governance artifacts. An executive sponsor generally needs a concise outcome, cost, risk, and decision summary instead of feature-level dependency details. Effective architecture communication must therefore adapt the content and level of detail to each audience's responsibilities and decision rights.
NEW QUESTION # 73
You are identifying the highest-impact optimization for a deployment whose token cost is dominated by a long, repeated system prompt and a large retrieved context per request.
Which optimization most directly targets the dominant cost driver?
Answer: D
NEW QUESTION # 74
You are classifying chunking strategies by the corpus type each is best suited to.
For each chunking strategy, select the appropriate corpus type: "Long Structured Documents,"
"Heterogeneous Short Records," or "Code or Hierarchical Specifications."
Answer:
Explanation:
Explanation:
* Function-level or section-level chunking for code modules - Code or Hierarchical Specifications
* Tree-aware chunking that follows code or specification hierarchy - Code or Hierarchical Specifications
* Per-record chunking where each record is one chunk - Heterogeneous Short Records
* Semantic chunking along clause or paragraph boundaries - Long Structured Documents
* Fixed-size chunking with overlap for short records of similar length - Heterogeneous Short Records
* Hierarchical chunking that mirrors document section structure - Long Structured Documents Chunking must preserve the structural unit that carries meaning in the source corpus. Code and hierarchical specifications are best divided at function, module, class, or tree boundaries because arbitrary token cuts can separate definitions from their implementation or parent context. Heterogeneous short-record collections should generally preserve each record as an independent chunk. Fixed-size overlapping chunks are also effective when records have broadly similar lengths and lack meaningful internal hierarchy. Long structured documents benefit from semantic boundaries such as clauses and paragraphs, while hierarchical chunking preserves relationships among sections, subsections, and parent headings. Anthropic notes that chunk size, boundaries, and overlap materially affect retrieval performance; therefore, one universal chunking method is inappropriate. Anthropic Contextual Retrieval
NEW QUESTION # 75
You are sequencing decomposed components in an invoice-processing pipeline.
For each of the decomposed components, select the execution layer it belongs to: "Pre-Processing," "Model Stage," or "Post-Processing."
Answer:
Explanation:
Explanation:
Model Stage, Post-Processing, Post-Processing, Model Stage, Pre-Processing, Pre-Processing Pre-processing prepares source material before Claude receives it. Optical character recognition converts scanned invoice images into machine-readable text. Because PII redaction is explicitly performed before model invocation, it also belongs in pre-processing. Additional activities at this layer can include file validation, malware scanning, normalization, page separation, and metadata extraction.
The model stage contains tasks requiring Claude's language-understanding capability. Classifying the invoice type involves interpreting textual and contextual features, while extracting structured invoice fields requires mapping unstructured content into defined business attributes.
Post-processing verifies and commits the model's result. Schema validation confirms that required fields exist, data types are correct, enumerated values are permitted, and structural constraints are satisfied.
Persistence must occur only after validation and any required human review because the system of record should not receive malformed or unapproved model output.
This decomposition separates probabilistic inference from deterministic processing. OCR, redaction, validation, and persistence do not need to be delegated to Claude when conventional components can execute them more predictably. The resulting architecture reduces model workload, strengthens privacy controls, improves auditability, and prevents unvalidated output from directly changing authoritative records.
Study Guide references/topics: Task decomposition; pre-processing; model inference; post-processing; deterministic validation; privacy-by-design; system-of-record protection.
NEW QUESTION # 76
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