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| Section | Weight | Objectives |
|---|---|---|
| Solution Design & Architecture | 17% | - Alignment with business value, cost, performance, and SLAs - End-to-end architecture design - Decomposition techniques for complex problem solving - Translating business problems into Claude-based AI solutions - Multi-agent systems and orchestration - Architectural patterns
|
| Stakeholder Communication & Lifecycle Management | 14% | - Communicating architectural decisions - Stakeholder management - Solution lifecycle management - Discovery and requirements gathering - Service-level agreements - Architecture documentation |
| Integration | 19% | - Claude integration mechanisms
- Authentication and authorization analysis - RAG pipeline design
|
| Claude Models, Prompting & Context Engineering | 13% | - Prompt reuse and context engineering strategies - Claude model selection and trade-offs - Guardrails - Context window optimization - System prompts and prompt templates |
| Governance, Safety & Risk Management | 14% | - Human-in-the-loop validation - Regulatory and compliance requirements - Ethical AI considerations - AI safety and guardrails - Security and risk management |
| Developer Productivity & Operational Enablement | 7% | - Debugging and operational issue resolution - AI-assisted developer workflows - Developer enablement - Claude tooling configuration for teams |
| Evaluation, Testing & Optimization | 16% | - System issue diagnosis - Evaluation framework design - Cost and performance optimization - A/B testing - Evaluation metrics and datasets - Production monitoring and optimization |
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NEW QUESTION # 39
You are evaluating prompting claims in a peer's design document.
For each claim, select yes if the claim reflects sound practice. Otherwise, select no.
Answer:
Explanation:
Explanation:
Yes, Yes, No, Yes, No
Zero-shot prompting is a reasonable baseline for a clearly defined closed-set classifier because the model receives both the permissible labels and their meanings. Performance should then be measured against an evaluation set before additional prompt complexity is introduced. Few-shot examples are also sound for extraction because examples containing the exact field names demonstrate the required schema and reduce formatting ambiguity. Anthropic identifies examples as one of the most reliable methods for steering output format, structure, and consistency. Prompting Best Practices Chain-of-thought is not an unconditional default. Additional reasoning is appropriate when the task requires substantive decomposition or complex judgment, but routine classification and extraction may gain little while consuming more tokens and increasing latency. The technique should be selected according to evaluated task requirements.
An even-handed comparison governed by explicit criteria mitigates leading-question bias by requiring symmetrical treatment of alternatives rather than presupposing a preferred conclusion. Finally, prompt validation is model-specific. New model versions may follow instructions differently, change formatting behavior, alter tool selection, or produce different latency and cost characteristics. Anthropic's migration guidance consequently recommends reevaluating prompts and workloads against the new baseline. Model Migration Guide Study Guide references/topics: Zero-shot prompting; few-shot examples; reasoning techniques; neutral framing; model migration and regression evaluation.
NEW QUESTION # 40
A document analysis service processes legal filings averaging 80,000 tokens each. Each filing is queried by attorneys an average of 14 times during a case. The current architecture sends the full filing on every query.
The CFO has asked you to reduce per-query costs while preserving response quality. The security officer requires that filing contents not be stored outside Fabrikam's tenancy.
Which optimization approach should you recommend?
Answer: D
Explanation:
The 80,000-token filing is a large, stable prefix reused across fourteen requests, making prompt caching the strongest optimization. The first request creates the cache; subsequent identical-prefix requests use lower- priced cache reads while retaining the complete filing, preserving the evidence available to Claude. Anthropic documents cache reads at a fraction of standard input cost and confirms that caches are isolated by organization and, on supported platforms, by workspace. Cached representations remain in memory rather than persistent storage. Options A and C may omit context through summarization or retrieval and therefore require additional quality evaluation. Option D lowers unit cost but may reduce reasoning quality. The design must preserve prefix identity, choose an appropriate TTL, and verify the tenancy configuration.
Study Guide references/topics: Prompt caching ; cache isolation; stable prefixes; long-document analysis; cost optimization.
NEW QUESTION # 41
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: A
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 # 42
You are evaluating an evaluation set used to score a Claude-based hiring-support tool. The set is drawn from one geographic region and one tenure band.
Which response is most appropriate?
Answer: D
Explanation:
An evaluation set must represent the populations, operating conditions, and use cases that the deployed system will encounter. A dataset drawn from only one geographic region and one tenure band cannot reliably demonstrate that the hiring-support tool performs consistently across its intended users. Expanding the evaluation set enables architects to identify regional performance differences, tenure-related errors, unsupported generalizations, and potentially discriminatory outcomes before broader deployment. The system should then be rescored using the expanded dataset, with results examined across relevant population slices rather than relying only on an aggregate score. Options A and B preserve or worsen the representation gap.
Option D is also inappropriate because qualitative feedback can supplement structured evaluation but cannot replace measurable, repeatable testing of accuracy, fairness, and reliability across the intended deployment population.
NEW QUESTION # 43
A Claude architect needs to ensure that a security-hardening flag cannot be disabled by any individual engineer after it is set.
Which configuration scope correctly enforces this requirement?
Answer: B
Explanation:
Managed configuration is designed for organization-wide security and compliance controls that individual users and repositories must not override. Applying the hardening flag centrally establishes an administrative policy boundary and prevents engineers from disabling the control through user, project, or local settings.
Anthropic identifies managed scope as the appropriate location for security policies, non-overridable compliance requirements, and standardized configurations deployed by IT or DevOps. Managed values ordinarily take precedence over command-line arguments and every user-controlled settings scope. Claude Code Settings Option A depends on every engineer maintaining the setting and allows the user to edit or delete it. Option B affects only pipeline execution and does not protect local, interactive, IDE, or other execution paths. Option D standardizes the setting within the repository but does not make it tamper-resistant; contributors with repository write access could alter the file, use higher-precedence local settings where permitted, or operate outside the repository configuration.
The implementation should also verify active policy delivery, monitor configuration-change events, and test that startup or execution fails safely if the managed setting is absent or invalid. Central definition without enforcement and verification would not fully satisfy the requirement.
Study Guide references/topics: Managed settings; non-overridable controls; enterprise hardening; policy enforcement; configuration governance; defense against local override.
NEW QUESTION # 44
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