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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Claude Models, Prompting & Context Engineering | 13% | - Prompt reuse and context engineering strategies - Context window optimization - System prompts and prompt templates - Guardrails - Claude model selection and trade-offs |
| Topic 2: Developer Productivity & Operational Enablement | 7% | - Debugging and operational issue resolution - Claude tooling configuration for teams - Developer enablement - AI-assisted developer workflows |
| Topic 3: Evaluation, Testing & Optimization | 16% | - System issue diagnosis - Cost and performance optimization - Evaluation framework design - A/B testing - Evaluation metrics and datasets - Production monitoring and optimization |
| Topic 4: Governance, Safety & Risk Management | 14% | - Ethical AI considerations - Regulatory and compliance requirements - AI safety and guardrails - Human-in-the-loop validation - Security and risk management |
| Topic 5: Solution Design & Architecture | 17% | - Multi-agent systems and orchestration - Translating business problems into Claude-based AI solutions - Architectural patterns
- End-to-end architecture design - Alignment with business value, cost, performance, and SLAs |
| Topic 6: Integration | 19% | - Authentication and authorization analysis - Enterprise system integration - RAG pipeline design
|
| Topic 7: Stakeholder Communication & Lifecycle Management | 14% | - Service-level agreements - Solution lifecycle management - Discovery and requirements gathering - Communicating architectural decisions - Stakeholder management - Architecture documentation |
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NEW QUESTION # 86
The platform team at Trenova Systems, Inc. needs to reduce per-query cost and p95 latency for a high-volume Claude pipeline without degrading output quality on the core use case.
Which two optimizations directly target both cost and latency simultaneously? (Select two.)
Answer: C,D
Explanation:
Prompt caching reduces repeated input processing when a stable system prefix is reused, directly lowering input cost and processing latency. Model routing provides the second optimization: routine cases can use a faster, lower-cost model, while difficult cases retain access to the more capable model. The routing policy must be validated against representative evaluations to confirm that quality remains within the required threshold. Increasing max_tokens can increase output cost and generation time. Retrieving the complete corpus and adding examples to every request both enlarge the input, worsening latency and token consumption. Anthropic identifies prompt caching as a cost-and-latency optimization and recommends selecting a model according to the workload's quality, speed, and economic requirements. Prompt caching ; reducing latency
NEW QUESTION # 87
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 # 88
You are defining escalation criteria for ambiguous cases in an automated workflow.
Which set of criteria best supports consistent escalation?
Answer: B
Explanation:
Option D defines observable and repeatable escalation triggers. Confidence thresholds identify cases where the system lacks sufficient certainty. High-impact categories ensure that legally, financially, medically, or operationally consequential decisions receive human attention regardless of nominal confidence. Classifier- generated ambiguity flags add an independent control, while an explicit user request preserves the individual' s ability to obtain review. These triggers should be documented, tested, logged, and mapped to reviewer authority and response-time objectives. Options A and C make escalation discretionary and inconsistent.
Option B is purely reactive because the potentially harmful automated action has already occurred. A sound workflow escalates before consequential execution and records the reason, evidence, reviewer, decision, and outcome.
Study Guide references/topics: Human-in-the-loop governance; escalation thresholds; ambiguity detection; consequential decisions; review auditability.
NEW QUESTION # 89
A Claude architect observes that after a recent model-version upgrade, grounded responses began including claims not supported by the retrieved source documents.
Which mitigation is most directly targeted at this failure mode?
Answer: D
Explanation:
The observed failure is a groundedness defect: the response contains claims that cannot be traced to retrieved evidence. Option A directly constrains generation to the supplied sources, requires visible attribution, and verifies that each material claim has supporting text. Unsupported claims should be removed, qualified, or replaced with an explicit statement that the available sources are insufficient. Regression evaluation in Option B may detect the failure but does not itself prevent unsupported claims. OAuth addresses identity and authorization, while input classifiers address malicious or unsafe retrieved content; neither verifies factual support. Anthropic recommends citations, supporting quotations, and post-generation claim verification to reduce hallucinations in document-grounded applications. Reducing hallucinations
NEW QUESTION # 90
After a prompt-template update, several previously passing test cases now produce unexpected outputs.
Which test type is specifically designed to detect this category of failure?
Answer: A
Explanation:
Regression tests determine whether behavior that previously met requirements still passes after a change. A stable reference set provides identical inputs, expected outcomes, and scoring rules across prompt versions, allowing the team to attribute failures to the update. Integration tests verify interactions among components but may not measure detailed output behavior. Adversarial tests target attacks and misuse cases, while smoke tests establish only that essential system paths remain available. The failed cases should be added to or retained within the regression suite, and the old and new prompt should be compared under the same model, parameters, tools, and retrieval context. Anthropic describes regression evaluations as protection against behavioral backsliding. Demystifying evals for AI agents
NEW QUESTION # 91
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