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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Integration | 19% | - Claude integration mechanisms
- Enterprise system integration |
| Topic 2: Claude Models, Prompting & Context Engineering | 13% | - Context window optimization - Guardrails - Prompt reuse and context engineering strategies - Claude model selection and trade-offs - System prompts and prompt templates |
| Topic 3: Developer Productivity & Operational Enablement | 7% | - Debugging and operational issue resolution - Claude tooling configuration for teams - Developer enablement - AI-assisted developer workflows |
| Topic 4: Solution Design & Architecture | 17% | - Translating business problems into Claude-based AI solutions - Multi-agent systems and orchestration - End-to-end architecture design - Alignment with business value, cost, performance, and SLAs - Decomposition techniques for complex problem solving - Architectural patterns
|
| Topic 5: Governance, Safety & Risk Management | 14% | - Security and risk management - AI safety and guardrails - Regulatory and compliance requirements - Ethical AI considerations - Human-in-the-loop validation |
| Topic 6: Evaluation, Testing & Optimization | 16% | - Production monitoring and optimization - A/B testing - Evaluation framework design - Cost and performance optimization - Evaluation metrics and datasets - System issue diagnosis |
| Topic 7: Stakeholder Communication & Lifecycle Management | 14% | - Stakeholder management - Architecture documentation - Communicating architectural decisions - Discovery and requirements gathering - Service-level agreements - Solution lifecycle management |
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NEW QUESTION # 27
You are distinguishing functional from non-functional requirements during discovery.
Which item is a non-functional requirement?
Answer: C
Explanation:
Option D defines a measurable performance characteristic rather than a business capability. The requirement specifies a percentile-based latency threshold-under 800 milliseconds at the 95th percentile-and ties that threshold to the expected workload volume. It therefore describes how well the system must operate under defined conditions.
Options A, B, and C are functional requirements. They describe actions the solution must perform: extracting fields and creating a downstream record, producing an editable draft, and assigning tickets to categories.
Functional requirements define observable system behaviors or outputs.
A strong non-functional requirement must include a metric, threshold, measurement method, and operating condition. For Option D, the architect should further define whether latency is measured end-to-end or only at the model API, the test duration, concurrency, input-size distribution, cache state, timeout behavior, geographic region, and treatment of failed requests. Without those details, teams may report incompatible results while claiming to satisfy the same requirement.
Non-functional requirements for Claude solutions commonly address latency, availability, throughput, cost, privacy, security, recoverability, observability, scalability, and regulatory compliance.
Study Guide references/topics: Structured discovery; functional requirements; non-functional requirements; p95 latency; workload conditions; measurable acceptance criteria.
NEW QUESTION # 28
You are designing a test strategy for a Claude-based pipeline that handles sensitive financial data.
Which two test types should be prioritized to cover both safety under attack and cross-component correctness? (Select two.)
Answer: A,B
Explanation:
Adversarial tests directly evaluate behavior under attack, including prompt injection, malformed records, malicious document content, credential-extraction attempts, and unauthorized tool requests. Integration tests provide the complementary coverage required by the question: they verify authentication, retrieval, prompt construction, model invocation, validation, logging, and downstream actions as one end-to-end system.
Regression, smoke, and unit testing remain valuable, but they do not jointly satisfy the two stated objectives.
A stable regression set measures previously established behavior; smoke tests provide shallow availability confirmation; and prompt-rendering unit tests cover only one component. Anthropic recommends explicit prompt-injection defenses and task-specific evaluations that represent both normal and edge-case traffic.
Prompt-injection mitigation
NEW QUESTION # 29
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: A
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 # 30
An engineer inadvertently commits an API key to the repository by placing it in .claude/settings.json.
Which configuration design principle was violated?
Answer: C
Explanation:
A shared project settings file is intended to be committed so that team-level configuration can travel with the repository. Consequently, it must never contain API keys, access tokens, passwords, or comparable secrets.
Credentials should be injected at runtime through an approved secret-management mechanism, environment- specific credential provider, or delegated authentication flow. This separates distributable configuration from sensitive authentication material and supports rotation, revocation, auditing, and least privilege. Options A and B concern configuration precedence rather than secret handling. Option D is incorrect because MCP server definitions may legitimately be stored in project configuration; only their sensitive credential values must remain outside version control. Because the key was exposed, the operational response should also include immediate revocation or rotation and investigation of repository history. Claude Code: Settings files and precedence
NEW QUESTION # 31
You are reviewing a peer's end-to-end design for a Claude-based platform expected to scale to thousands of concurrent users.
For each statement, indicate Yes if it reflects sound architectural practice. Otherwise, select No.
Answer:
Explanation:
Explanation:
* Authentication and authorization run before retrieval so retrieval can filter by identity - Yes
* An asynchronous queue absorbs bursty traffic between intake and the model-invocation layer - Yes
* Tax computation is encoded directly in the system prompt rather than in code - No
* Conversation logs include unredacted government identifiers to maximize tuning signal - No Authentication and authorization must precede retrieval so unauthorized information never enters model context. An asynchronous queue is appropriate for burst absorption, backpressure, controlled concurrency, and retry management at scale. Deterministic tax computation should be implemented in validated code or a controlled calculation tool, not delegated to probabilistic prompt interpretation. Including unredacted government identifiers in conversation logs violates data-minimization principles and creates unnecessary privacy, security, and regulatory exposure. Sensitive fields should be removed, tokenized, or redacted before logging or model use unless specifically required and authorized. The resulting design separates deterministic computation, access control, scalable orchestration, and language reasoning into appropriate architectural layers.
NEW QUESTION # 32
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