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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Governance, Safety & Risk Management | 14% | - Ethical AI considerations - Human-in-the-loop validation - Security and risk management - Regulatory and compliance requirements - AI safety and guardrails |
| Topic 2: Developer Productivity & Operational Enablement | 7% | - Developer enablement - Debugging and operational issue resolution - Claude tooling configuration for teams - AI-assisted developer workflows |
| Topic 3: Claude Models, Prompting & Context Engineering | 13% | - Guardrails - Claude model selection and trade-offs - System prompts and prompt templates - Prompt reuse and context engineering strategies - Context window optimization |
| Topic 4: Evaluation, Testing & Optimization | 16% | - A/B testing - System issue diagnosis - Evaluation framework design - Cost and performance optimization - Production monitoring and optimization - Evaluation metrics and datasets |
| Topic 5: Integration | 19% | - Claude integration mechanisms
- RAG pipeline design
|
| Topic 6: Stakeholder Communication & Lifecycle Management | 14% | - Service-level agreements - Communicating architectural decisions - Discovery and requirements gathering - Architecture documentation - Stakeholder management - Solution lifecycle management |
| Topic 7: Solution Design & Architecture | 17% | - Translating business problems into Claude-based AI solutions - Alignment with business value, cost, performance, and SLAs - End-to-end architecture design - Multi-agent systems and orchestration - Decomposition techniques for complex problem solving - Architectural patterns
|
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NEW QUESTION # 108
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 # 109
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 # 110
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 testing determines whether a change has broken behavior that previously satisfied the defined acceptance criteria. A stable reference set preserves known-good cases and enables the updated prompt to be compared with the prior baseline using consistent scoring rules. This makes Option B directly aligned with the described failure. Adversarial testing focuses on malicious or boundary inputs, such as prompt injection.
Smoke testing confirms that the system remains broadly available, but it does not measure whether previously correct model behavior has degraded. Integration testing verifies contracts and interactions among components and may not detect output-quality regressions caused specifically by a prompt change. Anthropic recommends task-specific evaluations that mirror real workloads, include edge cases, and measure performance against explicit success criteria throughout prompt iteration. Anthropic: Define success criteria and build evaluations
NEW QUESTION # 111
You are auditing a procurement-assistant agent whose defined responsibility is to draft purchase requests for review.
For each tool currently configured on the agent, select yes if the tool should remain after a least-privilege audit. Otherwise, select no if it should be removed.
Answer:
Explanation:
Explanation:
Yes, No, Yes, No, Yes
Least privilege requires each retained tool to be necessary for the agent's assigned responsibility and restricted to the minimum action level needed. Catalog lookup is appropriate because vendor and SKU information is needed to prepare an accurate request. The policy-document tool is similarly justified because the agent must ground the draft in applicable procurement rules. The draft-write tool is also aligned because it writes only to a review queue, preserving the required human approval boundary.
The inbox-send tool exceeds the stated responsibility. Sending a request directly to a vendor converts a drafting assistant into an externally acting purchasing agent and bypasses the review stage. The production- database administrator tool is unrelated to procurement and introduces unnecessary destructive capability, privilege-escalation exposure, and potential data-access risk.
Anthropic's prompt-injection guidance recommends limiting access to sensitive data and actions and applying least privilege so that a compromised agent can cause minimal damage. Claude Code also supports tool- specific allow, ask, and deny rules that are enforced outside the model. Prompt-Injection Mitigation, Claude Code Permissions Study Guide references/topics: Capability-bloat auditing; least privilege; tool scoping; human review gates; separation of drafting and execution authority.
NEW QUESTION # 112
You are presenting an architectural decision to a mixed audience that includes an executive sponsor and the engineering leads who will implement the decision.
Which presentation strategy best serves both audiences?
Answer: D
Explanation:
Option B uses progressive disclosure to meet the decision needs of both audiences. The executive sponsor first receives the selected direction, intended business outcomes, major constraints, cost or performance implications, and accepted trade-offs. This establishes why the decision matters and whether it remains aligned with strategic objectives.
The presentation can then move into the technical rationale required by engineering leads: component responsibilities, integration contracts, model and retrieval choices, security controls, rejected alternatives, implementation dependencies, evaluation gates, operational consequences, and migration requirements. This structure gives engineers enough evidence to implement the decision without forcing the executive audience to interpret unnecessary low-level detail.
Option A excludes the sponsor's concerns and fails to connect technical design to business value. Option C removes the context required for governance and implementation, making the decision appear arbitrary.
Option D assumes that all participants require identical information density, which usually produces a presentation that is too technical for executives and insufficiently precise for engineers.
The strongest communication pattern is layered but internally consistent: one decision, one rationale, and multiple levels of detail tailored to the responsibilities of the audience.
Study Guide references/topics: Mixed-audience communication; progressive disclosure; business outcomes; architectural trade-offs; technical rationale; implementation implications.
NEW QUESTION # 113
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