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| Section | Weight | Objectives |
|---|---|---|
| Troubleshooting and Optimization | 10% | - Identify causes of poor or unexpected outputs - Adjust prompts and configuration to improve results - Diagnose context and performance issues |
| Governance, Risk, and Responsible Use | 15% | - Manage data sensitivity and privacy risks - Understand limitations and ethical boundaries - Apply responsible AI principles and policies |
| Product and Model Selection | 12% | - Match model capabilities to task requirements - Understand model differences and use cases - Select appropriate Claude product features |
| Workflow Integration and Solution Design | 16% | - Design AI-assisted processes and solutions - Plan end-to-end Claude-augmented workflows - Integrate Claude into existing business workflows |
| Output Evaluation and Validation | 21% | - Detect hallucinations, biases, and inconsistencies - Apply fact-checking and validation methods - Determine when human review is required - Evaluate outputs for accuracy, completeness, and consistency |
| Prompting and Task Execution | 14% | - Create effective prompts for business and technical tasks - Iterate prompts to improve output quality - Adapt prompting strategies by task type - Apply task decomposition to structure complex requests |
| Configuration and Knowledge Management | 12% | - Manage knowledge sources and context - Maintain consistent system-level behavior - Configure Claude Projects with instructions |
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17. Frage
You are a business analyst reviewing prompt drafts written by teammates.
Which two elements belong in a well-structured prompt for a business task? (Select two.) Each correct answer presents part of the solution.
Antwort: A,B
Begründung:
Options A and B are correct because Claude needs a clear task and the context required to perform that task appropriately. The prompt should state the requested action using direct language-for example, summarize, compare, classify, draft, calculate, or recommend. It should then provide relevant information such as the intended audience, business objective, authoritative sources, constraints, success criteria, and required output format.
Anthropic's prompting guidance emphasizes clarity, specificity, contextual grounding, and explicit formatting instructions. Audience and purpose are particularly important in business tasks because the same underlying information may need very different treatment for executives, customers, technical teams, or regulators.
Option C can be useful when an approachable tone is genuinely required, but it is not an essential element of every structured business prompt. Tone should be selected according to the task rather than added automatically. Option D introduces excessive and potentially irrelevant information; only products or services relevant to the task should be included. Option E similarly adds noise unless the anecdote supplies necessary factual context or illustrates the desired output.
Therefore, a clear task statement and relevant goal-and-audience context form the indispensable foundation.
See Anthropic's prompting best practices .
18. Frage
You are evaluating a Claude output that appears to contain hallucinated content.
Which response step should be performed first when a hallucination is suspected?
Antwort: A
Begründung:
Under the objective Identify hallucinations, inconsistencies, and biases in responses , diagnosing potential generative errors requires structured evidence validation before taking corrective action.
The first diagnostic step is claim isolation: carefully examining the output to pinpoint the exact statement, metric, or entity that appears unsupported by the provided source context or verified reality (Option D).
Pinpointing the specific defect allows the reviewer to compare it directly against the source data to confirm whether it is an actual hallucination, an ungrounded inference, or a valid synthesis. Discarding the entire output without identifying the error (Option A) prevents root-cause analysis and wastes working iterations.
Logging risks (Option B) or broadcasting notifications to users (Option C) are secondary actions that occur only after the defect has been isolated and confirmed.
19. Frage
A customer-success team member is reviewing a Claude-generated customer health-check report against the agreed deliverable list. The report covers all five required topics, but the renewal-risk section names only one risk factor where the deliverable specification calls for renewal risk to be assessed across pricing, product fit, and stakeholder change.
Which conclusion is best supported?
Antwort: B
Begründung:
Under the objective Evaluate Claude-generated outputs for accuracy and completeness , quality assurance involves auditing both macro-level structure (presence of required topics) and micro-level depth (thoroughness against detailed specifications).
The report successfully includes all five mandatory high-level headings, satisfying basic structural completeness. However, evaluating the internal substance of the renewal-risk section reveals an under-scoped deliverable: the specification required analyzing three distinct dimensions (pricing, product fit, and stakeholder change), but Claude addressed only one. Therefore, Option D is the accurate assessment: the artifact fulfills topical scope but lacks required operational depth and must be expanded prior to client delivery. Claiming it completely satisfies requirements (Option C) ignores explicit criteria, while labeling the entire section missing (Option B) or offloading internal QA to the customer (Option A) violates quality management standards.
20. Frage
You have iterated a Claude prompt eight times, and the output quality has plateaued for the last four attempts.
Which conclusion is most appropriate?
Antwort: D
21. Frage
You are a project manager working through a long planning conversation with Claude and must keep the conversation productive as it grows.
Which two practices keep a long planning conversation productive? (Select two.) Each correct answer presents a complete solution.
Antwort: B,C
Begründung:
Under the objective Understand, manage context limitations and memory considerations and when to restart, summarize, or persist , managing conversational state across long planning sessions is essential for maintaining output quality and coherence. Large Language Models operate within finite context windows where attention can degrade as extraneous tokens accumulate.
Option B is a best practice because shifting subtopics often dilutes earlier instructions; explicitly restating key constraints and strategic goals ensures the model anchors its attention on active parameters without being distracted by obsolete conversational turns. Option C represents context compression-a core technique where key milestones, agreed parameters, and decisions are consolidated into a dense summary. This reduces token overhead, frees up working memory, and prevents hallucination or drift. Conversely, opening a new thread for every minor question (Option A) fractures contextual continuity, switching topics arbitrarily (Option D) pollutes attention heads, and avoiding summarization (Option E) accelerates context bloat and degrades performance.
22. Frage
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