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| Section | Weight | Objectives |
|---|---|---|
| Configuration and Knowledge Management | 12% | - Manage uploaded knowledge and connectors - Maintain and update configurations - Configure Claude Projects with instructions and knowledge sources - Create effective system-level instructions |
| Product and Model Selection | 12% | - Align model selection with task requirements - Manage context limitations and memory - Differentiate between Claude model types - Select appropriate Claude product features |
| Prompting and Task Execution | 14% | - Iterate prompts to improve output quality - Adapt prompting strategy by task type - Apply task decomposition techniques to structure complex requests - Create effective prompts for business and technical tasks |
| Workflow Integration and Solution Design | 16% | - Analyze requirements and use cases - Integrate Claude into existing workflows and communicate its value and limitations - Use Claude for research, planning, and process optimization - Support solution design through iteration |
| Troubleshooting and Optimization | 10% | - Optimize workflows for efficiency and effectiveness - Identify and diagnose issues with underperforming prompts or poor outputs - Adjust approaches based on feedback and results |
| Output Evaluation and Validation | 21% | - Apply fact-checking and validation techniques - Edit and adapt outputs for the intended audience - Identify hallucinations, inconsistencies, and biases in responses - Evaluate Claude-generated outputs for accuracy and completeness - Organize and curate information and select appropriate output formats - Determine when human review or additional verification is required |
| Governance, Risk, and Responsible Use | 15% | - Follow organizational AI policies and governance standards - Understand ethical implications of Claude use - Apply data sensitivity, regulatory, and privacy considerations - Identify appropriate and inappropriate use cases |
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139. 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.
140. Frage
A Claude associate is identifying the bottleneck in a content workflow that takes too long to publish. The workflow has five steps: research, drafting, internal review, legal review, and publication. There is cycle-time data for each step from the past month.
Which signal most directly identifies the bottleneck?
Antwort: B
Begründung:
Option A is correct because a persistent queue or growing backlog directly indicates that work is arriving faster than a step can process it. That step is constraining the throughput of the overall workflow: earlier work accumulates while later stages wait for completed items. Queue length should still be examined over a representative period to distinguish a sustained bottleneck from a temporary spike.
Option C is useful supporting evidence but is not always the most direct signal. A step may have a long per- item cycle time because the work is inherently complex while still possessing sufficient capacity to handle incoming demand. Conversely, a relatively short step can become the bottleneck when its available capacity is too low for the volume arriving. Employee-reported workload in Option B is subjective and may reflect effort, stress, or simultaneous responsibilities rather than workflow throughput. Option D relies on an unsupported assumption: a recently added step is not necessarily the constraint.
A sound Claude-assisted optimization process should map the workflow, collect measurable evidence, identify the constraint, test a targeted improvement, and compare results against a baseline. Anthropic recommends measurable, task-relevant success criteria and evaluation methods rather than vague impressions.
Therefore, the largest sustained queue or backlog is the strongest direct indicator. See Anthropic's guidance on defining measurable success criteria and evaluations .
141. Frage
A workflow analyst is designing a hand-off between Claude and a human reviewer in an existing process. The reviewer needs to know what to check and what is in scope for their review.
Which hand-off design is most appropriate?
Antwort: C
Begründung:
Answer B is correct. A reliable Claude-to-human hand-off requires explicit, documented responsibilities for both stages of the workflow. The workflow should define the content and quality standards Claude's output must satisfy and separately identify what the human reviewer must verify before approval. Review criteria might include factual accuracy, source support, completeness, policy compliance, tone, confidentiality, and authorization to publish or act.
Option A produces inconsistent oversight because individual reviewers may apply different standards or overlook critical controls. Domain judgment remains important, but it should supplement documented minimum requirements rather than replace them. Option C defines Claude's responsibilities but leaves the human review scope ambiguous, creating accountability gaps. Option D introduces another weakness: Claude should not independently determine the controls used to validate its own output. A stable, human-approved checklist may be appropriate, but dynamically generated criteria could omit the exact risks requiring independent review.
Explicit acceptance and review criteria make the workflow repeatable, auditable, and easier to improve.
Anthropic's evaluation guidance emphasizes defining clear success criteria and using task-specific evaluation methods. Applying that principle to a human hand-off means documenting both the expected AI deliverable and the reviewer's approval responsibilities. Anthropic: Define success criteria and build evaluations
142. Frage
You are a knowledge worker preparing inputs for a Claude prompt.
Which data type most clearly requires extra handling such as redaction or anonymization before being included in the prompt?
Antwort: A
Begründung:
Under the objective Apply data, privacy sensitivity, regulatory policies, and privacy considerations , enterprise data protection policies require strict tiering of data assets based on sensitivity and regulatory mandates (such as GDPR, PCI-DSS, and HIPAA).
Customer government identifiers (e.g., SSN, National Insurance number) combined with full payment card numbers and individual names (Option B) represent highly sensitive Personally Identifiable Information (PII) and protected financial data. Ingesting this data into AI prompts without masking, tokenization, or redaction introduces severe compliance violations and data privacy risks. In contrast, published corporate press releases (Option A), publicly accessible executive job titles (Option C), and public marketing product descriptions (Option D) are public information assets that require no anonymization or redaction before prompt inclusion.
143. Frage
Preparing an AI-assisted work product for external delivery, a senior manager is applying the AI Fluency Framework Diligence competency.
Which step best reflects Diligence principles?
Antwort: A
Begründung:
Option D correctly applies Diligence because it preserves human accountability throughout the use of Claude.
AI assistance does not transfer responsibility for the accuracy, legality, suitability, or consequences of a deliverable to the model. The responsible professional must identify claims, calculations, citations, recommendations, and other elements requiring independent validation and must correct deficiencies before the material is distributed externally.
Anthropic defines Diligence as taking responsibility for what people do with AI and how they do it. This includes maintaining appropriate oversight, recognizing the limitations of generated content, and ensuring that important outputs receive review proportionate to their impact. Anthropic's hallucination-reduction guidance similarly states that critical information must be validated because techniques that reduce hallucinations cannot eliminate them entirely.
Options A and B explicitly attempt to transfer accountability to Claude, which is incompatible with Diligence.
Option C incorrectly treats fluent presentation as proof of factual accuracy. Language models can produce convincing but unsupported statements, so apparent confidence cannot replace verification. The manager must retain ownership of the final work product and independently validate the elements that could affect external stakeholders. Anthropic's AI Fluency definitions and hallucination guidance
144. Frage
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