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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Configuration and Knowledge Management | 12% | - Configure Claude Projects with instructions - Maintain consistent system-level behavior - Manage knowledge sources and context |
| Topic 2: Troubleshooting and Optimization | 10% | - Adjust prompts and configuration to improve results - Identify causes of poor or unexpected outputs - Diagnose context and performance issues |
| Topic 3: Governance, Risk, and Responsible Use | 15% | - Manage data sensitivity and privacy risks - Understand limitations and ethical boundaries - Apply responsible AI principles and policies |
| Topic 4: Product and Model Selection | 12% | - Match model capabilities to task requirements - Understand model differences and use cases - Select appropriate Claude product features |
| Topic 5: Output Evaluation and Validation | 21% | - Evaluate outputs for accuracy, completeness, and consistency - Determine when human review is required - Apply fact-checking and validation methods - Detect hallucinations, biases, and inconsistencies |
| Topic 6: Workflow Integration and Solution Design | 16% | - Plan end-to-end Claude-augmented workflows - Integrate Claude into existing business workflows - Design AI-assisted processes and solutions |
| Topic 7: Prompting and Task Execution | 14% | - Create effective prompts for business and technical tasks - Iterate prompts to improve output quality - Apply task decomposition to structure complex requests - Adapt prompting strategies by task type |
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148. 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: C
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
149. Frage
You are setting up a Connector for a shared data source that several colleagues will use.
Which Connector setup step should be performed first?
Antwort: C
Begründung:
Option D is the necessary first step because authorization and data suitability must be established before the shared source is connected to Claude. The team should verify that the connector and its provider are approved under organizational AI, security, procurement, and privacy policies. It must also determine the source's data classification and whether information in that source may legally and contractually be processed through the connector.
Connecting the source first, as proposed in Option C, could expose restricted information before governance review has occurred. Testing a representative task under Option B likewise assumes that access has already been approved. Documenting the connector's purpose and suitable use cases under Option A is important, but that operational documentation should follow the initial determination that the connector and relevant data are permitted.
After approval, the team can connect the source using an account with appropriate permissions, verify authentication, test access boundaries, and confirm that Claude retrieves only information available to the authenticated user. The team should then document approved use cases, prohibited data, human-review requirements, and reporting procedures. This sequence follows least-privilege and data-governance principles and avoids treating successful authentication as evidence that a connector is organizationally authorized or suitable for every type of information.
150. Frage
A senior sales manager is presenting Claude's role in a project to a non-technical executive sponsor. The sponsor will be asked to approve continued investment based on the presentation.
Which framing best balances value and limitations?
Antwort: B
Begründung:
Answer A is correct. An executive sponsor needs a decision-oriented explanation of Claude's business contribution, not a technical description of how the model operates. The presentation should explain the tasks Claude performs effectively, connect those capabilities to measurable project outcomes, and state the relevant limitations together with the controls used to manage them. For example, the manager could explain that Claude accelerates first-draft creation and document analysis while human reviewers remain responsible for validating important facts, approving consequential outputs, and handling exceptional cases.
Option B acknowledges limitations but gives them disproportionate emphasis and frames the project defensively. Option C relies on technical model terminology and requires the sponsor to translate capabilities into business value independently. This fails to support an informed investment decision. Option D presents the benefits but withholds material limitations until later, creating an incomplete and potentially misleading business case.
A balanced presentation neither exaggerates Claude's reliability nor treats limitations as a reason to ignore its value. It establishes what Claude contributes, where human judgment remains necessary, and how the combined workflow supports the sponsor's desired outcomes. This reflects Anthropic's AI Fluency principles of appropriate delegation, critical evaluation, and responsible human accountability. Anthropic AI Fluency
151. 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.
152. Frage
You are an operations lead sorting proposed Claude use cases according to whether they are acceptable for your team to pursue.
Which two proposed use cases are acceptable for the team to pursue with Claude? (Select two.) Each correct answer presents a complete solution.
Antwort: A,C
Begründung:
Answers B and C are the two acceptable use cases as written. Drafting an internal training summary from approved documentation is a bounded synthesis task grounded in authorized sources. Synthesizing themes from approved customer feedback under established aggregation rules is likewise a controlled analysis task, provided the data handling follows organizational privacy requirements. Both activities support human work without assigning Claude a consequential decision about an individual. Option D feeds performance-review information into a promotion process, an employment-related high-risk domain requiring formal controls and accountable human judgment. Options A and E involve healthcare and legal work. Anthropic's current Usage Policy does not categorically ban those domains, but it requires qualified professional review for high-risk advice or decisions and requires disclosure when outputs are presented directly to consumers. The brief wording in A and E mentions professional review but does not establish the complete safeguards, authorization, privacy handling, or disclosure needed to treat the workflow as approved. Therefore, they are not unconditionally acceptable "as stated." Anthropic's AI Fluency Delegation principle also requires considering consequences, reversibility, and the human capabilities that must remain in the process. B and C are the only choices that fully specify low-risk, policy-bounded knowledge work without relying on unstated safeguards. Anthropic Usage Policy
153. Frage
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