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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Workflow Integration and Solution Design | 16% | - Plan end-to-end Claude-augmented workflows - Design AI-assisted processes and solutions - Integrate Claude into existing business workflows |
| Topic 2: Governance, Risk, and Responsible Use | 15% | - Manage data sensitivity and privacy risks - Apply responsible AI principles and policies - Understand limitations and ethical boundaries |
| Topic 3: Troubleshooting and Optimization | 10% | - Diagnose context and performance issues - Identify causes of poor or unexpected outputs - Adjust prompts and configuration to improve results |
| Topic 4: Prompting and Task Execution | 14% | - Apply task decomposition to structure complex requests - Adapt prompting strategies by task type - Create effective prompts for business and technical tasks - Iterate prompts to improve output quality |
| Topic 5: Configuration and Knowledge Management | 12% | - Manage knowledge sources and context - Maintain consistent system-level behavior - Configure Claude Projects with instructions |
| Topic 6: Product and Model Selection | 12% | - Understand model differences and use cases - Match model capabilities to task requirements - Select appropriate Claude product features |
| Topic 7: Output Evaluation and Validation | 21% | - Detect hallucinations, biases, and inconsistencies - Evaluate outputs for accuracy, completeness, and consistency - Determine when human review is required - Apply fact-checking and validation methods |
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NEW QUESTION # 22
You are an operations lead and notice that a Project's outputs have been drifting from the team's current expectations.
Which maintenance action should be tried first?
Answer: A
Explanation:
Project output drift commonly occurs when persistent instructions, reference documents, terminology, policies, or expected formats no longer reflect the team's current operating standard. Option C addresses the most direct and controllable cause by reviewing the Project's instructions and knowledge sources before replacing the Project or changing models.
Anthropic explains that Project instructions define how Claude should behave and respond across chats in that Project. Documents uploaded to Project knowledge provide persistent context that Claude uses throughout those chats. If either component contains obsolete requirements, conflicting guidance, or superseded source material, Claude can consistently produce results that no longer meet the team's expectations. The appropriate maintenance response is to compare instructions and knowledge against current standards, remove obsolete files, resolve contradictions, clarify requirements, and test the revised configuration with representative requests.
Option A is unnecessarily destructive before the cause has been diagnosed. Option B changes the model without correcting stale or conflicting instructions and therefore may preserve the underlying problem. Option D institutionalizes degraded output rather than correcting it. Reviewing and updating the Project configuration is the lowest-risk and most evidence-based first action. Anthropic's Project management guidance
NEW QUESTION # 23
A customer-success associate is drafting a prompt that asks Claude to summarize a customer issue from the case file. The case file includes the customer's full government identifier, home address, name, account number, and the description of the issue. The summary the associate needs is about the issue.
Which approach is most appropriate?
Answer: B
Explanation:
Under the objective Apply data, privacy sensitivity, regulatory policies, and privacy considerations (Domain 6: Governance, Risk, and Responsible Use), data minimization is a primary privacy requirement.
When processing enterprise documents with AI tools, users must never submit highly sensitive Personally Identifiable Information (PII)-such as national government identifiers (SSNs/National Insurance numbers) or private home addresses-unless strictly required for the analytical task. Because the associate only needs a summary of the core customer problem, redacting or removing those non-essential high-sensitivity fields prior to input ingestion (Option C) enforces sound privacy hygiene. Relying on in-prompt instructions to "ignore" ingested PII (Option A) still exposes raw sensitive tokens to the model context. Swapping identifiers for other sensitive account data (Option B) fails data protection standards.
NEW QUESTION # 24
A finance consultant is adapting a Claude-generated narrative for a quarterly investor update. Investors have a fixed time window for the call and expect the headline result and forward outlook to be clear early.
Which adaptation is most appropriate for this audience?
Answer: C
Explanation:
Under the objective Edit, adapt, refine, and compare the output generated by Claude model(s) for the intended audience (Domain 2: Output Evaluation and Validation), adapting generated text requires restructuring content to match the executive decision-making style of the stakeholders.
Executive and investor audiences operate under strict time constraints and prioritize the bottom-line financial outcome before examining underlying mechanics (following the Minto Pyramid / Bottom-Line-Up-Front principle). Option C correctly structures the executive narrative: opening immediately with the headline financial figures, following with a concise breakdown of key operational drivers, and concluding with forward-looking guidance. Structuring the briefing chronologically (Option D) or burying core financial results in the middle of operational prose (Option A) creates cognitive friction and wastes time. Leading with the forward outlook before establishing current financial performance (Option B) lacks context.
NEW QUESTION # 25
You are setting up a Connector for a shared data source that several colleagues will use.
Which Connector setup step should be performed first?
Answer: C
Explanation:
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.
NEW QUESTION # 26
A Claude associate is evaluating a proposed use case in which Claude would issue final hiring decisions without any human review, applying the AI Fluency Framework Delegation competency.
Which classification best reflects Delegation principles?
Answer: D
Explanation:
Under the objective Understand ethical implications of AI usage and the AI Fluency Framework Delegation competency, determining autonomous task suitability depends on assessing human consequence, ethical responsibility, and legal accountability.
Hiring decisions directly impact individuals' careers, livelihoods, and civil rights, carrying substantial legal, moral, and organizational consequences. Delegation principles strictly classify autonomous employment, promotion, and termination determinations as non-delegable tasks that must remain under human ownership (Option A). Human judgment, context discernment, and legal accountability cannot be offloaded to an algorithmic model. Sample-based after-the-fact reviews (Option B) or delegating fully in pursuit of processing efficiency (Option C) violate responsible AI governance. Furthermore, classifying the prohibition as merely an issue of system technical reliability (Option D) ignores the fundamental imperative of ethical and human accountability.
NEW QUESTION # 27
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