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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Workflow Integration and Solution Design | 16% | - Use Claude for research, planning, and process optimization - Support solution design through iteration - Integrate Claude into existing workflows and communicate its value and limitations - Analyze requirements and use cases |
| Topic 2: Governance, Risk, and Responsible Use | 15% | - Identify appropriate and inappropriate use cases - Follow organizational AI policies and governance standards - Apply data sensitivity, regulatory, and privacy considerations - Understand ethical implications of Claude use |
| Topic 3: Configuration and Knowledge Management | 12% | - Configure Claude Projects with instructions and knowledge sources - Maintain and update configurations - Create effective system-level instructions - Manage uploaded knowledge and connectors |
| Topic 4: Troubleshooting and Optimization | 10% | - Optimize workflows for efficiency and effectiveness - Adjust approaches based on feedback and results - Identify and diagnose issues with underperforming prompts or poor outputs |
| Topic 5: Product and Model Selection | 12% | - Differentiate between Claude model types - Align model selection with task requirements - Manage context limitations and memory - Select appropriate Claude product features |
| Topic 6: Prompting and Task Execution | 14% | - Iterate prompts to improve output quality - Apply task decomposition techniques to structure complex requests - Adapt prompting strategy by task type - Create effective prompts for business and technical tasks |
| Topic 7: Output Evaluation and Validation | 21% | - Apply fact-checking and validation techniques - Determine when human review or additional verification is required - Organize and curate information and select appropriate output formats - Edit and adapt outputs for the intended audience - Evaluate Claude-generated outputs for accuracy and completeness - Identify hallucinations, inconsistencies, and biases in responses |
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NEW QUESTION # 89
Before uploading a customer file for a Code Execution analysis, a customer-success associate at Noventra, Ltd. is reviewing the planned task.
Which two practices best align with sound data handling? (Select two.)
Answer: A,B
Explanation:
Options B and E apply organizational governance and data-minimization requirements before the file enters Claude's execution environment. The associate must first establish whether Code Execution is an approved processing method for the file's classification. If the information exceeds the approved sensitivity level, the user must escalate to privacy, security, legal, or another designated authority rather than proceeding independently.
The file must also be inspected for customer identifiers, confidential fields, regulated information, credentials, or data unrelated to the calculation. Unnecessary fields should be removed, redacted, or anonymized. A sandbox limits interaction with the surrounding system, but it does not change the sensitivity of uploaded data, provide authorization, or override the organization's handling rules. Therefore, Options C and D incorrectly treat technical isolation as a governance exemption. Option A allows urgency to displace mandatory review.
Anthropic explains that Code Execution runs in an isolated container, while also warning that file-processing configurations and network access create security considerations. Its privacy guidance recommends careful treatment of highly sensitive personal and confidential information. The correct control is to minimize and authorize the data before uploading it, not rely solely on the sandbox afterward. Anthropic's Code Execution security guidance
NEW QUESTION # 90
Before sharing a Claude-generated brief, an analyst is applying the AI Fluency Framework Discernment competency to evaluate it.
Which review best reflects Discernment principles?
Answer: B
Explanation:
Under the objective Evaluate Claude-generated outputs for accuracy and completeness and the AI Fluency Framework Discernment competency, rigorous output evaluation requires systematic verification rather than surface appraisal.
Discernment requires actively auditing AI outputs across three core dimensions: completeness against original prompt requirements, factual fidelity against underlying source documentation, and compliance with domain- specific professional standards (Option D). Relying on surface fluency, elegant style, or subjective plausibility (Option A) creates a false sense of security, as LLMs frequently articulate hallucinations with high confidence. Treating logical flow or tone as proxies for factual accuracy (Option B) skips necessary verification gates. Similarly, spot-checking only the opening and closing paragraphs (Option C) overlooks hallucinations, omissions, or logical flaws buried within the main body.
NEW QUESTION # 91
An account manager has used Claude to extract requirements from stakeholder notes. Three requirements appear to conflict: two stakeholders specified mutually exclusive workflow steps, and a third requirement assumes an outcome that the first two would prevent.
Which step is most appropriate?
Answer: D
Explanation:
Option C is correct because Claude can identify and organize conflicting requirements, but it does not possess the authority or stakeholder context required to resolve substantive business disagreements independently.
Each conflicting requirement should be retained, labelled with its originating source, and marked with an unresolved status. The relevant stakeholders can then clarify priorities, negotiate trade-offs, or provide an authoritative decision.
Option A delays a known dependency and risks allowing downstream design work to proceed on incompatible assumptions. Option B permits Claude to invent compromise language that may not represent any stakeholder's actual position. Claude may help draft possible resolution questions, but those proposals must not be treated as approved requirements. Option D is equally inappropriate because apparent business justification in meeting notes does not grant the account manager or Claude authority to discard competing requirements.
Anthropic's hallucination guidance recommends grounding claims in supplied evidence and making outputs auditable with citations. Its AI Fluency Discernment principles also require humans to evaluate outputs critically and retain final judgment. Source traceability and stakeholder escalation therefore provide the most reliable resolution path. See Anthropic's guidance on reducing hallucinations and the AI Fluency Framework .
NEW QUESTION # 92
You are an operations lead evaluating prompting strategies that teammates have proposed for various task types.
Which two strategies represent appropriate matches between strategy and task type? (Select two.) Each correct answer presents part of the solution.
Answer: B,E
Explanation:
Under the objective Adapt prompting strategies based on task type (e.g., analysis, research, drafting, brainstorming) , prompting architectures must match the operational nature and variability of the task.
Option C correctly pairs open, exploratory prompting with divergent brainstorming and early-stage strategy, allowing Claude the flexibility to generate wide-ranging perspectives, identify unexpected market angles, and propose creative concepts. Option E correctly pairs rigid, templated prompting (with explicit constraints, schema definitions, and validation rules) with recurring compliance reporting, where deterministic output structure, consistency, and adherence to regulatory schemas are mandatory. Conversely, applying rigid templates to creative ideation (Option A) constrains creativity, using open exploration for contract redlining (Option B) risks hallucination and missed clauses, and enforcing rigid schemas on informal unformatted feedback (Option D) creates unnecessary rigidity.
NEW QUESTION # 93
You are a knowledge worker evaluating which Claude feature matches each proposed use case.
Which two feature-to-use-case matches are appropriate? (Select two.)
Each correct answer presents a complete solution.
Answer: A,E
Explanation:
Options A and E correctly match capabilities to tasks. A quarterly report derived from many documents benefits from Claude's long-context processing because the model must review, connect, and synthesize information distributed across numerous sources. The user should organize the sources clearly, identify the required reporting structure, and verify that the resulting report accurately represents the inputs.
A complex multistep pricing calculation should use a calculator or code-execution capability. Language models can reason about a calculation's structure, but deterministic computation tools provide stronger numerical reliability and make intermediate operations easier to inspect. The human reviewer should still verify inputs, formulas, units, and business rules.
Option B does not require long-context processing because a single short email can ordinarily be handled within a basic chat request. Option C unnecessarily invokes web search for a stable, established mathematical formula that can be supplied directly or calculated with an appropriate tool. Option D mismatches a numerical tool with a language-editing task; rewriting a description in simpler language is a core text-generation capability and does not require a calculator.
Effective feature selection means matching the task's actual bottleneck-context capacity, external information retrieval, deterministic computation, or reusable content creation-to the capability designed to address it.
NEW QUESTION # 94
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