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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: Troubleshooting and Optimization | 10% | - Identify causes of poor or unexpected outputs - Diagnose context and performance issues - Adjust prompts and configuration to improve results |
| Topic 3: Product and Model Selection | 12% | - Select appropriate Claude product features - Match model capabilities to task requirements - Understand model differences and use cases |
| Topic 4: Prompting and Task Execution | 14% | - Create effective prompts for business and technical tasks - Apply task decomposition to structure complex requests - Iterate prompts to improve output quality - Adapt prompting strategies by task type |
| Topic 5: Governance, Risk, and Responsible Use | 15% | - Manage data sensitivity and privacy risks - Apply responsible AI principles and policies - Understand limitations and ethical boundaries |
| Topic 6: Configuration and Knowledge Management | 12% | - Manage knowledge sources and context - Configure Claude Projects with instructions - Maintain consistent system-level behavior |
| Topic 7: Output Evaluation and Validation | 21% | - Determine when human review is required - Evaluate outputs for accuracy, completeness, and consistency - Detect hallucinations, biases, and inconsistencies - Apply fact-checking and validation methods |
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NEW QUESTION # 25
A knowledge worker often switches between Claude and another AI service while working on a long-running project.
Which Claude feature most directly reduces repetitive context-setting between sessions?
Answer: D
Explanation:
Under the objective Select appropriate Claude product features (e.g., projects, research mode, chat, artifacts) , maintaining persistent context across extended workflows is supported by Claude's Memory features.
Claude's Memory allows users to store key preferences, ongoing project parameters, and working context that persist across independent chat sessions. Utilizing the memory import capability to transfer core project context from previous workspaces or external AI tools into Claude provides an immediate, grounded foundation for future interactions without requiring manual context re-entry in every session (Option A). In contrast, pasting static Artifacts manually into each new session (Option B) creates repetitive friction.
Utilizing Incognito mode (Option C) deliberately disables session memory, eliminating continuity. Disabling Memory and repeatedly re-pasting reference paragraphs (Option D) disregards the built-in feature designed to automate persistent context.
NEW QUESTION # 26
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?
Answer: C
Explanation:
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.
NEW QUESTION # 27
A business analyst is converting a sticky-note request from a stakeholder into a structured prompt for Claude.
Which is the correct order of conversion steps?
(1) Confirm the audience and the decision the response must support.
(2) Submit the structured prompt to Claude.
(3) Identify the underlying task hidden in the sticky-note language.
(4) Specify the desired length, tone, and output format.
(5) Add the supporting facts and constraints relevant to the task.
Answer: D
Explanation:
Answer C gives the proper conversion sequence. The analyst first determines the real task hidden behind the informal sticky-note wording (3); without that step, the prompt may solve the wrong problem. Next, the analyst confirms who will consume the result and which decision it must support (1). Audience and decision purpose determine what evidence is relevant, so the supporting facts, boundaries, and constraints are added afterward (5). Once the substantive requirements are stable, the analyst specifies presentation requirements such as length, tone, and output format (4). The completed prompt can then be submitted to Claude (2). This yields 3, 1, 5, 4, 2. Option A starts with the audience before clarifying the underlying task. Option D supplies facts before establishing the decision context, which can result in irrelevant or insufficient context. Option B begins by submitting a prompt that has not yet been structured. Anthropic's official prompting guidance says Claude performs best with clear, explicit instructions, relevant context, precise output constraints, and sequentially stated steps when order or completeness matters. These principles align directly with the AI Fluency Description competency: clearly describe the intended product, process, and interaction before expecting a reliable result. Anthropic prompting best practices
NEW QUESTION # 28
You are a content creator. A first response from Claude is accurate but uses inconsistent terminology that does not match your organization's glossary.
Which iterative change addresses the issue most directly?
Answer: D
Explanation:
Under the objective Iterate prompts to improve output quality (Domain 1: Prompting and Task Execution), resolving domain-specific vocabulary inconsistencies requires grounding the prompt with explicit terminology constraints.
When Claude generates accurate conceptual content but uses generic or non-standard synonyms, the most direct prompt refinement is providing the organization's canonical terminology glossary (or uploading it to Project knowledge) accompanied by a strict constraint instructing the model to use only those defined terms (Option D). This eliminates vocabulary drift and guarantees brand consistency. Expanding prompt length (Option A) amplifies terminology discrepancies. Encouraging invented terms (Option B) worsens terminology alignment. Deleting topics from the prompt (Option C) fails the original task requirements.
NEW QUESTION # 29
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: D
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 # 30
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