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| Section | Weight | Objectives |
|---|---|---|
| Governance, Risk, and Responsible Use | 15% | - Follow organizational AI policies and governance standards - Understand ethical implications of Claude use - Identify appropriate and inappropriate use cases - Apply data sensitivity, regulatory, and privacy considerations |
| Product and Model Selection | 12% | - Align model selection with task requirements - Manage context limitations and memory - Select appropriate Claude product features - Differentiate between Claude model types |
| Prompting and Task Execution | 14% | - Iterate prompts to improve output quality - Create effective prompts for business and technical tasks - Apply task decomposition techniques to structure complex requests - Adapt prompting strategy by task type |
| Workflow Integration and Solution Design | 16% | - Integrate Claude into existing workflows and communicate its value and limitations - Use Claude for research, planning, and process optimization - Support solution design through iteration - Analyze requirements and use cases |
| Configuration and Knowledge Management | 12% | - Maintain and update configurations - Configure Claude Projects with instructions and knowledge sources - Create effective system-level instructions - Manage uploaded knowledge and connectors |
| Output Evaluation and Validation | 21% | - Organize and curate information and select appropriate output formats - Identify hallucinations, inconsistencies, and biases in responses - Edit and adapt outputs for the intended audience - Determine when human review or additional verification is required - Evaluate Claude-generated outputs for accuracy and completeness - Apply fact-checking and validation techniques |
| Troubleshooting and Optimization | 10% | - Identify and diagnose issues with underperforming prompts or poor outputs - Optimize workflows for efficiency and effectiveness - Adjust approaches based on feedback and results |
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NEW QUESTION # 107
A business analyst has been asked to use Claude to extract requirements from a recorded stakeholder interview transcript. The transcript is approximately 90 minutes long and covers multiple topics.
Which approach is most likely to produce a usable structured output?
Answer: A
Explanation:
Under the objective Create effective prompts for business and technical tasks , prompting Claude for complex analysis requires clear framing, explicit task decomposition, and structural schema constraints.
Transcripts from 90-minute interviews contain unstructured, conversational discussions spanning multiple priorities, assumptions, and debates. Providing the raw source material alongside a structured schema- specifying categories such as must-haves, nice-to-haves, unresolved open questions, and stakeholder conflicts (Option A)-enables Claude to parse and categorize information accurately without losing critical context. In contrast, forcing a single flat list (Option B) flattens priority nuances and obscures conflicts. Summarizing first (Option C) introduces unnecessary loss of detail and risks compounding omissions in the second pass.
Parsing every statement containing a verb (Option D) creates extreme token noise and unworkable overhead.
NEW QUESTION # 108
A marketing associate is drafting Project custom instructions for a brand-voice writing workspace. The workspace will be used by several writers, and outputs need to be consistent across writers and chats.
Which approach is most likely to produce consistent outputs across many chats?
Answer: B
Explanation:
Project custom instructions should contain persistent behavioral requirements that apply to every conversation in the workspace. Option C is correct because it explicitly defines Claude's role, intended audience, brand attributes, tone boundaries, and terminology requirements. These elements collectively reduce ambiguity and help different writers obtain consistent results across separate chats.
Option A relies too heavily on a single example. Examples can demonstrate style, but they may not communicate every prohibited tone, terminology rule, or audience requirement. Option B delegates important persistent constraints to individual writers, creating predictable variation between chats. Option D places the glossary in Project knowledge but fails to make terminology compliance an explicit behavioral requirement.
Claude may reference knowledge files when relevant, but custom instructions should directly state that approved terminology must be followed.
The strongest configuration separates behavior from supporting material: custom instructions define what Claude must consistently do, while Project knowledge stores detailed glossaries, brand guides, approved examples, and other reference documents. This aligns with Claude Project guidance concerning reusable instructions, scoped knowledge, roles, workflows, and consistent output expectations.
NEW QUESTION # 109
A project manager is evaluating a proposal to use Claude to autonomously approve refunds above a defined threshold without human review. The volume is high-roughly 2,000 refunds per week-the average refund amount is moderate, and a small percentage of refunds in historical audits required correction. The team needs to reduce cycle time.
Which response is most appropriate?
Answer: D
Explanation:
Option B provides the strongest balance between operational efficiency and proportionate human oversight.
The historical correction rate demonstrates that Claude's output cannot safely be treated as uniformly reliable, while the high weekly volume makes universal manual processing unnecessarily inefficient. A risk-based triage design allows clearly defined, low-value, reversible cases to proceed through tightly controlled automation while routing ambiguous or higher-impact cases to accountable human decision-makers.
The classification rules must be explicit and independently tested. Relevant factors could include refund amount, unusual transaction history, policy exceptions, disputed facts, suspected fraud, and the confidence or completeness of the available evidence. Standard cases would remain subject to human approval, while high- risk cases would receive supervisor review.
Options A and C rely primarily on retrospective sampling. That approach may detect patterns of errors, but it does not prevent an incorrect refund from being issued in an individual case. Option D preserves strong oversight but requires manual approval for every transaction, failing to use risk differentiation to address the cycle-time problem.
Claude should augment the workflow through classification, evidence gathering, and recommendation.
Human authority must remain at the points where uncertainty, financial impact, or policy exceptions make an error more consequential.
NEW QUESTION # 110
You are a refining a prompt over several iterations. The output drifts further from the requested template with each change.
Which step is most appropriate?
Answer: A
Explanation:
Under the objective Iterate prompts to improve output quality , managing prompt development requires maintaining baseline versioning and avoiding cumulative degradation.
When successive prompt revisions introduce drift, unintended behaviors, or progressive degradation away from the target output template, continuing to add modifications on top of an already degraded prompt only exacerbates the issue. The appropriate engineering practice is rollback: reverting immediately to the historical version that yielded the highest quality baseline (Option C) and resuming disciplined, single-variable iterations from that verified state. Discarding all historical prompt versions (Option A) loses valuable context, making multiple erratic modifications simultaneously (Option B) creates further unpredictability, and accepting substandard output (Option D) fails quality assurance standards.
NEW QUESTION # 111
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: A
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 # 112
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