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| Section | Weight | Objectives |
|---|---|---|
| Configuration and Knowledge Management | 12% | - Configure Claude Projects with instructions and knowledge sources - Manage uploaded knowledge and connectors - Create effective system-level instructions - Maintain and update configurations |
| 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 |
| Product and Model Selection | 12% | - Align model selection with task requirements - Differentiate between Claude model types - Select appropriate Claude product features - Manage context limitations and memory |
| Troubleshooting and Optimization | 10% | - Adjust approaches based on feedback and results - Identify and diagnose issues with underperforming prompts or poor outputs - Optimize workflows for efficiency and effectiveness |
| Governance, Risk, and Responsible Use | 15% | - Apply data sensitivity, regulatory, and privacy considerations - Follow organizational AI policies and governance standards - Identify appropriate and inappropriate use cases - Understand ethical implications of Claude use |
| Prompting and Task Execution | 14% | - Adapt prompting strategy by task type - Create effective prompts for business and technical tasks - Apply task decomposition techniques to structure complex requests - Iterate prompts to improve output quality |
| Output Evaluation and Validation | 21% | - Edit and adapt outputs for the intended audience - Organize and curate information and select appropriate output formats - Apply fact-checking and validation techniques - Evaluate Claude-generated outputs for accuracy and completeness - Determine when human review or additional verification is required - Identify hallucinations, inconsistencies, and biases in responses |
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質問 # 66
You are a knowledge worker summarizing common prompt failure patterns for the team.
Which pattern most often produces vague or off-target output?
正解：B
解説：
Option A describes an under-specified prompt. Naming only a topic does not tell Claude what action to perform, who will use the result, which information should receive priority, or how the response must be organized. Claude must then infer these requirements, increasing the likelihood of a generic, mis-scoped, or incorrectly formatted response.
An effective business prompt normally identifies the task, supplies relevant context, defines the intended audience, states the desired output structure, and includes material constraints such as tone, length, scope, or exclusions. A representative example can further clarify the expected standard. Consequently, Options B, C, and D describe techniques that generally reduce ambiguity rather than cause it.
Anthropic's prompting guidance recommends clear, detailed instructions and relevant context, while its prompt-engineering framework assumes that users first define success criteria and a testable objective. The prompt should make the expected behavior observable: "summarize this policy for store managers in a five- bullet briefing" is materially stronger than "employee policy." When an output is vague, the correct diagnosis is usually not insufficient model capability but insufficiently described intent, audience, or format. Claude usage and prompting guidance
質問 # 67
You are an operations lead integrating Claude into a multi-person team workflow.
Which integration step should be performed first?
正解：B
解説：
Under the objective Integrate Claude into existing workflows to augment or redesign workflows , successful AI adoption requires structured process mapping prior to implementation, piloting, or scaling.
The foundational first step (Option A) is conducting task decomposition and interface analysis: analyzing the existing workflow to determine exact stages where Claude provides tangible leverage (e.g., synthesis, drafting, data categorization) versus stages that mandate human decision-making, verification, and sign-off (human-in-the-loop handoffs). Attempting to run a pilot (Option B), iterate based on pilot metrics (Option C), or author final training documentation (Option D) before defining the functional boundaries and handoff architecture results in unstructured trials, unclear accountability, and higher operational risk.
質問 # 68
You are a customer-success manager considering whether to escalate a Claude-generated response.
Which factor most strongly raises the impact of the output and supports escalation before external use?
正解：B
質問 # 69
You are reviewing claims about prompt iteration that colleagues have made during recent training.
Which two claims about prompt iteration are accurate? (Select two.)
Each correct answer presents a complete solution.
正解：B、C
解説：
Options C and E are correct because disciplined prompt iteration is an evidence-based improvement cycle.
First, compare the output with the original task requirements and success criteria. Identify a specific weakness, such as missing evidence, excessive length, incorrect tone, poor structure, ambiguous instructions, or failure to follow a required schema. Then make a targeted change addressing that weakness and compare the new output with the previous version using the same evaluation criteria.
Option B changes nothing except the sampled response, so it does not reliably diagnose or repair the prompt.
Option D is false because additional iterations can introduce regressions, conflicting instructions, or unnecessary complexity. Option A proposes an impossible and wasteful stopping rule; there are effectively unlimited alternative phrasings. Iteration should stop when the output meets the established quality threshold, when improvement has plateaued, or when a different model, workflow, tool, or human-editing approach is more appropriate.
Anthropic describes prompt engineering as a cycle of defining success criteria, testing representative cases, refining the prompt, and validating the result. Consequently, targeted modification and criteria-based comparison-not repetition or iteration volume-determine whether the process is productive. See Anthropic' s evaluation framework and prompting guidance .
質問 # 70
You are running a weekly feedback-and-adjustment cycle.
Which is the correct order of cycle steps?
(1) Translate feedback into one or two targeted adjustments for the next cycle.
(2) Run the next cycle and compare results against the prior cycle.
(3) Capture the specific feedback received during the review.
(4) Document what changed, what effect it produced, and any remaining issues.
(5) Decide whether to continue iterating, switch approach, or escalate.
正解：A
解説：
Under the objective Adjust approach based on feedback and results , structured workflow iteration follows a disciplined empirical sequence to guarantee controlled optimization.
The feedback loop begins with intake: systematically capturing concrete, specific feedback and defect observations from the recent review session (Step 3). Next, that feedback is translated into one or two targeted, hypothesis-driven prompt or workflow modifications (Step 1). The practitioner then executes the revised workflow and directly benchmarks the new outputs against the previous baseline (Step 2). After execution, the practitioner documents the exact changes made, the measurable effects observed, and any residual edge cases (Step 4). Finally, the practitioner evaluates the results to determine the strategic direction-whether to perform another iteration cycle, pivot to an alternative prompting approach, or escalate to human stakeholders (Step 5). This establishes the correct operational sequence as 3, 1, 2, 4, 5 .
質問 # 71
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