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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Governance, Risk, and Responsible Use | 15% | - Follow organizational AI policies and governance standards - Apply data sensitivity, regulatory, and privacy considerations - Identify appropriate and inappropriate use cases - Understand ethical implications of Claude use |
| Topic 2: Prompting and Task Execution | 14% | - Iterate prompts to improve output quality - Apply task decomposition techniques to structure complex requests - Create effective prompts for business and technical tasks - Adapt prompting strategy by task type |
| Topic 3: 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 4: Output Evaluation and Validation | 21% | - Identify hallucinations, inconsistencies, and biases in responses - Organize and curate information and select appropriate output formats - Evaluate Claude-generated outputs for accuracy and completeness - Apply fact-checking and validation techniques - Edit and adapt outputs for the intended audience - Determine when human review or additional verification is required |
| Topic 5: Configuration and Knowledge Management | 12% | - Manage uploaded knowledge and connectors - Create effective system-level instructions - Configure Claude Projects with instructions and knowledge sources - Maintain and update configurations |
| Topic 6: Workflow Integration and Solution Design | 16% | - Use Claude for research, planning, and process optimization - Integrate Claude into existing workflows and communicate its value and limitations - Analyze requirements and use cases - Support solution design through iteration |
| Topic 7: Product and Model Selection | 12% | - Select appropriate Claude product features - Manage context limitations and memory - Align model selection with task requirements - Differentiate between Claude model types |
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問題 #131
An HR business partner is configuring two separate Projects for two distinct client engagements.
Which configuration best prevents context bleed between the two engagements?
答案：D
解題說明：
Under the Claude objective Configure Claude Projects with instructions and knowledge sources , preventing context bleed and data crossover between distinct clients is a critical privacy, security, and project governance requirement. Projects in Claude serve as isolated contextual containers designed to compartmentalize project-specific knowledge files, custom system instructions, and chat histories.
Option A correctly implements strict boundary isolation by creating dedicated, independent Projects for each client engagement. By giving each Project its own discrete knowledge base and unique system prompt, reference material and conversational state from one client remain entirely inaccessible to the other. In contrast, combining client documentation into a single shared Project (Option B) or sharing memory configurations across workstreams (Option C) directly causes cross-tenant contamination and context bleed.
Similarly, using unstructured general chats without Project containers (Option D) lacks scoped memory and persistence boundaries, significantly elevating operational and confidentiality risks.
問題 #132
You are an analyst reviewing a Claude response that addresses two of three required questions and skips the third.
Which prompt element most likely needs strengthening?
答案：C
解題說明：
Under the objective Identify, diagnose, and resolve issues with underperforming prompts and/or poor outputs , diagnosing incomplete outputs requires mapping the specific failure mode directly to the corresponding prompt component.
When a model reliably answers some inquiries but completely omits another, the root cause is typically prompt ambiguity or buried instructions within dense prose. Strengthening the core task statement by explicitly enumerating the required deliverables (e.g., using numbered lists or tagged XML blocks like < question_1 > , < question_2 > , < question_3 > ) and mandating a complete response for each item (Option D) provides clear structural constraints that prevent omissions. Adding generic background context (Option A), changing persona framing (Option C), or relaxing output formatting constraints (Option B) fails to address the underlying instruction-following issue.
問題 #133
A knowledge worker is explaining context-window basics to a teammate.
Which statement most accurately describes a context window?
答案：D
解題說明：
Answer B is correct. A context window is the bounded amount of information that a model can consider when generating a response. Depending on the Claude interface or implementation, this can include the current user message, earlier messages in the active conversation, system instructions, uploaded documents, tool results, and other supplied content. The capacity is finite and is normally measured in tokens rather than words or pages.
Option A incorrectly defines the context window as only the information the model considers most relevant.
Relevance selection or retrieval can determine which materials are placed into the window, but the context window itself is the total bounded processing capacity. Option C incorrectly treats it as unlimited cross- session memory. A model does not automatically receive every previous conversation whenever it responds in a different chat. Any available memory or Project knowledge is a separate feature and must be supplied or retrieved into the active context. Option D is also incorrect because users generally do not configure the context-window size independently for each chat.
When a conversation becomes too long or cluttered, the user may need to summarize essential information, remove irrelevant material, or start a focused new conversation. This preserves relevant context and reduces conflicts between current and obsolete instructions. Anthropic: Context windows
問題 #134
You are setting up a Connector for a shared data source that several colleagues will use.
Which Connector setup step should be performed first?
答案：C
解題說明：
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.
問題 #135
An operations assistant is using Claude to support process optimization for an existing intake workflow. The assistant has access to the current process documentation and recent cycle-time data.
Which activity best uses Claude to support this process optimization task?
答案：B
解題說明：
Under the objective Leverage Claude for research, planning, and process optimization , using Claude for business process re-engineering relies on diagnostic synthesis grounded in empirical operational data.
Option C demonstrates the optimal collaborative use case: ingesting existing process maps alongside actual cycle-time metrics, using Claude's analytical capabilities to locate workflow bottlenecks, delays, and redundancies, and generating structured optimization proposals for human review and decision-making.
Relying on generic, ungrounded industry templates (Option A) overlooks internal operational context.
Expecting Claude to calculate exact cycle-time reductions deterministically (Option B) misuses LLMs for predictive queuing simulations. Replacing human stakeholder interviews with simulated automated AI interactions (Option D) produces ungrounded assumptions and fails change management standards.
問題 #136
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