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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Configuration and Knowledge Management | 12% | - Create effective system-level instructions - Manage uploaded knowledge and connectors - Configure Claude Projects with instructions and knowledge sources - Maintain and update configurations |
| Topic 2: Product and Model Selection | 12% | - Select appropriate Claude product features - Manage context limitations and memory - Differentiate between Claude model types - Align model selection with task requirements |
| Topic 3: Governance, Risk, and Responsible Use | 15% | - Follow organizational AI policies and governance standards - Understand ethical implications of Claude use - Apply data sensitivity, regulatory, and privacy considerations - Identify appropriate and inappropriate use cases |
| Topic 4: Output Evaluation and Validation | 21% | - Determine when human review or additional verification is required - Edit and adapt outputs for the intended audience - Apply fact-checking and validation techniques - Organize and curate information and select appropriate output formats - Evaluate Claude-generated outputs for accuracy and completeness - Identify hallucinations, inconsistencies, and biases in responses |
| Topic 5: Troubleshooting and Optimization | 10% | - Optimize workflows for efficiency and effectiveness - Identify and diagnose issues with underperforming prompts or poor outputs - Adjust approaches based on feedback and results |
| Topic 6: Workflow Integration and Solution Design | 16% | - Analyze requirements and use cases - Integrate Claude into existing workflows and communicate its value and limitations - Use Claude for research, planning, and process optimization - Support solution design through iteration |
| Topic 7: Prompting and Task Execution | 14% | - Iterate prompts to improve output quality - Create effective prompts for business and technical tasks - Adapt prompting strategy by task type - Apply task decomposition techniques to structure complex requests |
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NEW QUESTION # 84
You are optimizing a recurring Claude workflow and must complete the diagnostic steps before changing the workflow.
Which two diagnostic steps must be completed BEFORE changing the recurring workflow? (Select two.) Each correct answer presents part of the solution.
Answer: A,C
Explanation:
Under the objective Identify, diagnose, and resolve issues with underperforming prompts and/or poor outputs , workflow optimization must be rooted in evidence-based diagnostics before making modifications to system instructions or prompt chains.
The diagnostic phase requires two prerequisite steps: establishing a quantitative baseline by measuring quality metrics, latency, token spend, and human intervention frequency across recent historical runs (Option D), and conducting a bottleneck analysis to pinpoint the exact sub-steps responsible for performance degradation, excessive token cost, or quality defects (Option B). Implementing changes without these baseline diagnostics leads to blind iterations. Deployment (Option C) and SOP updates (Option E) are downstream execution phases, while leadership notifications (Option A) occur after solutions are verified.
NEW QUESTION # 85
A knowledge worker is defining what "workflow optimization" means at the Associate level for the team's recurring AI-supported workflows.
Which description is most accurate?
Answer: C
Explanation:
Option B gives the most accurate definition of workflow optimization. The objective is to improve measurable performance-such as cycle time, quality, consistency, cost, or throughput-without removing controls that protect accuracy, privacy, compliance, and accountable decision-making. Claude should be introduced where its capabilities provide clear leverage, such as drafting, summarization, classification, source synthesis, or structured extraction.
Option A incorrectly assumes that fewer steps always produce a better workflow. Some steps, including validation, escalation, approval, and documentation, exist because they control significant risks. Removing them may reduce apparent processing time while increasing rework or downstream harm. Option C similarly treats maximum automation as the goal. A manual step should remain human-controlled when it requires authority, empathy, professional judgment, or responsibility for a consequential decision. Option D confuses optimization with inflexible standardization. Templates can improve consistency, but workflows must still accommodate legitimate differences in inputs and risk.
A sound optimization process maps the current workflow, measures its bottlenecks, removes duplication, selects suitable Claude-supported steps, defines hand-offs, retains necessary reviews, and pilots the revised process against a baseline. The change is successful only when evidence shows that it improves the intended operational outcomes without weakening quality or governance.
NEW QUESTION # 86
A customer-success associate is drafting a prompt that asks Claude to summarize a customer issue from the case file. The case file includes the customer ' s full government identifier, home address, name, account number, and the description of the issue. The summary the associate needs is about the issue.
Which approach is most appropriate?
Answer: B
Explanation:
Under the objective Apply data, privacy sensitivity, regulatory policies, and privacy considerations (Domain
6: Governance, Risk, and Responsible Use), data minimization is a primary privacy requirement.
When processing enterprise documents with AI tools, users must never submit highly sensitive Personally Identifiable Information (PII)-such as national government identifiers (SSNs/National Insurance numbers) or private home addresses-unless strictly required for the analytical task. Because the associate only needs a summary of the core customer problem, redacting or removing those non-essential high-sensitivity fields prior to input ingestion (Option C) enforces sound privacy hygiene. Relying on in-prompt instructions to " ignore " ingested PII (Option A) still exposes raw sensitive tokens to the model context. Swapping identifiers for other sensitive account data (Option B) fails data protection standards.
NEW QUESTION # 87
You are an operations team member preparing inputs to summarize customer feedback. The feedback file mixes public quotations with personal contact details.
Which preparation approach is most appropriate?
Answer: C
Explanation:
Option D follows data-minimization and purpose-limitation principles. The task requires analysis of customer feedback, not access to customer contact information. The appropriate preparation process is therefore to retain the feedback statements needed for the analysis while removing names, email addresses, telephone numbers, account identifiers, and other unnecessary personal information.
Option A removes the useful content while retaining the sensitive data, directly reversing the correct handling approach. Option B exposes unnecessary personal information merely to provide more context. Option C discards the genuine feedback and invites Claude to fabricate replacement content, producing an unreliable summary. Redaction or anonymization preserves the analytical value of the input while reducing privacy exposure and limiting the consequences of unauthorized disclosure.
Anthropic advises users to be thoughtful about sharing highly sensitive personal or confidential information.
Its implementation guidance also recommends removing personally identifiable information unless it is explicitly required and authorized. Redaction does not override organizational policy: the user must still confirm that the approved Claude environment, access controls, contractual terms, retention settings, and intended purpose permit the remaining data to be processed. Anthropic's sensitive-data guidance
NEW QUESTION # 88
You are reviewing Connector setups that colleagues have proposed for various tasks.
Which two Connector setups represent safe and appropriate uses? (Select two.) Each correct answer presents a complete solution.
Answer: A,C
Explanation:
Under the objective Manage uploaded knowledge and connectors (e.g., Google Drive, Gmail) , establishing data connectors requires adhering to the principle of least privilege, strict scope boundary management, and robust data classification.
Option B is an authorized, safe integration: connecting Claude to an approved, bounded project management workspace with explicit scope controls allows the model to summarize tasks and sprint updates safely. Option D is an ideal read-only integration, granting access to public documentation and reference repositories where confidential data leakage risks are zero. Conversely, connecting AI tools to core payroll infrastructure (Option E) or sensitive personnel/HR databases (Option A) introduces severe compliance, privacy, and unauthorized access liabilities. Linking personal cloud storage repositories containing unvetted mixed personal data (Option C) violates data governance standards by risking unintentional exposure of private files.
NEW QUESTION # 89
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