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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prompting and Task Execution | 14% | - Create effective prompts for business and technical tasks - Adapt prompting strategy by task type - Iterate prompts to improve output quality - Apply task decomposition techniques to structure complex requests |
| Topic 2: Governance, Risk, and Responsible Use | 15% | - Understand ethical implications of Claude use - Identify appropriate and inappropriate use cases - Apply data sensitivity, regulatory, and privacy considerations - Follow organizational AI policies and governance standards |
| Topic 3: Workflow Integration and Solution Design | 16% | - Support solution design through iteration - Use Claude for research, planning, and process optimization - Analyze requirements and use cases - Integrate Claude into existing workflows and communicate its value and limitations |
| Topic 4: 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 5: Product and Model Selection | 12% | - Manage context limitations and memory - Select appropriate Claude product features - Differentiate between Claude model types - Align model selection with task requirements |
| Topic 6: Output Evaluation and Validation | 21% | - Evaluate Claude-generated outputs for accuracy and completeness - Determine when human review or additional verification is required - Edit and adapt outputs for the intended audience - Apply fact-checking and validation techniques - Identify hallucinations, inconsistencies, and biases in responses - Organize and curate information and select appropriate output formats |
| Topic 7: Configuration and Knowledge Management | 12% | - Create effective system-level instructions - Maintain and update configurations - Manage uploaded knowledge and connectors - Configure Claude Projects with instructions and knowledge sources |
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NEW QUESTION # 75
You are a knowledge worker evaluating which Claude feature matches each proposed use case.
Which two feature-to-use-case matches are appropriate? (Select two.)
Each correct answer presents a complete solution.
Answer: B,C
Explanation:
Options A and E correctly match capabilities to tasks. A quarterly report derived from many documents benefits from Claude's long-context processing because the model must review, connect, and synthesize information distributed across numerous sources. The user should organize the sources clearly, identify the required reporting structure, and verify that the resulting report accurately represents the inputs.
A complex multistep pricing calculation should use a calculator or code-execution capability. Language models can reason about a calculation's structure, but deterministic computation tools provide stronger numerical reliability and make intermediate operations easier to inspect. The human reviewer should still verify inputs, formulas, units, and business rules.
Option B does not require long-context processing because a single short email can ordinarily be handled within a basic chat request. Option C unnecessarily invokes web search for a stable, established mathematical formula that can be supplied directly or calculated with an appropriate tool. Option D mismatches a numerical tool with a language-editing task; rewriting a description in simpler language is a core text-generation capability and does not require a calculator.
Effective feature selection means matching the task's actual bottleneck-context capacity, external information retrieval, deterministic computation, or reusable content creation-to the capability designed to address it.
NEW QUESTION # 76
A Claude associate is reviewing a Claude-drafted set of customer-segment descriptions for an internal marketing brief. One description applies a generalization about a demographic group's purchasing behavior that does not hold for individual customers in the segment.
Which response is most appropriate?
Answer: B
Explanation:
Option B removes the unsupported demographic generalization and replaces it with evidence-based behavioral analysis. A useful customer segment should be grounded in observable factors-such as purchase frequency, product usage, channel preference, or demonstrated needs-rather than assumptions about how members of a demographic group supposedly behave.
Internal distribution does not eliminate bias risk. An internal marketing brief may influence targeting, product decisions, resource allocation, or customer treatment. Therefore, Option A is incorrect. Option C is unnecessarily broad because responsible segmentation remains possible when criteria are relevant, lawful, and supported by data. Option D preserves the biased claim and merely adds a disclaimer; acknowledging that a generalization is unreliable does not make it appropriate to retain.
The reviewer should identify the unsupported statement, examine the underlying evidence, replace group- level assumptions with validated patterns, and test whether the revised description unfairly disadvantages or misrepresents customers. Anthropic's Usage Policy prohibits promoting discriminatory practices based on protected or identifying attributes and emphasizes responsible handling of potentially biased outputs. AI Fluency Discernment similarly requires users to assess the usefulness, limitations, and consequences of generated material rather than accepting plausible wording uncritically. Anthropic's Usage Policy
NEW QUESTION # 77
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,B
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 # 78
You are a knowledge worker reviewing a Claude-generated brief that asserts a "well-known industry standard" without naming any source.
How should this assertion be handled?
Answer: C
Explanation:
The phrase "well-known industry standard" is not evidence. Without the name of the standard, its issuing body, version, publication date, or an authoritative citation, the statement cannot be independently verified.
Option D correctly treats the assertion as unsupported and requires either authoritative substantiation or removal.
Claude can produce fluent, confident language that appears credible even when the underlying claim is incomplete, inaccurate, or fabricated. Adding stronger language, as proposed in Option A, would amplify the unsupported claim rather than correct it. Options B and C confuse plausibility and confidence with verification. A claim does not become reliable merely because it sounds familiar or uses reassuring terminology.
The reviewer should identify the purported standard, locate the original issuing organization or official publication, confirm that the standard applies to the subject and jurisdiction, and cite the relevant section. If those checks cannot be completed, the claim should be deleted or rewritten to disclose uncertainty. Anthropic recommends grounding factual statements in direct source material, using citations to make claims auditable, and retracting claims for which supporting evidence cannot be found. This approach directly addresses hallucination risk while preserving the brief's credibility. Anthropic's hallucination-reduction guidance
NEW QUESTION # 79
You are a project manager reviewing Claude usage approaches that teammates have proposed for a project workflow.
Which two workflow steps are well-suited for Claude support? (Select two.) Each correct answer presents a complete solution.
Answer: A,D
Explanation:
Under the objective Identify appropriate and inappropriate use cases , determining suitable application domains hinges on evaluating AI capabilities against risk severity and the presence of human oversight.
Claude excels at information extraction, multi-document synthesis, and structured text generation.
Synthesizing complex findings from multiple research papers (Option D) and compiling meeting notes into organized, structured status summaries (Option E) are classic high-leverage knowledge-work tasks that streamline team operations while keeping final outputs subject to team review. Conversely, allowing an AI model to autonomously authorize external corporate press releases without human sign-off (Option A), bind an organization to contractual liabilities (Option B), or determine career-altering personnel promotions (Option C) represents dangerous and inappropriate delegation that violates responsible AI principles, legal compliance, and human accountability standards.
NEW QUESTION # 80
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