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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 - Determine when human review or additional verification is required - Identify hallucinations, inconsistencies, and biases in responses - Evaluate Claude-generated outputs for accuracy and completeness |
| Topic 2: Configuration and Knowledge Management | 12% | - Maintain and update configurations - Manage uploaded knowledge and connectors - Create effective system-level instructions - Configure Claude Projects with instructions and knowledge sources |
| Topic 3: 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 |
| Topic 4: Workflow Integration and Solution Design | 16% | - Use Claude for research, planning, and process optimization - Analyze requirements and use cases - Support solution design through iteration - Integrate Claude into existing workflows and communicate its value and limitations |
| Topic 5: Product and Model Selection | 12% | - Differentiate between Claude model types - Select appropriate Claude product features - Manage context limitations and memory - Align model selection with task requirements |
| Topic 6: Governance, Risk, and Responsible Use | 15% | - Understand ethical implications of Claude use - Follow organizational AI policies and governance standards - Identify appropriate and inappropriate use cases - Apply data sensitivity, regulatory, and privacy considerations |
| Topic 7: Prompting and Task Execution | 14% | - Create effective prompts for business and technical tasks - Apply task decomposition techniques to structure complex requests - Adapt prompting strategy by task type - Iterate prompts to improve output quality |
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NEW QUESTION # 62
You are a knowledge worker summarizing common prompt failure patterns for the team.
Which pattern most often produces vague or off-target output?
Answer: D
Explanation:
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
NEW QUESTION # 63
You are a knowledge manager structuring source material in the context window for a long Claude evaluation task.
Which two practices produce a more effective use of the context window? (Select two.) Each correct answer presents a complete solution.
Answer: D,E
Explanation:
Under the objective Organize and curate information for optimal outputs and select appropriate output formats , structuring reference documents effectively within the context window ensures high-fidelity retrieval and accurate source attribution.
Option B highlights the standard prompt engineering practice of using distinct structural delimiters (such as XML tags like < doc_1 > , < policy > , or Markdown code fences). Delimiters allow Claude to distinguish cleanly between different source boundaries, instructions, and inputs, minimizing attribution errors. Option E leverages positional attention dynamics (addressing the "lost in the middle" phenomenon), where LLMs tend to recall and attend most accurately to information positioned at the very beginning (primacy effect) or at the end (recency effect) of the context window. Conversely, dumping uncurated text (Option A) adds noise and consumes quota, repeating instructions excessively (Option C) wastes tokens without improving adherence, and blending documents into an unstructured blob (Option D) obscures citation provenance.
NEW QUESTION # 64
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: C
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 # 65
You are using Claude to redesign an employee onboarding workflow and must complete the high-level decomposition steps before specifying detailed artifacts.
Which two decomposition steps must be completed BEFORE specifying exact email content and the schedule? (Select two.) Each correct answer presents part of the solution.
Answer: B,D
Explanation:
Under the objective Apply task decomposition techniques to structure complex requests , complex business problems must be decomposed hierarchically-from macro objectives down to granular operational artifacts.
Before generating low-level execution components such as scheduled email templates or individual checklists, the architect must first establish the high-level structure. This entails defining the overall strategic objective of the program (Option D) to establish success metrics, and identifying the sequential lifecycle phases (e.g., Pre-boarding, Day 1, Week 1, 30-60-90 days) of the onboarding journey (Option B). Once the overarching framework and chronological phases are clearly defined, prompt engineers can systematically task Claude with drafting the supporting content for each specific phase. Piloting (Option A) and full rollout (Option E) occur downstream after artifact creation, while budgetary negotiations (Option C) are outside workflow decomposition.
NEW QUESTION # 66
You are a bank manager presented with a request to use Claude as the sole decision-maker on customer credit- line approvals.
Which response is most appropriate?
Answer: B
Explanation:
Credit-line approval directly affects an individual's financial eligibility and is therefore a high-risk financial decision. Claude may assist analysts by summarizing application information, checking completeness, organizing evidence, or identifying cases requiring further review, but qualified humans must retain final decision authority. Option C provides this augmentation model.
Option A values processing speed over fairness, regulatory compliance, explainability, and accountability.
Option B is unacceptable because compliance and risk controls must be established before production, not after customers are affected. Option D increases the risk by allowing Claude both to decide and communicate a consequential financial determination without professional review.
Anthropic's Usage Policy explicitly identifies loan approvals and creditworthiness determinations as high-risk financial use cases. It requires a qualified professional to review advice, recommendations, or subjective decisions that directly affect consumers before finalization. The organization remains responsible for accuracy and appropriateness. Depending on the deployment, disclosure of AI involvement may also be required.
A compliant workflow should define permitted assistance, require human approval, validate the model's outputs, monitor for disparate impact, document decision rationale, and provide appropriate appeal or correction processes. Claude should augment professional judgment, not replace the accountable decision- maker. Anthropic's high-risk use-case requirements
NEW QUESTION # 67
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