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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prompting and Task Execution | 14% | - Iterating prompts to improve output - Task decomposition - Creating effective prompts - Adapting prompting strategies to task types |
| Topic 2: Configuration and Knowledge Management | 12% | - Maintaining configuration and knowledge sources - Managing instructions and knowledge sources - Configuring Claude Projects - Managing uploaded knowledge and connectors |
| Topic 3: Troubleshooting and Optimization | 10% | - Diagnosing underperforming prompts and outputs - Optimizing workflows for efficiency and effectiveness - Adjusting approaches based on feedback and results |
| Topic 4: Output Evaluation and Validation | 21% | - Editing, adapting, and refining outputs - Evaluating output accuracy and completeness - Fact-checking and validation - Identifying hallucinations, inconsistencies, and bias - Determining when human review or verification is required |
| Topic 5: Governance, Risk, and Responsible Use | 15% | - Data sensitivity, privacy, and regulatory considerations - Identifying appropriate and inappropriate AI use cases - Ethical and responsible AI use - Organizational AI governance and policies |
| Topic 6: Workflow Integration and Solution Design | 16% | - Integrating Claude into existing workflows - Communicating Claude's value and limitations - Solution design and iteration - Analyzing requirements and use cases - Research, planning, and process optimization |
| Topic 7: Product and Model Selection | 12% | - Selecting Claude models - Managing context limitations and memory - Balancing quality, speed, and cost - Selecting appropriate Claude product features |
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NEW QUESTION # 82
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 # 83
The first output from Claude is partially useful but has tone and detail issues.
Which two refinement actions are most likely to produce a meaningfully better output? (Select two.) Each correct answer presents a complete solution.
Answer: D,E
Explanation:
Option A directly addresses the tone problem by specifying both the required voice and the audience for whom the response is being written. Audience context helps Claude determine vocabulary, technical depth, formality, and emphasis. Option B addresses ambiguity by providing a concrete example that demonstrates the desired structure, detail level, tone, and overall quality.
Option C may produce superficial variation, but it does not correct the prompt's underlying deficiencies.
Option D supplies no actionable description of what must improve. Option E increases output variability without clarifying the missing tone or detail requirements and may make results less consistent.
Prompt iteration should be diagnostic. The user should identify the specific defect, modify one or more prompt elements that control that defect, generate a new output, and compare it with the original success criteria. If the response remains too general, the next revision might add required sections, source material, minimum depth, or an evaluation rubric.
Anthropic's prompting guidance recommends clear instructions, relevant context, examples, and explicit output requirements. Examples are particularly useful when the intended standard is difficult to describe abstractly. A and B therefore introduce information Claude can act upon, while the other choices merely repeat or randomize the same insufficient instruction. Anthropic's prompt-engineering guidance
NEW QUESTION # 84
You are a customer-success lead running an interactive workflow where each turn must respond quickly and the per-turn reasoning is light.
Which selection best aligns with the requirements?
Answer: C
Explanation:
Option A correctly matches the model tier to the workload. An interactive, high-volume workflow with light reasoning places greater importance on response speed, operational efficiency, and cost than on maximum reasoning depth. Haiku is positioned as the fast, lightweight Claude model for responsive tasks, making it the appropriate starting choice.
Option B uses a criterion unrelated to performance. Option C is incorrect because model tiers differ in capability, latency, and cost characteristics. Option D prioritizes advanced reasoning even though the scenario explicitly states that reasoning demands are light and rapid responses are required. Selecting a more capable model than the task needs can increase latency and expense without producing a meaningful business benefit.
Model selection should still be validated empirically. The team should define an acceptable quality threshold, test representative customer-success requests, measure response latency and task accuracy, and confirm that Haiku meets the required service level. If the real workload contains complex exceptions, the workflow could route only those cases to a more capable model. Anthropic's current learning materials characterize Haiku as its fastest option for lightweight work, while its prompt-engineering guidance identifies model selection as a direct lever for improving cost and latency. Anthropic's Claude learning resources
NEW QUESTION # 85
You are evaluating a Claude output that appears to contain hallucinated content.
Which response step should be performed first when a hallucination is suspected?
Answer: B
Explanation:
Under the objective Identify hallucinations, inconsistencies, and biases in responses , diagnosing potential generative errors requires structured evidence validation before taking corrective action.
The first diagnostic step is claim isolation: carefully examining the output to pinpoint the exact statement, metric, or entity that appears unsupported by the provided source context or verified reality (Option D).
Pinpointing the specific defect allows the reviewer to compare it directly against the source data to confirm whether it is an actual hallucination, an ungrounded inference, or a valid synthesis. Discarding the entire output without identifying the error (Option A) prevents root-cause analysis and wastes working iterations.
Logging risks (Option B) or broadcasting notifications to users (Option C) are secondary actions that occur only after the defect has been isolated and confirmed.
NEW QUESTION # 86
You are evaluating proposed workflow changes against whether they meaningfully improve the workflow.
Which two proposed changes meaningfully improve a recurring Claude workflow? (Select two.) Each correct answer presents a complete solution.
Answer: C,E
Explanation:
Under the objective Optimize workflows for efficiency and effectiveness , meaningful operational improvements must deliver measurable gains in throughput, token efficiency, or output reliability.
Option C provides major efficiency gains by moving recurring reference documentation into a persistent Claude Project knowledge base, eliminating redundant token overhead and manual copy-pasting across dozens of daily chats. Option E provides vital effectiveness and risk mitigation by establishing a standardized human-in-the-loop quality gate, preventing defective or hallucinated outputs from propagating into downstream operational pipelines. In contrast, renaming workspaces (Option A), re-labeling steps for cosmetic readability (Option B), or shuffling documentation headings (Option D) represent superficial administrative edits that do not improve execution speed, cost, or output quality.
NEW QUESTION # 87
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