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| Section | Weight | Objectives |
|---|---|---|
| Prompting and Task Execution | 14% | - Create effective prompts for business and technical tasks - Iterate prompts to improve output quality - Apply task decomposition to structure complex requests - Adapt prompting strategies by task type |
| Governance, Risk, and Responsible Use | 15% | - Understand limitations and ethical boundaries - Manage data sensitivity and privacy risks - Apply responsible AI principles and policies |
| Product and Model Selection | 12% | - Understand model differences and use cases - Select appropriate Claude product features - Match model capabilities to task requirements |
| Configuration and Knowledge Management | 12% | - Maintain consistent system-level behavior - Manage knowledge sources and context - Configure Claude Projects with instructions |
| Output Evaluation and Validation | 21% | - Evaluate outputs for accuracy, completeness, and consistency - Apply fact-checking and validation methods - Detect hallucinations, biases, and inconsistencies - Determine when human review is required |
| Troubleshooting and Optimization | 10% | - Identify causes of poor or unexpected outputs - Adjust prompts and configuration to improve results - Diagnose context and performance issues |
| Workflow Integration and Solution Design | 16% | - Integrate Claude into existing business workflows - Plan end-to-end Claude-augmented workflows - Design AI-assisted processes and solutions |
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NEW QUESTION # 23
You are a product designer using Claude in a solution-design exercise.
Which solution-design step should be performed first when using Claude?
Answer: A
Explanation:
Option C is correct because effective solution design begins by defining the problem, desired outcome, success criteria, and relevant constraints. Without this foundation, Claude may generate polished concepts that solve the wrong problem or violate important business, technical, regulatory, budgetary, or audience requirements. Anthropic's AI Fluency Delegation guidance calls this "Problem Awareness": understanding the goal and the work required before deciding how to involve AI. Its applied Delegation process likewise begins with establishing the project vision and defining what a successful result looks like before breaking the work into tasks.
After the problem and constraints are clear, Claude can productively generate diverse solution concepts, making Option A a later ideation step. Option B follows concept generation because alternatives cannot be compared until they exist and the evaluation constraints have been established. Option D comes later still, after a candidate has been evaluated and selected. Starting with mock-ups would prematurely commit effort to an unvalidated solution. Therefore, articulating the problem and constraints first creates the grounding Claude needs for relevant ideation, systematic comparison, and defensible design decisions. This sequence is consistent with Anthropic's AI Fluency Delegation guidance and Applying Delegation lesson .
NEW QUESTION # 24
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: C
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 # 25
You are designing a checklist for evaluating Claude outputs and must complete the upstream steps before scoring an output.
Which two steps must be completed BEFORE scoring a Claude output against the checklist? (Select two.) Each correct answer presents part of the solution.
Answer: B,D
Explanation:
An evaluation cannot be applied consistently until the organization defines what the output must achieve and what score constitutes an acceptable result. Therefore, the evaluator must first define task-specific criteria, such as factual accuracy, completeness, relevance, tone, format compliance, privacy preservation, or citation quality. The evaluator must then establish a threshold or decision rule that separates acceptable from unacceptable performance.
Anthropic's evaluation guidance states that successful AI applications begin with clearly defined success criteria followed by evaluations that measure performance against those criteria. Effective criteria should be specific, measurable, achievable, and relevant to the application's purpose. A threshold converts those criteria into an operational decision-for example, requiring all material claims to be supported or setting a minimum score across defined dimensions.
Options A, D, and E occur after evaluation. Promotion into production follows an acceptable result.
Escalation to a senior reviewer occurs after an output fails or presents elevated risk. Recording the final score and reviewer comments also follows the scoring activity. Without B and C, different reviewers could apply inconsistent standards or approve an output without knowing what "acceptable" means. Anthropic's evaluation guidance
NEW QUESTION # 26
You are an HR specialist sorting potential ethical risks of a Claude-supported workflow.
Which two risks pose the highest ethical concern in an HR-adjacent workflow? (Select two.) Each correct answer presents a complete solution.
Answer: A,D
NEW QUESTION # 27
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 # 28
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