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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prompting and Task Execution | 14% | - Iterate prompts to improve output quality - Apply task decomposition techniques to structure complex requests - Create effective prompts for business and technical tasks - Adapt prompting strategy by task type |
| Topic 2: Governance, Risk, and Responsible Use | 15% | - Apply data sensitivity, regulatory, and privacy considerations - Identify appropriate and inappropriate use cases - Understand ethical implications of Claude use - Follow organizational AI policies and governance standards |
| Topic 3: Troubleshooting and Optimization | 10% | - Optimize workflows for efficiency and effectiveness - Adjust approaches based on feedback and results - Identify and diagnose issues with underperforming prompts or poor outputs |
| Topic 4: Output Evaluation and Validation | 21% | - Edit and adapt outputs for the intended audience - Identify hallucinations, inconsistencies, and biases in responses - Organize and curate information and select appropriate output formats - Evaluate Claude-generated outputs for accuracy and completeness - Apply fact-checking and validation techniques - Determine when human review or additional verification is required |
| Topic 5: Workflow Integration and Solution Design | 16% | - Analyze requirements and use cases - Use Claude for research, planning, and process optimization - Support solution design through iteration - Integrate Claude into existing workflows and communicate its value and limitations |
| Topic 6: Product and Model Selection | 12% | - Align model selection with task requirements - Select appropriate Claude product features - Manage context limitations and memory - Differentiate between Claude model types |
| Topic 7: Configuration and Knowledge Management | 12% | - Manage uploaded knowledge and connectors - Configure Claude Projects with instructions and knowledge sources - Maintain and update configurations - Create effective system-level instructions |
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NEW QUESTION # 132
You are a project manager at Orinexa, Inc. You are picking a Claude model for a new production workload and must complete the requirements steps before testing candidates.
Which two steps must be completed BEFORE testing candidate models on a representative sample? (Select two.) Each correct answer presents part of the solution.
Answer: A,E
Explanation:
Under the objective Align model selection with task requirements (e.g., cost, speed, quality) , model evaluation follows a structured engineering workflow prior to benchmark testing.
Before running comparative empirical tests on a representative sample dataset, the team must first establish evaluation criteria: defining the required output quality bar, maximum acceptable latency, throughput targets, and budget constraints (Option A). Next, the team maps those requirements against known model family characteristics (Haiku, Sonnet, Opus) to select the appropriate candidate models for testing (Option C). Once requirements are set and candidates selected, empirical sample testing can proceed. Promoting models to production (Option D), securing final platform/security rollout sign-offs (Option E), and ongoing regression testing for new model releases (Option B) are downstream deployment activities that occur after testing is completed.
NEW QUESTION # 133
A customer-success team member is reviewing a Claude-generated customer health-check report against the agreed deliverable list. The report covers all five required topics, but the renewal-risk section names only one risk factor where the deliverable specification calls for renewal risk to be assessed across pricing, product fit, and stakeholder change.
Which conclusion is best supported?
Answer: D
Explanation:
Under the objective Evaluate Claude-generated outputs for accuracy and completeness , quality assurance involves auditing both macro-level structure (presence of required topics) and micro-level depth (thoroughness against detailed specifications).
The report successfully includes all five mandatory high-level headings, satisfying basic structural completeness. However, evaluating the internal substance of the renewal-risk section reveals an under-scoped deliverable: the specification required analyzing three distinct dimensions (pricing, product fit, and stakeholder change), but Claude addressed only one. Therefore, Option D is the accurate assessment: the artifact fulfills topical scope but lacks required operational depth and must be expanded prior to client delivery. Claiming it completely satisfies requirements (Option C) ignores explicit criteria, while labeling the entire section missing (Option B) or offloading internal QA to the customer (Option A) violates quality management standards.
NEW QUESTION # 134
You are an operations assistant selecting a model for a recurring meeting-summary task that does not require deep reasoning and runs at moderate volume.
Which selection best balances cost, speed, and quality?
Answer: A
Explanation:
Under the objective Align model selection with task requirements (e.g., cost, speed, quality) , model architecture selection must be balanced against operational constraints, cognitive task complexity, and throughput requirements.
Claude 3.5 Sonnet serves as the enterprise default workhorse, engineered to deliver high-tier reasoning, comprehensive summarization capability, and excellent structural precision at a balanced cost and latency profile (Option C). For standard, recurring administrative tasks like meeting summarization at moderate operational volume, Sonnet provides the optimal equilibrium. Claude 3 Opus (Option B) is over-engineered and cost-prohibitive for routine summarization that does not demand advanced multi-step logic. Haiku (Option A) is optimized for near-instant latency and high-volume basic classification, but the characterization that it is "suitable only when the task requires the fastest turnaround" misrepresents its general utility, making Sonnet the primary choice. Training a custom base model (Option D) is completely unnecessary and outside standard enterprise workflows.
NEW QUESTION # 135
You are a Claude associate troubleshooting a Claude response that introduces facts not present in the supplied source.
Which root cause is most directly indicated?
Answer: C
Explanation:
Answer D is correct. When Claude introduces facts that are absent from the supplied source, the most directly indicated prompt defect is the absence of an explicit grounding constraint. The prompt should clearly instruct Claude to use only the supplied material, avoid relying on external or general knowledge, and state when the source does not contain enough information. It can also require supporting quotations or citations for factual claims so that unsupported additions can be detected and removed.
Option A identifies a possible contributing factor, but asking for a comprehensive response does not inherently authorize invented information. A properly constrained comprehensive response can remain fully grounded in the source. Option B is incorrect because assigning a researcher role does not necessarily produce unsupported claims; the scope and permitted sources determine the behavior. Option C is also possible in a poorly designed template, but the presence of headings alone does not explain why Claude treats unsupported content as factual.
Anthropic specifically recommends external-knowledge restrictions, direct quotations, citations, and permission to acknowledge insufficient information when reducing hallucinations. Therefore, the missing source-only constraint is the clearest root cause and should be corrected first. Anthropic: Reduce hallucinations
NEW QUESTION # 136
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: B,D
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 # 137
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