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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Troubleshooting and Optimization | 10% | - Optimizing workflows for efficiency and effectiveness - Diagnosing underperforming prompts and outputs - Adjusting approaches based on feedback and results |
| Topic 2: Prompting and Task Execution | 14% | - Adapting prompting strategies to task types - Task decomposition - Creating effective prompts - Iterating prompts to improve output |
| Topic 3: Governance, Risk, and Responsible Use | 15% | - Identifying appropriate and inappropriate AI use cases - Data sensitivity, privacy, and regulatory considerations - Ethical and responsible AI use - Organizational AI governance and policies |
| Topic 4: Product and Model Selection | 12% | - Balancing quality, speed, and cost - Selecting appropriate Claude product features - Selecting Claude models - Managing context limitations and memory |
| Topic 5: Configuration and Knowledge Management | 12% | - Configuring Claude Projects - Maintaining configuration and knowledge sources - Managing uploaded knowledge and connectors - Managing instructions and knowledge sources |
| Topic 6: Workflow Integration and Solution Design | 16% | - Research, planning, and process optimization - Communicating Claude's value and limitations - Analyzing requirements and use cases - Solution design and iteration - Integrating Claude into existing workflows |
| Topic 7: Output Evaluation and Validation | 21% | - Editing, adapting, and refining outputs - Identifying hallucinations, inconsistencies, and bias - Evaluating output accuracy and completeness - Determining when human review or verification is required - Fact-checking and validation |
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NEW QUESTION # 21
You are an HR specialist reviewing model choices made by colleagues for various tasks.
Which two model choices represent appropriate matches between the model and the task? (Select two.) Each correct answer presents a complete solution.
Answer: D,E
Explanation:
Under the objective Align model selection with task requirements (e.g., cost, speed, quality) , deploying models effectively requires matching model cognitive capacity to task complexity and throughput requirements.
Option C correctly routes complex, multi-layered tasks-such as synthesizing intricate legal precedents, statutory compliance, or multi-step policy analysis-to high-capability models (e.g., Claude 3 Opus or Sonnet) where deep reasoning is essential. Option E correctly routes simple, high-frequency, low-complexity tasks-such as standard FAQ lookups-to light, fast models (e.g., Claude 3 Haiku) to achieve ultra-low latency and minimal cost per request. Conversely, choosing models purely based on cost (Option A) is inefficient, deploying large frontier models for simple keyword extraction (Option B) wastes financial and rate-limit budgets, and assigning complex strategic synthesis across many documents to lightweight models (Option D) leads to shallow analysis and missed nuances.
NEW QUESTION # 22
An HR associate is preparing to use Claude on a sensitive personnel matter. The organization's AI policy explicitly states that information related to ongoing investigations must not be entered into AI tools.
Which response best aligns with the policy?
Answer: B
Explanation:
Under the objective Apply data, privacy sensitivity, regulatory policies, and privacy considerations , compliance with explicit organizational AI governance and data boundary mandates is mandatory.
When corporate policy strictly prohibits entering information concerning active personnel investigations into AI tools, employees must fully adhere to that boundary without attempting informal workarounds. Option B correctly enforces compliance: immediately refraining from submitting any investigation-related text to Claude and escalating to HR leadership to determine authorized, conventional processing channels.
Attempting to bypass the rule via Incognito mode (Option C) or informal paraphrasing/anonymization (Option D) violates the explicit governance mandate and risks data spillage in legally sensitive matters.
Assuming the data can simply be entered once closed (Option A) ignores enduring confidentiality requirements.
NEW QUESTION # 23
You are an analyst working on a one-off complex problem that genuinely requires multi-step reasoning over an extended chain of thought.
Which model is best suited to this work?
Answer: C
Explanation:
Among the choices provided, Opus is the appropriate model for a complex, one-off problem requiring sustained, multi-step reasoning. Opus models are designed for advanced analysis, demanding knowledge work, complex tool use, and long-horizon tasks. The additional capability is justified because the workload explicitly prioritizes reasoning quality rather than minimum latency or cost.
Option A incorrectly treats lightweight models as universally preferable. Haiku is optimized for speed and cost-sensitive, straightforward workloads, but that does not make it the best choice for every complex problem. Option B ignores genuine capability and reasoning differences between model tiers. Option D is false because the Claude lineup includes models designed for difficult reasoning tasks.
The current Claude portfolio has evolved and now includes additional high-capability models, but Opus remains the strongest valid selection among the listed answers. In production, the analyst should test representative cases and compare accuracy, latency, and cost rather than choosing solely by tier name. For this explicitly complex one-off task, however, higher reasoning capability is the dominant requirement.
Anthropic describes Opus as suited to complex analysis, enterprise work, advanced research, and deep reasoning. Anthropic's model-selection guidance
NEW QUESTION # 24
You are a Claude associate running an ethics review on a Claude-assisted deliverable before publication.
Which is the correct order of review steps?
(1) Confirm that transparency requirements are met for the audience receiving the deliverable.
(2) Document the review outcome and any changes made.
(3) Identify the audience and the potential impact of the deliverable on that audience.
(4) Make targeted revisions or escalate if a concern requires more authority to resolve.
(5) Examine the content for unsupported generalizations, fairness concerns, and accuracy issues.
Answer: B
Explanation:
Answer A follows the strongest ethics-review dependency order. First identify the audience and the potential consequences of the deliverable (3), because ethical risk depends on who receives the material and how it may affect them. Next examine the content for unsupported generalizations, fairness problems, and accuracy defects (5). With the audience and substantive risks understood, confirm that the necessary transparency or disclosure requirements are met (1). Then correct the deliverable or escalate concerns that exceed the reviewer's authority (4). Finally, record the review result and all changes made (2), preserving accountability and traceability. The resulting sequence is 3, 5, 1, 4, 2. Option B checks transparency before completing the substantive ethics assessment; Options C and D begin without first establishing the affected audience and impact. Anthropic's AI Fluency framework treats Discernment as critical assessment of AI output and Diligence as responsible, ethical use with human accountability. Anthropic's Usage Policy likewise emphasizes human review and disclosure for high-risk uses. Therefore, an ethics review should move from impact framing, through content assessment and transparency, to remediation and documentation-not merely confirm that an AI disclosure exists and assume the deliverable is safe. Anthropic AI Fluency course , Anthropic Usage Policy
NEW QUESTION # 25
You are a knowledge worker who decomposed a "draft a board memo" request into fifteen tiny steps and noticed the output now feels disjointed because related ideas were split apart.
Which adjustment is most appropriate?
Answer: D
Explanation:
Task decomposition is valuable only when the resulting stages represent meaningful, logically connected units of work. Here, dividing a board memo into fifteen extremely small steps has fragmented related ideas and weakened the document's narrative coherence. Option B corrects over-decomposition by combining naturally connected activities into broader sections, such as framing the decision, analyzing evidence, presenting risks, and stating the recommendation.
Option A removes dependency order entirely, which would make the workflow less reliable. Option C intensifies the existing problem by creating even smaller fragments. Option D goes too far in the opposite direction by discarding all useful structure. The appropriate solution is neither maximal decomposition nor a single unstructured instruction; it is a limited number of cohesive stages with clear dependencies.
Anthropic explains that explicit prompt chaining is useful when intermediate outputs must be inspected or when a specific pipeline structure is required. However, its guidance also warns against unnecessary delegation and overengineering when simpler direct work preserves context more effectively. The correct granularity should therefore improve control and reviewability without separating ideas that must be developed together. Anthropic's prompting best practices
NEW QUESTION # 26
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