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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Governance, Risk, and Responsible Use | 15% | - Follow organizational AI policies and governance standards - Identify appropriate and inappropriate use cases - Understand ethical implications of Claude use - Apply data sensitivity, regulatory, and privacy considerations |
| Topic 2: Prompting and Task Execution | 14% | - Apply task decomposition techniques to structure complex requests - Iterate prompts to improve output quality - Adapt prompting strategy by task type - Create effective prompts for business and technical tasks |
| 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% | - Support solution design through iteration - Integrate Claude into existing workflows and communicate its value and limitations - Use Claude for research, planning, and process optimization - Analyze requirements and use cases |
| Topic 5: Product and Model Selection | 12% | - Differentiate between Claude model types - Manage context limitations and memory - Select appropriate Claude product features - Align model selection with task requirements |
| Topic 6: Configuration and Knowledge Management | 12% | - Maintain and update configurations - Configure Claude Projects with instructions and knowledge sources - Create effective system-level instructions - Manage uploaded knowledge and connectors |
| Topic 7: Output Evaluation and Validation | 21% | - Organize and curate information and select appropriate output formats - Apply fact-checking and validation techniques - Edit and adapt outputs for the intended audience - Evaluate Claude-generated outputs for accuracy and completeness - Identify hallucinations, inconsistencies, and biases in responses - Determine when human review or additional verification is required |
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NEW QUESTION # 14
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: C,E
Explanation:
Under the objective Understand ethical implications of AI usage , evaluating HR workflows requires identifying systemic harms, legal compliance liabilities, and threats to employee rights.
Option A highlights the ethical danger of autonomous algorithmic decision-making: delegating high-stakes determinations (such as termination, performance ratings, or compensation) to an AI system without accountable human oversight violates fundamental fairness and labor ethics. Option E represents severe algorithmic bias and discrimination: generating outputs that systematically disadvantage candidates or employees based on protected characteristics (e.g., race, gender, age, disability). Both scenarios represent critical ethical risks. Conversely, minor clerical typos in calendar invites (Option B), formatting irregularities in general announcements (Option C), and punctuation style inconsistencies (Option D) are minor operational defects that carry zero ethical or regulatory consequences.
NEW QUESTION # 15
You are a knowledge worker reviewing a Claude-generated brief that asserts a "well-known industry standard" without naming any source.
How should this assertion be handled?
Answer: D
Explanation:
The phrase "well-known industry standard" is not evidence. Without the name of the standard, its issuing body, version, publication date, or an authoritative citation, the statement cannot be independently verified.
Option D correctly treats the assertion as unsupported and requires either authoritative substantiation or removal.
Claude can produce fluent, confident language that appears credible even when the underlying claim is incomplete, inaccurate, or fabricated. Adding stronger language, as proposed in Option A, would amplify the unsupported claim rather than correct it. Options B and C confuse plausibility and confidence with verification. A claim does not become reliable merely because it sounds familiar or uses reassuring terminology.
The reviewer should identify the purported standard, locate the original issuing organization or official publication, confirm that the standard applies to the subject and jurisdiction, and cite the relevant section. If those checks cannot be completed, the claim should be deleted or rewritten to disclose uncertainty. Anthropic recommends grounding factual statements in direct source material, using citations to make claims auditable, and retracting claims for which supporting evidence cannot be found. This approach directly addresses hallucination risk while preserving the brief's credibility. Anthropic's hallucination-reduction guidance
NEW QUESTION # 16
When refining a Claude output that does not meet expectations, which iterative refinement step should be performed first?
Answer: B
Explanation:
Answer D is correct. Iterative refinement should begin by diagnosing the current output against the original requirements. The reviewer must identify specific weaknesses such as missing information, unsupported claims, incorrect tone, poor organization, excessive length, or failure to follow the requested format. This diagnosis provides evidence for deciding what must change in the prompt.
After identifying the weaknesses, the reviewer can modify the prompt to address them, which corresponds to Option C. The revised prompt is then submitted and the resulting output captured, as described in Option A.
Finally, the new output is compared with the original criteria to determine whether the revision improved performance, as stated in Option B. The logical sequence is therefore D, C, A, and B.
Beginning with prompt modification before diagnosing the failure encourages random changes and makes it difficult to determine which revision produced an improvement. Similarly, a new output cannot be submitted or evaluated before a targeted revision has been created. Anthropic's guidance recommends defining clear success criteria, evaluating results against those criteria, and using a structured self-correction process:
generate a draft, review it, and then refine it based on identified deficiencies. Effective iteration is therefore diagnostic and evidence-based rather than trial and error. Anthropic prompting best practices
NEW QUESTION # 17
You are an operations lead sorting proposed Claude use cases according to whether they are acceptable for your team to pursue.
Which two proposed use cases are acceptable for the team to pursue with Claude? (Select two.) Each correct answer presents a complete solution.
Answer: B,C
Explanation:
Answers B and C are the two acceptable use cases as written. Drafting an internal training summary from approved documentation is a bounded synthesis task grounded in authorized sources. Synthesizing themes from approved customer feedback under established aggregation rules is likewise a controlled analysis task, provided the data handling follows organizational privacy requirements. Both activities support human work without assigning Claude a consequential decision about an individual. Option D feeds performance-review information into a promotion process, an employment-related high-risk domain requiring formal controls and accountable human judgment. Options A and E involve healthcare and legal work. Anthropic's current Usage Policy does not categorically ban those domains, but it requires qualified professional review for high-risk advice or decisions and requires disclosure when outputs are presented directly to consumers. The brief wording in A and E mentions professional review but does not establish the complete safeguards, authorization, privacy handling, or disclosure needed to treat the workflow as approved. Therefore, they are not unconditionally acceptable "as stated." Anthropic's AI Fluency Delegation principle also requires considering consequences, reversibility, and the human capabilities that must remain in the process. B and C are the only choices that fully specify low-risk, policy-bounded knowledge work without relying on unstated safeguards. Anthropic Usage Policy
NEW QUESTION # 18
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: B
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 # 19
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