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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Workflow Integration and Solution Design | 16% | - Support solution design through iteration - Analyze requirements and use cases - Integrate Claude into existing workflows and communicate its value and limitations - Use Claude for research, planning, and process optimization |
| Topic 2: Governance, Risk, and Responsible Use | 15% | - Apply data sensitivity, regulatory, and privacy considerations - Follow organizational AI policies and governance standards - Identify appropriate and inappropriate use cases - Understand ethical implications of Claude use |
| Topic 3: Product and Model Selection | 12% | - Manage context limitations and memory - Select appropriate Claude product features - Differentiate between Claude model types - Align model selection with task requirements |
| Topic 4: Output Evaluation and Validation | 21% | - Determine when human review or additional verification is required - Apply fact-checking and validation techniques - Organize and curate information and select appropriate output formats - Evaluate Claude-generated outputs for accuracy and completeness - Edit and adapt outputs for the intended audience - Identify hallucinations, inconsistencies, and biases in responses |
| Topic 5: Configuration and Knowledge Management | 12% | - Create effective system-level instructions - Configure Claude Projects with instructions and knowledge sources - Maintain and update configurations - Manage uploaded knowledge and connectors |
| Topic 6: 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 7: 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 |
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NEW QUESTION # 30
A strategy manager is reviewing a Claude-generated recommendation to consolidate the company's two regional warehouses into one. The recommendation cites three sources that estimate cost savings and one source confirming that consolidation is operationally feasible. Two source documents also contain estimates of one-time transition costs and a stakeholder concern about regional service levels, neither of which appears in the recommendation.
Which concern is most warranted?
Answer: A
Explanation:
Option D is correct because the recommendation is incomplete and directionally biased. Although its cited evidence may genuinely support consolidation, the omitted transition costs and service-level concern are material to the decision. Accuracy is not limited to whether included claims are supported; it also requires completeness, balanced use of the available evidence, and faithful treatment of factors that could alter the conclusion.
The recommendation should be re-prompted to incorporate the omitted evidence, quantify its effect where possible, explain unresolved uncertainty, and present counterarguments or risks alongside the projected savings. The revised output should then be checked against every relevant source. Anthropic recommends multidimensional evaluations aligned with the application's purpose, including task fidelity, relevance, context utilization, and other task-specific criteria. Its hallucination guidance also recommends citations and verification of claims against supporting source text.
Option A incorrectly treats material evidence as optional appendix content when it belongs in the core analysis. Option B invents an arbitrary source-count threshold; evidence quality and coverage matter more than a fixed number. Option C considers only confirmatory citations and ignores contradictory or qualifying information. Therefore, D identifies the central problem: selective evidence usage that could mislead stakeholders. See Anthropic's guidance on evaluating outputs and verifying claims with citations .
NEW QUESTION # 31
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: C,E
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 # 32
A knowledge worker has asked Claude to produce a one-page operating procedure. The team will edit the procedure, reuse it across multiple onboarding cycles, and update it when the underlying process changes.
Which output format is most appropriate?
Answer: B
Explanation:
Answer D is correct. An operating procedure is substantial, self-contained content that the team intends to edit, iterate on, reuse, and reference later. Those characteristics closely match the intended use of Claude Artifacts. An Artifact places the procedure in a dedicated workspace separate from the conversational exchange, allowing users to review the content as a standalone document, request targeted revisions, edit supported content directly, and move between versions.
Option A can work, but repeatedly copying revised text from chat into another location creates avoidable manual effort and increases the risk of saving an outdated or incomplete version. Option B is inappropriate because the entire transcript contains conversational material that is not part of the operating procedure and makes the authoritative version harder to identify. Option C keeps the procedure embedded in a chat, which is less suitable for a durable operational document that must be maintained across onboarding cycles.
Anthropic identifies documents as a common Artifact type and states that Artifacts are appropriate for content users are likely to edit, build upon, reuse outside the conversation, or reference later. Therefore, an Artifact provides the clearest and most maintainable working format among the available choices. Anthropic: What are Artifacts and how do I use them?
NEW QUESTION # 33
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: A
Explanation:
Among the models presented in the answer choices, Opus is the best fit for a one-off, highly complex problem requiring sustained multi-step reasoning. Opus models are intended for demanding knowledge work and advanced reasoning where output quality is more important than obtaining the lowest latency or cost.
Anthropic's model guidance distinguishes higher-capability Opus models from balanced Sonnet models and faster, more economical Haiku models. Claude Platform Docs Option A incorrectly prioritizes speed and cost even though the task explicitly requires complex reasoning.
Haiku is more appropriate for high-volume, latency-sensitive, or comparatively straightforward tasks. Option B identifies a genuine advantage of Sonnet-its balance of capability, speed, and cost-but the task is described as sufficiently difficult to justify choosing the stronger reasoning option. Option D is incorrect because model performance does not remain identical as task complexity increases.
Model selection should follow the actual requirements of the work. Important considerations include reasoning depth, accuracy requirements, latency, expected usage volume, and cost. A routine, repeatable classification workflow might favor Haiku, while many everyday professional tasks fit Sonnet. A difficult specialized problem where reasoning quality is the primary concern supports selecting Opus from the available choices.
NEW QUESTION # 34
A manager is deciding what changes to make while conducting a Memory maintenance review for a long- running workflow.
Which two actions best support Memory quality over time? (Select two.)
Answer: C,E
Explanation:
Option B creates a recoverable checkpoint before maintenance. Exporting the current Memory allows the manager to restore important context if a necessary entry is accidentally removed or rewritten incorrectly.
Option E performs the actual quality maintenance by removing stale or inaccurate information and adding durable context that the workflow still requires.
Deleting everything, as proposed in Option A, discards valid context and forces the system to reconstruct it imperfectly from later activity. Option C assumes that the absence of a reported problem proves every stored entry remains accurate; stale Memory may affect future tasks even when current outputs appear acceptable.
Option D eliminates useful continuity instead of correcting the specific quality problems.
Anthropic allows users to view, edit, and export Memory. Its documentation states that exported Memory can serve as a backup or be transferred to another service. Users can also add information that Claude has not automatically retained. A responsible maintenance cycle should therefore back up the current state, inspect entries for accuracy and relevance, remove obsolete details, add missing durable context, and verify future outputs. This preserves continuity while reducing the risk that outdated assumptions contaminate later sessions. Anthropic's Memory import and export guidance
NEW QUESTION # 35
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