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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Output Evaluation and Validation | 21% | - Evaluate Claude-generated outputs for accuracy and completeness - Edit and adapt outputs for the intended audience - Identify hallucinations, inconsistencies, and biases in responses - Determine when human review or additional verification is required - Organize and curate information and select appropriate output formats - Apply fact-checking and validation techniques |
| Topic 2: Workflow Integration and Solution Design | 16% | - Use Claude for research, planning, and process optimization - Analyze requirements and use cases - Support solution design through iteration - Integrate Claude into existing workflows and communicate its value and limitations |
| Topic 3: Governance, Risk, and Responsible Use | 15% | - Identify appropriate and inappropriate use cases - Understand ethical implications of Claude use - Apply data sensitivity, regulatory, and privacy considerations - Follow organizational AI policies and governance standards |
| Topic 4: 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 5: Configuration and Knowledge Management | 12% | - Maintain and update configurations - Create effective system-level instructions - Manage uploaded knowledge and connectors - Configure Claude Projects with instructions and knowledge sources |
| Topic 6: 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 7: Prompting and Task Execution | 14% | - Apply task decomposition techniques to structure complex requests - Iterate prompts to improve output quality - Create effective prompts for business and technical tasks - Adapt prompting strategy by task type |
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NEW QUESTION # 94
A knowledge worker often switches between Claude and another AI service while working on a long-running project.
Which Claude feature most directly reduces repetitive context-setting between sessions?
Answer: D
Explanation:
Under the objective Select appropriate Claude product features (e.g., projects, research mode, chat, artifacts) , maintaining persistent context across extended workflows is supported by Claude's Memory features.
Claude's Memory allows users to store key preferences, ongoing project parameters, and working context that persist across independent chat sessions. Utilizing the memory import capability to transfer core project context from previous workspaces or external AI tools into Claude provides an immediate, grounded foundation for future interactions without requiring manual context re-entry in every session (Option A). In contrast, pasting static Artifacts manually into each new session (Option B) creates repetitive friction.
Utilizing Incognito mode (Option C) deliberately disables session memory, eliminating continuity. Disabling Memory and repeatedly re-pasting reference paragraphs (Option D) disregards the built-in feature designed to automate persistent context.
NEW QUESTION # 95
A finance consultant is adapting a Claude-generated narrative for a quarterly investor update. Investors have a fixed time window for the call and expect the headline result and forward outlook to be clear early.
Which adaptation is most appropriate for this audience?
Answer: A
Explanation:
Under the objective Edit, adapt, refine, and compare the output generated by Claude model(s) for the intended audience (Domain 2: Output Evaluation and Validation), adapting generated text requires restructuring content to match the executive decision-making style of the stakeholders.
Executive and investor audiences operate under strict time constraints and prioritize the bottom-line financial outcome before examining underlying mechanics (following the Minto Pyramid / Bottom-Line-Up-Front principle). Option C correctly structures the executive narrative: opening immediately with the headline financial figures, following with a concise breakdown of key operational drivers, and concluding with forward-looking guidance. Structuring the briefing chronologically (Option D) or burying core financial results in the middle of operational prose (Option A) creates cognitive friction and wastes time. Leading with the forward outlook before establishing current financial performance (Option B) lacks context.
NEW QUESTION # 96
You are a product designer using Claude in a solution-design exercise.
Which solution-design step should be performed first when using Claude?
Answer: B
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 # 97
You are a knowledge manager structuring source material in the context window for a long Claude evaluation task.
Which two practices produce a more effective use of the context window? (Select two.) Each correct answer presents a complete solution.
Answer: B,C
Explanation:
Under the objective Organize and curate information for optimal outputs and select appropriate output formats , structuring reference documents effectively within the context window ensures high-fidelity retrieval and accurate source attribution.
Option B highlights the standard prompt engineering practice of using distinct structural delimiters (such as XML tags like < doc_1 > , < policy > , or Markdown code fences). Delimiters allow Claude to distinguish cleanly between different source boundaries, instructions, and inputs, minimizing attribution errors. Option E leverages positional attention dynamics (addressing the "lost in the middle" phenomenon), where LLMs tend to recall and attend most accurately to information positioned at the very beginning (primacy effect) or at the end (recency effect) of the context window. Conversely, dumping uncurated text (Option A) adds noise and consumes quota, repeating instructions excessively (Option C) wastes tokens without improving adherence, and blending documents into an unstructured blob (Option D) obscures citation provenance.
NEW QUESTION # 98
A knowledge worker is explaining the cost, speed, and quality trade-offs across Claude models to a colleague.
Which generalization correctly describes the trade-off?
Answer: D
Explanation:
Option B is the best generalization, although the source's original phrase "consume more of the rate limit" was technically imprecise and has been corrected to "consume more resources." Anthropic's current model comparison characterizes Haiku as the fastest and lowest-cost tier, Sonnet as a balance of speed and intelligence, and Opus or higher capability tiers as appropriate for more demanding work with greater cost or latency.
Option A incorrectly assumes that higher capability automatically reduces usage consumption. Option C makes the opposite unsupported absolute: lightweight models do not always require multiple passes. Option D ignores documented differences in latency, pricing, context capacity, tool support, and workload suitability.
The generalization must not be treated as a universal per-task law. Anthropic now advises evaluating cost per completed task rather than relying only on per-token pricing. A stronger model may complete difficult work in fewer turns and can occasionally cost less overall than repeated attempts with a smaller model. Teams should therefore define quality, latency, volume, and budget requirements, then benchmark candidate models on representative workloads. Option B remains the correct broad comparison, but empirical testing determines the actual production choice. See Anthropic's model comparison , model-selection guidance , and cost-per- task guidance .
NEW QUESTION # 99
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