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| Section | Weight | Objectives |
|---|---|---|
| Output Evaluation and Validation | 21% | - Edit and adapt outputs for the intended audience - Determine when human review or additional verification is required - Organize and curate information and select appropriate output formats - Identify hallucinations, inconsistencies, and biases in responses - Apply fact-checking and validation techniques - Evaluate Claude-generated outputs for accuracy and completeness |
| Governance, Risk, and Responsible Use | 15% | - Apply data sensitivity, regulatory, and privacy considerations - Follow organizational AI policies and governance standards - Understand ethical implications of Claude use - Identify appropriate and inappropriate use cases |
| Troubleshooting and Optimization | 10% | - Adjust approaches based on feedback and results - Identify and diagnose issues with underperforming prompts or poor outputs - Optimize workflows for efficiency and effectiveness |
| Configuration and Knowledge Management | 12% | - Maintain and update configurations - Configure Claude Projects with instructions and knowledge sources - Manage uploaded knowledge and connectors - Create effective system-level instructions |
| Workflow Integration and Solution Design | 16% | - Analyze requirements and use cases - Integrate Claude into existing workflows and communicate its value and limitations - Use Claude for research, planning, and process optimization - Support solution design through iteration |
| Prompting and Task Execution | 14% | - Apply task decomposition techniques to structure complex requests - Adapt prompting strategy by task type - Create effective prompts for business and technical tasks - Iterate prompts to improve output quality |
| Product and Model Selection | 12% | - Align model selection with task requirements - Manage context limitations and memory - Differentiate between Claude model types - Select appropriate Claude product features |
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91. Frage
You are an operations lead mapping production tasks to Claude model strengths to set realistic expectations.
Which two production tasks fit Claude's strengths most clearly? (Select two.) Each correct answer presents a complete solution.
Antwort: D,E
Begründung:
Under the objective Differentiate between Claude model types (e.g., Haiku, Sonnet, Opus) and foundational AI capabilities, generative models are specifically optimized for natural language understanding, cross-document synthesis, and nuanced stylistic drafting.
Drafting audience-tailored, empathetic customer communications for human review (Option C) and synthesizing large volumes of complex source text into concise executive summaries (Option E) leverage Claude's extensive context window and linguistic fluency. Conversely, tasks requiring millisecond real-time event-driven streaming detection (Option A) or high-frequency deterministic quantitative financial settlement calculations on live feeds (Option D) are poorly suited for general LLMs and require specialized, deterministic streaming engines. Additionally, delegating final binding legal rulings entirely to an AI (Option B) represents an inappropriate governance risk.
92. Frage
You are an operations assistant asking Claude to summarize 200 weekly metric records across multiple regions.
Which output format communicates the data most clearly?
Antwort: B
Begründung:
Under the objective Organize and curate information for optimal outputs and select appropriate output formats , selecting the right presentation format is determined by data density, comparability, and target audience utility.
When dealing with a high volume of multi-variable quantitative data (such as 200 metric records across multiple regional cohorts), structured tabular formatting provides the highest visual scanability and cognitive efficiency. Organizing data into a Markdown table with uniform rows, standardized metric columns, and regional sorting (Option D) enables immediate horizontal and vertical cross-comparison, trend spot-checking, and rapid verification. In contrast, narrative prose, regional paragraph briefs, or unstructured bullet points (Options A, B, and C) obscure numerical trends, hide outliers, and create unnecessary reading overhead for operational consumers.
93. Frage
You are an operations team member preparing inputs to summarize customer feedback. The feedback file mixes public quotations with personal contact details.
Which preparation approach is most appropriate?
Antwort: A
Begründung:
Option D follows data-minimization and purpose-limitation principles. The task requires analysis of customer feedback, not access to customer contact information. The appropriate preparation process is therefore to retain the feedback statements needed for the analysis while removing names, email addresses, telephone numbers, account identifiers, and other unnecessary personal information.
Option A removes the useful content while retaining the sensitive data, directly reversing the correct handling approach. Option B exposes unnecessary personal information merely to provide more context. Option C discards the genuine feedback and invites Claude to fabricate replacement content, producing an unreliable summary. Redaction or anonymization preserves the analytical value of the input while reducing privacy exposure and limiting the consequences of unauthorized disclosure.
Anthropic advises users to be thoughtful about sharing highly sensitive personal or confidential information.
Its implementation guidance also recommends removing personally identifiable information unless it is explicitly required and authorized. Redaction does not override organizational policy: the user must still confirm that the approved Claude environment, access controls, contractual terms, retention settings, and intended purpose permit the remaining data to be processed. Anthropic's sensitive-data guidance
94. Frage
A Claude associate is identifying the bottleneck in a content workflow that takes too long to publish. The workflow has five steps: research, drafting, internal review, legal review, and publication. There is cycle-time data for each step from the past month.
Which signal most directly identifies the bottleneck?
Antwort: A
Begründung:
Option A is correct because a persistent queue or growing backlog directly indicates that work is arriving faster than a step can process it. That step is constraining the throughput of the overall workflow: earlier work accumulates while later stages wait for completed items. Queue length should still be examined over a representative period to distinguish a sustained bottleneck from a temporary spike.
Option C is useful supporting evidence but is not always the most direct signal. A step may have a long per- item cycle time because the work is inherently complex while still possessing sufficient capacity to handle incoming demand. Conversely, a relatively short step can become the bottleneck when its available capacity is too low for the volume arriving. Employee-reported workload in Option B is subjective and may reflect effort, stress, or simultaneous responsibilities rather than workflow throughput. Option D relies on an unsupported assumption: a recently added step is not necessarily the constraint.
A sound Claude-assisted optimization process should map the workflow, collect measurable evidence, identify the constraint, test a targeted improvement, and compare results against a baseline. Anthropic recommends measurable, task-relevant success criteria and evaluation methods rather than vague impressions.
Therefore, the largest sustained queue or backlog is the strongest direct indicator. See Anthropic's guidance on defining measurable success criteria and evaluations .
95. Frage
The first output from Claude is partially useful but has tone and detail issues.
Which two refinement actions are most likely to produce a meaningfully better output? (Select two.) Each correct answer presents a complete solution.
Antwort: A,B
Begründung:
Option A directly addresses the tone problem by specifying both the required voice and the audience for whom the response is being written. Audience context helps Claude determine vocabulary, technical depth, formality, and emphasis. Option B addresses ambiguity by providing a concrete example that demonstrates the desired structure, detail level, tone, and overall quality.
Option C may produce superficial variation, but it does not correct the prompt's underlying deficiencies.
Option D supplies no actionable description of what must improve. Option E increases output variability without clarifying the missing tone or detail requirements and may make results less consistent.
Prompt iteration should be diagnostic. The user should identify the specific defect, modify one or more prompt elements that control that defect, generate a new output, and compare it with the original success criteria. If the response remains too general, the next revision might add required sections, source material, minimum depth, or an evaluation rubric.
Anthropic's prompting guidance recommends clear instructions, relevant context, examples, and explicit output requirements. Examples are particularly useful when the intended standard is difficult to describe abstractly. A and B therefore introduce information Claude can act upon, while the other choices merely repeat or randomize the same insufficient instruction. Anthropic's prompt-engineering guidance
96. Frage
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