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| Section | Weight | Objectives |
|---|---|---|
| Configuration and Knowledge Management | 12% | - Managing instructions and knowledge sources - Configuring Claude Projects - Managing uploaded knowledge and connectors - Maintaining configuration and knowledge sources |
| Troubleshooting and Optimization | 10% | - Diagnosing underperforming prompts and outputs - Adjusting approaches based on feedback and results - Optimizing workflows for efficiency and effectiveness |
| Product and Model Selection | 12% | - Selecting Claude models - Selecting appropriate Claude product features - Balancing quality, speed, and cost - Managing context limitations and memory |
| Workflow Integration and Solution Design | 16% | - Analyzing requirements and use cases - Research, planning, and process optimization - Communicating Claude's value and limitations - Solution design and iteration - Integrating Claude into existing workflows |
| Output Evaluation and Validation | 21% | - Editing, adapting, and refining outputs - Identifying hallucinations, inconsistencies, and bias - Determining when human review or verification is required - Fact-checking and validation - Evaluating output accuracy and completeness |
| Governance, Risk, and Responsible Use | 15% | - Data sensitivity, privacy, and regulatory considerations - Identifying appropriate and inappropriate AI use cases - Organizational AI governance and policies - Ethical and responsible AI use |
| Prompting and Task Execution | 14% | - Adapting prompting strategies to task types - Task decomposition - Iterating prompts to improve output - Creating effective prompts |
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78. Frage
A knowledge worker is adjusting an approach after a successful pilot revealed two opportunities for further improvement. The pilot's core results were positive, and the team is preparing for a broader rollout.
Which adjustment is most appropriate?
Antwort: B
Begründung:
Answer B is correct. The pilot has already demonstrated that the core approach is effective, so the team should retain the successful elements while making targeted changes for the two identified improvement opportunities. A controlled comparison before full rollout then provides evidence that the revised approach performs at least as well as the original pilot and that the modifications produce the expected improvement.
Option A ignores known improvement opportunities and unnecessarily carries identified weaknesses into production. Option C implements the changes during the broader rollout without validating them first.
Monitoring is necessary, but it is not a substitute for controlled pre-rollout testing because defects could affect many more users. Option D tests only the two opportunities in isolation. This may fail to evaluate how the new changes interact with the rest of the successful workflow.
A controlled comparison can evaluate the pilot configuration and the revised configuration against the same acceptance criteria, representative cases, and quality measures. This preserves what worked while reducing the risk of unintended regression. Anthropic's evaluation guidance recommends measurable success criteria, task-specific tests, and evaluations that distinguish between competing approaches. Therefore, B represents the strongest evidence-based adjustment before scaling the workflow. Anthropic: Define success criteria and build evaluations
79. Frage
A customer-success associate is drafting a prompt that asks Claude to summarize a customer issue from the case file. The case file includes the customer ' s full government identifier, home address, name, account number, and the description of the issue. The summary the associate needs is about the issue.
Which approach is most appropriate?
Antwort: B
Begründung:
Under the objective Apply data, privacy sensitivity, regulatory policies, and privacy considerations (Domain
6: Governance, Risk, and Responsible Use), data minimization is a primary privacy requirement.
When processing enterprise documents with AI tools, users must never submit highly sensitive Personally Identifiable Information (PII)-such as national government identifiers (SSNs/National Insurance numbers) or private home addresses-unless strictly required for the analytical task. Because the associate only needs a summary of the core customer problem, redacting or removing those non-essential high-sensitivity fields prior to input ingestion (Option C) enforces sound privacy hygiene. Relying on in-prompt instructions to " ignore " ingested PII (Option A) still exposes raw sensitive tokens to the model context. Swapping identifiers for other sensitive account data (Option B) fails data protection standards.
80. Frage
A learning specialist is configuring a Project for course-design work. Their organization has an internal instructional-design standard and a preferred output structure for lesson outlines.
Which custom-instruction set best supports this use case?
Antwort: A
Begründung:
Option A is correct because effective Project instructions should establish Claude's role, the authoritative standards governing the work, and the expected output structure. These elements create consistent behaviour across the Project's course-design chats. The role establishes the professional perspective Claude should adopt, the instructional-design standard defines the governing methodology, and the lesson-outline structure specifies the required deliverable.
Anthropic explains that Project instructions tell Claude how to behave and respond across every chat within the Project. Consequently, recurring requirements should be placed in Project instructions instead of being manually repeated in each conversation. The organization's instructional-design documentation should also be uploaded to Project knowledge so Claude can reference the actual standard rather than infer it from a brief instruction.
Option B omits a known structural requirement and permits unnecessary variation. Option C supplies the format but not the governing methodology or role context; structure alone cannot communicate the complete instructional standard. Option D defeats the persistence benefit of Projects and increases inconsistency between users and chats.
Individual prompts can still specify lesson-specific objectives, audiences, subjects, and constraints, but the stable role, standards, and output schema belong in the Project-level instructions. See Anthropic's documentation on Project instructions and knowledge and prompting best practices .
Only six genuinely unique questions were found. This is the complete Set 3 batch.
81. Frage
A communications manager is drafting a prompt for Claude to produce a quarterly newsletter for store managers in three regions. The agreed key messages, regional details, and a prior newsletter the team liked are all available.
Which prompt approach is most likely to produce a usable first draft?
Antwort: D
Begründung:
Under the objective Create effective prompts for business and technical tasks , Anthropic's core prompt engineering principle emphasizes being explicit, direct, and providing complete structural constraints rather than relying on implicit document matching.
Option D establishes all essential prompt components directly within the prompt itself: explicitly defining the target audience (store managers), geographic scope (three regions), length constraints, tone guidelines, and the core messaging points, while treating the historical newsletter as a calibrated few-shot tone reference. This explicit framing eliminates ambiguity and prevents the model from copying obsolete structure or irrelevant sections from the historical document. In contrast, Option A relies excessively on broad pattern-matching against an attachment without providing explicit parameter boundaries in the prompt body. Option B lacks structural and tonal detail, while Option C delegates tone and length decisions entirely to the model.
82. Frage
You are evaluating a Claude output that appears to contain hallucinated content.
Which response step should be performed first when a hallucination is suspected?
Antwort: D
Begründung:
Under the objective Identify hallucinations, inconsistencies, and biases in responses , diagnosing potential generative errors requires structured evidence validation before taking corrective action.
The first diagnostic step is claim isolation: carefully examining the output to pinpoint the exact statement, metric, or entity that appears unsupported by the provided source context or verified reality (Option D).
Pinpointing the specific defect allows the reviewer to compare it directly against the source data to confirm whether it is an actual hallucination, an ungrounded inference, or a valid synthesis. Discarding the entire output without identifying the error (Option A) prevents root-cause analysis and wastes working iterations.
Logging risks (Option B) or broadcasting notifications to users (Option C) are secondary actions that occur only after the defect has been isolated and confirmed.
83. Frage
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