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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Troubleshooting and Optimization | 10% | - Adjust prompts and configuration to improve results - Identify causes of poor or unexpected outputs - Diagnose context and performance issues |
| Topic 2: Governance, Risk, and Responsible Use | 15% | - Apply responsible AI principles and policies - Understand limitations and ethical boundaries - Manage data sensitivity and privacy risks |
| Topic 3: Workflow Integration and Solution Design | 16% | - Integrate Claude into existing business workflows - Plan end-to-end Claude-augmented workflows - Design AI-assisted processes and solutions |
| Topic 4: Configuration and Knowledge Management | 12% | - Maintain consistent system-level behavior - Manage knowledge sources and context - Configure Claude Projects with instructions |
| Topic 5: Output Evaluation and Validation | 21% | - Evaluate outputs for accuracy, completeness, and consistency - Detect hallucinations, biases, and inconsistencies - Apply fact-checking and validation methods - Determine when human review is required |
| Topic 6: Prompting and Task Execution | 14% | - Iterate prompts to improve output quality - Apply task decomposition to structure complex requests - Create effective prompts for business and technical tasks - Adapt prompting strategies by task type |
| Topic 7: Product and Model Selection | 12% | - Select appropriate Claude product features - Understand model differences and use cases - Match model capabilities to task requirements |
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NEW QUESTION # 48
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: A,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 # 49
You are validating evaluation criteria proposed for a Claude-generated business brief.
Which three criteria are valid for evaluating a Claude-generated brief? (Select three.) Each correct answer presents part of the solution.
Answer: A,C,E
Explanation:
Options A, B, and C are correct. The source document incorrectly instructed the reader to select two and omitted Option B, even though Anthropic explicitly recognizes tone and style as valid evaluation dimensions.
The question has therefore been corrected to "Select three."
Factual accuracy measures whether claims faithfully reflect the supplied sources. Style consistency evaluates whether the brief follows the organization's established voice, terminology, and audience expectations.
Format and length compliance determine whether Claude followed explicit deliverable constraints. Together, these criteria cover factual fidelity, audience suitability, and instruction adherence.
Option D is not a sound criterion because an external industry word-count benchmark may have no relationship to the actual business need. The relevant measure is compliance with the requested length and sufficient coverage of the required material. Option E is also invalid as written: an executive summary is required only if the task specification, template, or audience needs call for one. Automatically demanding it would evaluate an unstated requirement.
Anthropic's evaluation documentation identifies task fidelity, relevance, tone and style, context utilization, and other task-specific success criteria as valid dimensions. It also stresses that criteria should be specific, measurable, achievable, and relevant. See Anthropic's official evaluation guidance .
NEW QUESTION # 50
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?
Answer: A
Explanation:
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.
NEW QUESTION # 51
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?
Answer: D
Explanation:
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.
NEW QUESTION # 52
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.
Answer: A,B
Explanation:
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.
NEW QUESTION # 53
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