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| Section | Weight | Objectives |
|---|---|---|
| Prompting and Task Execution | 14% | - Adapt prompting strategy by task type - Iterate prompts to improve output quality - Apply task decomposition techniques to structure complex requests - Create effective prompts for business and technical tasks |
| Troubleshooting and Optimization | 10% | - Optimize workflows for efficiency and effectiveness - Identify and diagnose issues with underperforming prompts or poor outputs - Adjust approaches based on feedback and results |
| Configuration and Knowledge Management | 12% | - Configure Claude Projects with instructions and knowledge sources - Maintain and update configurations - Create effective system-level instructions - Manage uploaded knowledge and connectors |
| Workflow Integration and Solution Design | 16% | - Use Claude for research, planning, and process optimization - Analyze requirements and use cases - Integrate Claude into existing workflows and communicate its value and limitations - Support solution design through iteration |
| Product and Model Selection | 12% | - Select appropriate Claude product features - Differentiate between Claude model types - Align model selection with task requirements - Manage context limitations and memory |
| Governance, Risk, and Responsible Use | 15% | - Apply data sensitivity, regulatory, and privacy considerations - Understand ethical implications of Claude use - Identify appropriate and inappropriate use cases - Follow organizational AI policies and governance standards |
| Output Evaluation and Validation | 21% | - Evaluate Claude-generated outputs for accuracy and completeness - Identify hallucinations, inconsistencies, and biases in responses - Edit and adapt outputs for the intended audience - Determine when human review or additional verification is required - Apply fact-checking and validation techniques - Organize and curate information and select appropriate output formats |
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NEW QUESTION # 13
You are a business analyst deciding which output-formatting and context-window practices to recommend to teammates.
Which two practices represent effective use of the context window? (Select two.) Each correct answer presents part of the solution.
Answer: A,C
Explanation:
Answers D and E are correct. Trimming irrelevant material keeps the context concentrated on information that can influence the requested result. This reduces distraction, avoids wasting context capacity, and lowers the chance that stale or unrelated content will compete with authoritative sources. Clear section headers separate instructions, source material, examples, and task inputs, helping Claude distinguish their functions and attribute claims correctly. Anthropic's prompting guidance recommends structured long-context inputs and descriptive XML tags for exactly this reason: explicit boundaries reduce ambiguity when a prompt contains multiple content types or documents. Option A is unsafe as a general practice because reusing an unchanged context block can carry obsolete facts or irrelevant constraints into a new request. Option B indiscriminately includes complete email threads, creating noise and potentially unnecessary personal or confidential data.
Option C removes all source boundaries, making attribution and instruction following harder. Although Anthropic also recommends placing long-form documents before the query in large-context prompts, placement cannot compensate for poor curation or missing structure. Effective context management therefore has two complementary parts: select only relevant material and organize that material into clearly labelled sections. D performs the curation step, while E supplies the structure needed for reliable interpretation.
Anthropic long-context prompting guidance
NEW QUESTION # 14
A finance manager is about to discuss a confidential pre-announcement figure with Claude.
Which two settings best align with sound privacy practice for this conversation? (Select two.) Each correct answer presents a complete solution.
Answer: C,D
Explanation:
Under the objective Apply data, privacy sensitivity, regulatory policies, and privacy considerations , managing confidential or market-sensitive figures requires controlling memory persistence and data retention settings.
Pre-announcement financial figures represent material non-public information (MNPI) that must not persist beyond the immediate analytical session. Option E applies privacy controls by utilizing Incognito mode, which ensures the session does not contribute to long-term memory or persist in chat history. Option B achieves the same data protection control in standard chat by explicitly toggling off Memory contributions, ensuring the sensitive number is not saved into persistent memory where it could leak into future unrelated prompts. Conversely, proceeding with default settings (Option A), assuming brief discussions avoid retention (Option C), or enabling persistent memory retention for confidential figures (Option D) creates serious compliance and insider data exposure risks.
NEW QUESTION # 15
You are a communications manager validating a Claude-generated newsletter that includes a specific industry growth figure. The figure appears in three trade-press articles, but none of them link to the underlying study.
Which validation step is correct?
Answer: B
Explanation:
Under the objective Apply fact-checking and validation techniques (Domain 2: Output Evaluation and Validation), rigorous verification requires tracing claims back to primary authoritative sources rather than accepting secondary repetition.
Trade publications frequently syndicate or republish unverified statistics in an echo chamber. Under Claude verification standards, the presence of an unlinked claim across multiple secondary articles does not constitute factual validation. The only rigorous verification method is primary source attribution: locating the original study, dataset, or methodology paper to confirm the exact numeric metric and context (Option C). Accepting claims merely due to repetition (Option A) risks propagating circular reporting. Presenting unverified figures as common knowledge (Option B) or arbitrarily cherry-picking high figures (Option D) violates communication ethics and data integrity standards.
NEW QUESTION # 16
You are an operations lead and notice that a Project's outputs have been drifting from the team's current expectations.
Which maintenance action should be tried first?
Answer: A
Explanation:
Project output drift commonly occurs when persistent instructions, reference documents, terminology, policies, or expected formats no longer reflect the team's current operating standard. Option C addresses the most direct and controllable cause by reviewing the Project's instructions and knowledge sources before replacing the Project or changing models.
Anthropic explains that Project instructions define how Claude should behave and respond across chats in that Project. Documents uploaded to Project knowledge provide persistent context that Claude uses throughout those chats. If either component contains obsolete requirements, conflicting guidance, or superseded source material, Claude can consistently produce results that no longer meet the team's expectations. The appropriate maintenance response is to compare instructions and knowledge against current standards, remove obsolete files, resolve contradictions, clarify requirements, and test the revised configuration with representative requests.
Option A is unnecessarily destructive before the cause has been diagnosed. Option B changes the model without correcting stale or conflicting instructions and therefore may preserve the underlying problem. Option D institutionalizes degraded output rather than correcting it. Reviewing and updating the Project configuration is the lowest-risk and most evidence-based first action. Anthropic's Project management guidance
NEW QUESTION # 17
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: B
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 # 18
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