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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 2: Configuration and Knowledge Management | 12% | - Manage uploaded knowledge and connectors - Create effective system-level instructions - Configure Claude Projects with instructions and knowledge sources - Maintain and update configurations |
| Topic 3: Product and Model Selection | 12% | - Differentiate between Claude model types - Select appropriate Claude product features - Align model selection with task requirements - Manage context limitations and memory |
| Topic 4: Output Evaluation and Validation | 21% | - Determine when human review or additional verification is required - Edit and adapt outputs for the intended audience - Apply fact-checking and validation techniques - Evaluate Claude-generated outputs for accuracy and completeness - Organize and curate information and select appropriate output formats - Identify hallucinations, inconsistencies, and biases in responses |
| Topic 5: Governance, Risk, and Responsible Use | 15% | - Apply data sensitivity, regulatory, and privacy considerations - Understand ethical implications of Claude use - Follow organizational AI policies and governance standards - Identify appropriate and inappropriate use cases |
| Topic 6: Prompting and Task Execution | 14% | - Adapt prompting strategy by task type - Iterate prompts to improve output quality - Create effective prompts for business and technical tasks - Apply task decomposition techniques to structure complex requests |
| Topic 7: Workflow Integration and Solution Design | 16% | - Integrate Claude into existing workflows and communicate its value and limitations - Support solution design through iteration - Use Claude for research, planning, and process optimization - Analyze requirements and use cases |
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NEW QUESTION # 50
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: A
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 # 51
You are reviewing items proposed for inclusion in a Project that supports policy summaries.
Which two items are appropriate to include in the Project? (Select two.) Each correct answer presents part of the solution.
Answer: C,E
Explanation:
Under the objective Configure Claude Projects with instructions and knowledge sources , curating Project scope requires adding only persistent, relevant reference assets while maintaining strict boundaries against unrelated context.
Option A represents foundational system instructions and prompt scaffolding: specifying target formatting schemas, structural sections, and audience expectations ensures consistent, repeatable policy summaries across all chats. Option B provides the canonical ground truth: uploading the current active policy documentation into the Project knowledge base gives Claude the direct reference material needed to generate factually grounded summaries. In contrast, uploading general presentation slide decks (Option C) injects unneeded token overhead. Including active legal case files (Option D) or employee onboarding records from unrelated workflows (Option E) causes context bleed, violates data compartmentalization principles, and wastes context window capacity.
NEW QUESTION # 52
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: A,C
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 # 53
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: A,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 # 54
You are an HR specialist reviewing data items proposed for inclusion in a Claude prompt.
Which two data items are appropriate to include in a Claude prompt for a routine task? (Select two.) Each correct answer presents a complete solution.
Answer: C,D
Explanation:
Options A and B apply data minimization appropriately. The redacted job description contains the content necessary for adaptation without exposing unnecessary identifying information. Likewise, an anonymized HR- policy summary provides the policy substance required for rewriting while removing personal details. Both inputs are proportionate to routine content-generation tasks.
Options C, D, and E contain sensitive employee information. Accommodation records may reveal health or disability-related information and normally require heightened confidentiality and a clearly authorized business purpose. Named salary information is confidential compensation data. Employee identifiers and hire dates can be used to identify or profile individual workers and are unnecessary merely to personalize onboarding communications. Supplying such information would violate the principle of using only the minimum data necessary for the task.
Anthropic advises users to be thoughtful about sharing sensitive personal, health, financial, and confidential business information. Its customer-support implementation guidance also recommends removing personally identifiable information unless it is explicitly required and authorized. Organizational classification rules, approved tools, access controls, contractual terms, and applicable privacy requirements must still be followed even when information has been partially redacted. Anthropic's sensitive-data guidance
NEW QUESTION # 55
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