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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Troubleshooting and Optimization | 10% | - Adjust approaches based on feedback and results - Optimize workflows for efficiency and effectiveness - Identify and diagnose issues with underperforming prompts or poor outputs |
| Topic 2: Output Evaluation and Validation | 21% | - Organize and curate information and select appropriate output formats - Apply fact-checking and validation techniques - Determine when human review or additional verification is required - Identify hallucinations, inconsistencies, and biases in responses - Edit and adapt outputs for the intended audience - Evaluate Claude-generated outputs for accuracy and completeness |
| Topic 3: Prompting and Task Execution | 14% | - Apply task decomposition techniques to structure complex requests - Iterate prompts to improve output quality - Adapt prompting strategy by task type - Create effective prompts for business and technical tasks |
| Topic 4: Governance, Risk, and Responsible Use | 15% | - Identify appropriate and inappropriate use cases - Follow organizational AI policies and governance standards - Understand ethical implications of Claude use - Apply data sensitivity, regulatory, and privacy considerations |
| Topic 5: 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 |
| Topic 6: Product and Model Selection | 12% | - Select appropriate Claude product features - Differentiate between Claude model types - Manage context limitations and memory - Align model selection with task requirements |
| Topic 7: Workflow Integration and Solution Design | 16% | - Support solution design through iteration - Integrate Claude into existing workflows and communicate its value and limitations - Use Claude for research, planning, and process optimization - Analyze requirements and use cases |
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NEW QUESTION # 85
You are a knowledge worker preparing inputs for a Claude prompt.
Which data type most clearly requires extra handling such as redaction or anonymization before being included in the prompt?
Answer: D
Explanation:
Under the objective Apply data, privacy sensitivity, regulatory policies, and privacy considerations , enterprise data protection policies require strict tiering of data assets based on sensitivity and regulatory mandates (such as GDPR, PCI-DSS, and HIPAA).
Customer government identifiers (e.g., SSN, National Insurance number) combined with full payment card numbers and individual names (Option B) represent highly sensitive Personally Identifiable Information (PII) and protected financial data. Ingesting this data into AI prompts without masking, tokenization, or redaction introduces severe compliance violations and data privacy risks. In contrast, published corporate press releases (Option A), publicly accessible executive job titles (Option C), and public marketing product descriptions (Option D) are public information assets that require no anonymization or redaction before prompt inclusion.
NEW QUESTION # 86
An operations assistant is redesigning a recurring data-processing workflow step to use Code Execution.
Which two design choices best support reliable integration? (Select two.)
Answer: B,E
Explanation:
Options A and E are correct because reliable automation requires controlled inputs and validated outputs.
Standardizing the expected input format reduces parsing failures, inconsistent field mappings, and uncontrolled variation between weekly runs. The integration should define required columns, data types, date formats, permitted null values, file naming conventions, and error-handling behaviour. This makes results comparable and supports repeatable testing.
Human verification remains necessary because successful code execution proves only that the program completed without a runtime error. It does not prove that the source data was correct, the implemented calculation matched the business requirement, or the result is operationally reasonable. A sanity check against an expected range, reconciled total, sample calculation, or known baseline helps detect silent logical and data- quality errors before downstream use.
Option B introduces avoidable variability and should instead be handled through standardized preprocessing or explicit adapters. Option C creates an undocumented interface that downstream consumers cannot reliably interpret. Option D confuses sandbox isolation and successful execution with result validation. Anthropic confirms that Code Execution can process files, perform calculations, and generate analytical outputs in a sandbox, but its outputs must still be evaluated against task-specific success criteria. See Anthropic's Code Execution documentation and evaluation guidance .
NEW QUESTION # 87
You are reviewing colleagues' responses to feedback received on Claude-drafted communications.
Which two feedback responses are productive? (Select two.)
Each correct answer presents a complete solution.
Answer: A,D
Explanation:
Options C and D are correct because productive feedback handling requires clarification, traceability, and controlled revision. When feedback is ambiguous, the writer should confirm the reviewer's intent before changing the draft. Otherwise, an incorrect interpretation may introduce new problems or remove content the reviewer intended to preserve.
Mapping each feedback item to a specific revision creates an auditable record showing what changed and why. It also helps identify unresolved comments, conflicting requests, and recommendations that were deliberately declined. This approach supports systematic comparison between the original and revised outputs.
Option A is inappropriate because contradictory suggestions cannot always be implemented simultaneously; the conflict must first be resolved by the appropriate reviewer or decision-maker. Option B postpones potentially material corrections without justification. Option E incorrectly assumes that Claude-generated text deserves automatic acceptance. Claude's output must be evaluated using the same professional, factual, and audience-specific standards applied to human-created content.
Anthropic's Description-Discernment process recommends assessing Claude's output, providing specific feedback, requesting targeted revisions, and applying human expertise and judgment to the final result.
Therefore, clarification and feedback-to-change traceability are the two productive responses. See Anthropic's Description-Discernment Loop and evaluation guidance .
NEW QUESTION # 88
An HR business partner is configuring two separate Projects for two distinct client engagements.
Which configuration best prevents context bleed between the two engagements?
Answer: D
Explanation:
Under the Claude objective Configure Claude Projects with instructions and knowledge sources , preventing context bleed and data crossover between distinct clients is a critical privacy, security, and project governance requirement. Projects in Claude serve as isolated contextual containers designed to compartmentalize project-specific knowledge files, custom system instructions, and chat histories.
Option A correctly implements strict boundary isolation by creating dedicated, independent Projects for each client engagement. By giving each Project its own discrete knowledge base and unique system prompt, reference material and conversational state from one client remain entirely inaccessible to the other. In contrast, combining client documentation into a single shared Project (Option B) or sharing memory configurations across workstreams (Option C) directly causes cross-tenant contamination and context bleed.
Similarly, using unstructured general chats without Project containers (Option D) lacks scoped memory and persistence boundaries, significantly elevating operational and confidentiality risks.
NEW QUESTION # 89
You are setting up a Connector for a shared data source that several colleagues will use.
Which Connector setup step should be performed first?
Answer: D
Explanation:
Option D is the necessary first step because authorization and data suitability must be established before the shared source is connected to Claude. The team should verify that the connector and its provider are approved under organizational AI, security, procurement, and privacy policies. It must also determine the source's data classification and whether information in that source may legally and contractually be processed through the connector.
Connecting the source first, as proposed in Option C, could expose restricted information before governance review has occurred. Testing a representative task under Option B likewise assumes that access has already been approved. Documenting the connector's purpose and suitable use cases under Option A is important, but that operational documentation should follow the initial determination that the connector and relevant data are permitted.
After approval, the team can connect the source using an account with appropriate permissions, verify authentication, test access boundaries, and confirm that Claude retrieves only information available to the authenticated user. The team should then document approved use cases, prohibited data, human-review requirements, and reporting procedures. This sequence follows least-privilege and data-governance principles and avoids treating successful authentication as evidence that a connector is organizationally authorized or suitable for every type of information.
NEW QUESTION # 90
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