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| Section | Weight | Objectives |
|---|---|---|
| Governance, Risk, and Responsible Use | 15% | - Ethical and responsible AI use - Identifying appropriate and inappropriate AI use cases - Data sensitivity, privacy, and regulatory considerations - Organizational AI governance and policies |
| Troubleshooting and Optimization | 10% | - Optimizing workflows for efficiency and effectiveness - Adjusting approaches based on feedback and results - Diagnosing underperforming prompts and outputs |
| Configuration and Knowledge Management | 12% | - Configuring Claude Projects - Managing uploaded knowledge and connectors - Maintaining configuration and knowledge sources - Managing instructions and knowledge sources |
| Product and Model Selection | 12% | - Selecting Claude models - Managing context limitations and memory - Balancing quality, speed, and cost - Selecting appropriate Claude product features |
| Output Evaluation and Validation | 21% | - Determining when human review or verification is required - Editing, adapting, and refining outputs - Identifying hallucinations, inconsistencies, and bias - Fact-checking and validation - Evaluating output accuracy and completeness |
| Workflow Integration and Solution Design | 16% | - Communicating Claude's value and limitations - Analyzing requirements and use cases - Integrating Claude into existing workflows - Solution design and iteration - Research, planning, and process optimization |
| Prompting and Task Execution | 14% | - Iterating prompts to improve output - Creating effective prompts - Task decomposition - Adapting prompting strategies to task types |
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NEW QUESTION # 93
You are a senior analyst using Claude to analyze requirements from several stakeholder sources.
Which is the correct order of steps?
(1) Group related requirements and flag conflicts or ambiguities for stakeholder review.
(2) Validate the structured output against the original sources for accuracy and completeness.
(3) Submit the consolidated inputs to Claude with a structured extraction prompt.
(4) Gather and label the source materials so each requirement can be traced.
(5) Document the final requirements set with source traceability and status.
Answer: B
Explanation:
Option D is correct: 4, 1, 3, 2, 5. First, gather and label the source materials so every requirement can be traced to a stakeholder interview, process document, or other authoritative input. Second, organize related material and identify apparent conflicts or ambiguities. This preparation prevents unrelated statements from being blended and highlights issues that Claude should preserve rather than silently resolve.
Third, submit the consolidated material using a structured extraction prompt that defines the requested fields, such as requirement identifier, statement, source, owner, priority, ambiguity, and status. Anthropic's prompting guidance emphasizes clear instructions, contextual information, and explicit output formats.
Fourth, validate Claude's structured output against the original sources. This step is essential because a well- formatted answer can still omit, distort, or combine requirements. Anthropic recommends auditable citations and verification against supporting text, retracting claims that cannot be grounded.
Finally, document the approved requirements set with traceability and resolution status. Options A and C begin analysis or prompting before the sources have been properly gathered and labelled. The missing text in Option B cannot establish a valid alternative, so it has been preserved as missing rather than invented. This sequence combines task decomposition with source-grounded validation. See Anthropic's prompting best practices and hallucination-reduction guidance .
NEW QUESTION # 94
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?
Answer: B
Explanation:
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.
NEW QUESTION # 95
You are a marketing manager using Claude to research a new market segment.
Which research step should be performed first?
Answer: A
Explanation:
Under the objective Leverage Claude for research, planning, and process optimization , effective AI- assisted research workflows follow structured problem decomposition principles.
Before interacting with the model or executing information retrieval prompts, the researcher must clearly frame the investigation by defining specific, bounded research questions and target hypotheses (Option D).
Defining explicit dimensions-such as market size, competitor landscape, target customer personas, and regulatory barriers-provides the foundational framework needed to engineer targeted prompts. Submitting open-ended prompts without explicit research objectives (Option B) produces unfocused, verbose outputs.
External validation (Option C) and final report synthesis (Option A) occur downstream after structured research has been executed.
NEW QUESTION # 96
A Claude associate is applying the AI Fluency Framework Diligence competency to ongoing Claude usage.
Which practice best reflects Diligence in a governance context?
Answer: B
Explanation:
Answer A is correct. Within the AI Fluency Framework, Diligence means taking responsibility for how AI is used and for the consequences of that use. In a governance context, this requires employees to apply organizational policies consistently during everyday work, monitor whether actual practices remain aligned with those policies, and correct deficiencies when they are identified. Governance is therefore an ongoing operational responsibility rather than a one-time administrative exercise.
Option B is incorrect because compliance cannot be limited to visible or formally reviewed activities.
Selective application creates unmanaged risks and indicates that governance has not become part of normal professional conduct. Option C describes useful onboarding and documentation activities, but completing training does not demonstrate continuing compliance. Policies must also be followed during real tasks and revisited as workflows, risks, and organizational requirements change. Option D is incorrect because a compliance team can define standards and provide oversight, but individual users remain responsible for applying those standards when using Claude.
The strongest practice combines consistent policy application, active identification of gaps, and corrective action over time. Anthropic defines Diligence as taking responsibility for what people do with AI and how they do it, which directly supports Answer A. Anthropic AI Fluency Framework
NEW QUESTION # 97
A finance manager must select a Claude feature for a task that requires deterministic calculations across a multi-tab spreadsheet.
Which selection best fits the requirement?
Answer: A
Explanation:
Code Execution is the appropriate feature because it can run Python or shell-based computation in a sandboxed environment, process uploaded files, perform complex calculations, and generate analytical outputs. For a multi-tab spreadsheet, Claude can use code to read each worksheet, apply defined formulas or transformation logic, reconcile values, and return results that can be reproduced and inspected.
A normal chat response can describe calculations but does not necessarily execute them. Research mode is designed to gather and synthesize information from sources rather than serve as the primary calculation engine for an uploaded workbook. An Artifact can display or preserve an output, but the Artifact surface alone is not the computational mechanism.
Code execution improves reproducibility, but it does not guarantee that the formulas, assumptions, or source data are correct. The finance manager must review the generated code, validate important totals against known values, test edge cases, and reconcile outputs with the workbook's business rules. Anthropic's documentation explicitly states that Code Execution can analyze data, process uploaded files, perform complex calculations, and create visualizations in a secure sandbox. Therefore, D is the only feature selection aligned with deterministic spreadsheet computation. Anthropic's Code Execution documentation
NEW QUESTION # 98
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