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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Governance, Risk, and Responsible Use | 15% | - Identify appropriate and inappropriate use cases - Apply data sensitivity, regulatory, and privacy considerations - Understand ethical implications of Claude use - Follow organizational AI policies and governance standards |
| Topic 2: 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 3: Output Evaluation and Validation | 21% | - Edit and adapt outputs for the intended audience - Identify hallucinations, inconsistencies, and biases in responses - Evaluate Claude-generated outputs for accuracy and completeness - Apply fact-checking and validation techniques - Determine when human review or additional verification is required - Organize and curate information and select appropriate output formats |
| Topic 4: Product and Model Selection | 12% | - Manage context limitations and memory - Differentiate between Claude model types - Select appropriate Claude product features - Align model selection with task requirements |
| Topic 5: 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 |
| Topic 6: Configuration and Knowledge Management | 12% | - Maintain and update configurations - Manage uploaded knowledge and connectors - Create effective system-level instructions - Configure Claude Projects with instructions and knowledge sources |
| Topic 7: Prompting and Task Execution | 14% | - Adapt prompting strategy by task type - Create effective prompts for business and technical tasks - Apply task decomposition techniques to structure complex requests - Iterate prompts to improve output quality |
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NEW QUESTION # 13
As part of a workflow redesign, a process analyst is applying the AI Fluency Framework Delegation competency.
Which two criteria most directly inform Delegation decisions? (Select two.)
Answer: B,C
Explanation:
Options B and E are correct because Delegation requires evaluating both the capabilities needed for a task and the risk created by assigning it to AI. Anthropic defines Delegation as thoughtfully deciding which work should be completed by a human, performed collaboratively, or handled independently by AI. Tasks requiring distinctly human empathy, creativity, contextual judgment, accountability, or value-based decisions are less suitable for complete delegation. Claude may assist, but a human should retain control of the consequential decision.
Reversibility and error consequences are also critical. A low-impact, easily checked draft can generally receive more AI autonomy than an irreversible action or a decision that could materially affect people.
Anthropic's research on workplace AI use similarly reports that employees tend to delegate tasks that are low- stakes and readily verifiable while applying active supervision to high-stakes work.
Workflow age, diagram complexity, and employee seniority are indirect characteristics; they do not establish whether Claude has the required capabilities or whether an error can be safely corrected. An old workflow can still contain unsuitable tasks, and a junior employee may perform highly sensitive work. Thus, B and E provide the most relevant task-level and risk-level criteria. See Anthropic's Delegation lesson and research on workplace AI delegation .
NEW QUESTION # 14
You are a project manager reviewing stakeholder messages that teammates have drafted about Claude's role in a workflow.
Which two messages are appropriate for stakeholder communication about Claude? (Select two.) Each correct answer presents part of the solution.
Answer: B,D
Explanation:
Options C and D are correct because responsible stakeholder communication must accurately describe Claude' s role, human accountability, and the integration's limitations. Stakeholders should understand which activities Claude performs, which outputs require verification, who retains decision authority, and what happens when the model produces uncertain, incomplete, biased, or incorrect results. Known failure modes and escalation paths should be communicated in language appropriate to the audience.
Anthropic's AI Fluency Framework defines Diligence as taking responsibility for what people do with AI and how they do it. Anthropic's research also identifies honesty about AI's role and consideration of the consequences of sharing AI-generated work as important AI Fluency behaviours. These principles support transparent descriptions rather than exaggerated capability claims.
Options A and B are inappropriate absolutes. Claude can make factual errors or produce outputs inconsistent with supplied context, so human review cannot be categorically removed. Option E is too vague to support informed trust, accountability, or effective governance. Appropriate communication should neither overstate Claude's reliability nor obscure its contribution. It should describe Claude as a capable assistant operating within defined boundaries, with humans maintaining responsibility for consequential decisions and quality control. See Anthropic's AI Fluency Framework and AI Fluency Index .
NEW QUESTION # 15
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: C,D
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 # 16
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: A,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 # 17
You are evaluating a Claude output that appears to contain hallucinated content.
Which response step should be performed first when a hallucination is suspected?
Answer: A
Explanation:
Under the objective Identify hallucinations, inconsistencies, and biases in responses , diagnosing potential generative errors requires structured evidence validation before taking corrective action.
The first diagnostic step is claim isolation: carefully examining the output to pinpoint the exact statement, metric, or entity that appears unsupported by the provided source context or verified reality (Option D).
Pinpointing the specific defect allows the reviewer to compare it directly against the source data to confirm whether it is an actual hallucination, an ungrounded inference, or a valid synthesis. Discarding the entire output without identifying the error (Option A) prevents root-cause analysis and wastes working iterations.
Logging risks (Option B) or broadcasting notifications to users (Option C) are secondary actions that occur only after the defect has been isolated and confirmed.
NEW QUESTION # 18
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