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| Section | Weight | Objectives |
|---|---|---|
| Output Evaluation and Validation | 21% | - Detect hallucinations, biases, and inconsistencies - Evaluate outputs for accuracy, completeness, and consistency - Determine when human review is required - Apply fact-checking and validation methods |
| Workflow Integration and Solution Design | 16% | - Design AI-assisted processes and solutions - Integrate Claude into existing business workflows - Plan end-to-end Claude-augmented workflows |
| Configuration and Knowledge Management | 12% | - Manage knowledge sources and context - Maintain consistent system-level behavior - Configure Claude Projects with instructions |
| Governance, Risk, and Responsible Use | 15% | - Apply responsible AI principles and policies - Manage data sensitivity and privacy risks - Understand limitations and ethical boundaries |
| Prompting and Task Execution | 14% | - Create effective prompts for business and technical tasks - Iterate prompts to improve output quality - Apply task decomposition to structure complex requests - Adapt prompting strategies by task type |
| Product and Model Selection | 12% | - Match model capabilities to task requirements - Understand model differences and use cases - Select appropriate Claude product features |
| Troubleshooting and Optimization | 10% | - Identify causes of poor or unexpected outputs - Adjust prompts and configuration to improve results - Diagnose context and performance issues |
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NEW QUESTION # 132
Before uploading a customer file for a Code Execution analysis, a customer-success associate at Noventra, Ltd. is reviewing the planned task.
Which two practices best align with sound data handling? (Select two.)
Answer: A,B
Explanation:
Options B and E apply organizational governance and data-minimization requirements before the file enters Claude's execution environment. The associate must first establish whether Code Execution is an approved processing method for the file's classification. If the information exceeds the approved sensitivity level, the user must escalate to privacy, security, legal, or another designated authority rather than proceeding independently.
The file must also be inspected for customer identifiers, confidential fields, regulated information, credentials, or data unrelated to the calculation. Unnecessary fields should be removed, redacted, or anonymized. A sandbox limits interaction with the surrounding system, but it does not change the sensitivity of uploaded data, provide authorization, or override the organization's handling rules. Therefore, Options C and D incorrectly treat technical isolation as a governance exemption. Option A allows urgency to displace mandatory review.
Anthropic explains that Code Execution runs in an isolated container, while also warning that file-processing configurations and network access create security considerations. Its privacy guidance recommends careful treatment of highly sensitive personal and confidential information. The correct control is to minimize and authorize the data before uploading it, not rely solely on the sandbox afterward. Anthropic's Code Execution security guidance
NEW QUESTION # 133
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,B
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 # 134
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,C
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 # 135
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,E
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 # 136
A procurement manager is evaluating a Claude-generated vendor proposal summary against the original RFP.
The RFP lists twelve numbered requirements across pricing, security, and service levels.
How should the summary be checked for completeness?
Answer: A
Explanation:
Option C is correct because completeness must be evaluated systematically against every explicit requirement in the authoritative source. The reviewer should create a traceability matrix that lists all twelve RFP requirements and identifies where each requirement is addressed in the Claude-generated summary. The review should confirm not only that each requirement appears, but also that its essential conditions, qualifications, and vendor response are represented accurately.
Matching headings, as proposed in Option A, cannot establish completeness. A section may have the correct title while omitting material requirements or misrepresenting the vendor's response. Option B is inadequate because checking only three requirements could miss important omissions involving security controls, pricing conditions, or service-level obligations. Option D evaluates coherence rather than coverage. A summary can read smoothly and persuasively while excluding inconvenient or contradictory information.
A robust evaluation should assess accuracy, completeness, relevance, and traceability separately. Any requirement that is absent, only partially represented, or unsupported by the proposal should be recorded and corrected before the summary is used in vendor evaluation. This requirement-by-requirement mapping prevents Claude's fluent presentation from being mistaken for evidence that the complete RFP has been covered.
NEW QUESTION # 137
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