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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Output Evaluation and Validation | 21% | - Evaluate Claude-generated outputs for accuracy and completeness - Edit and adapt outputs for the intended audience - Determine when human review or additional verification is required - Apply fact-checking and validation techniques - Organize and curate information and select appropriate output formats - Identify hallucinations, inconsistencies, and biases in responses |
| Topic 2: Workflow Integration and Solution Design | 16% | - Support solution design through iteration - Analyze requirements and use cases - Use Claude for research, planning, and process optimization - Integrate Claude into existing workflows and communicate its value and limitations |
| Topic 3: Product and Model Selection | 12% | - Align model selection with task requirements - Select appropriate Claude product features - Differentiate between Claude model types - Manage context limitations and memory |
| Topic 4: Troubleshooting and Optimization | 10% | - Adjust approaches based on feedback and results - Identify and diagnose issues with underperforming prompts or poor outputs - Optimize workflows for efficiency and effectiveness |
| Topic 5: Configuration and Knowledge Management | 12% | - Create effective system-level instructions - Maintain and update configurations - Configure Claude Projects with instructions and knowledge sources - Manage uploaded knowledge and connectors |
| Topic 6: 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 7: Governance, Risk, and Responsible Use | 15% | - Understand ethical implications of Claude use - Apply data sensitivity, regulatory, and privacy considerations - Identify appropriate and inappropriate use cases - Follow organizational AI policies and governance standards |
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NEW QUESTION # 124
You are evaluating whether a "redesign new-hire onboarding" request should be decomposed before prompting Claude.
Which feature of the request most strongly indicates decomposition will help?
Answer: C
Explanation:
Option B presents a compound assignment with three materially different deliverables: a process map, supporting content, and a measurement plan. Each requires different inputs, reasoning steps, evaluation criteria, and output structures. Decomposing the request allows the team to define the onboarding objective, map the current process, design the future-state workflow, draft the supporting materials, establish success measures, and then integrate the components into a coherent package.
Task decomposition improves control because each intermediate result can be reviewed before it becomes an input to the next stage. It also prevents one large prompt from underdeveloping a component, overlooking dependencies, or blending incompatible formats. For example, measurement criteria should be based on the redesigned process rather than generated independently before the workflow is defined.
Options A, C, and D describe familiar or narrowly bounded work that can reasonably be handled with one structured prompt. Anthropic's workflow guidance recommends breaking complex interactions into unique tasks and using prompt chains when additional structure is needed. Decomposition should therefore be driven by the number of distinct deliverables and their dependencies, not simply by the length of the request.
Anthropic's Claude workflow guidance
NEW QUESTION # 125
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,D
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 # 126
A Claude associate is evaluating a proposed use case in which Claude would issue final hiring decisions without any human review, applying the AI Fluency Framework Delegation competency.
Which classification best reflects Delegation principles?
Answer: D
Explanation:
Under the objective Understand ethical implications of AI usage and the AI Fluency Framework Delegation competency, determining autonomous task suitability depends on assessing human consequence, ethical responsibility, and legal accountability.
Hiring decisions directly impact individuals' careers, livelihoods, and civil rights, carrying substantial legal, moral, and organizational consequences. Delegation principles strictly classify autonomous employment, promotion, and termination determinations as non-delegable tasks that must remain under human ownership (Option A). Human judgment, context discernment, and legal accountability cannot be offloaded to an algorithmic model. Sample-based after-the-fact reviews (Option B) or delegating fully in pursuit of processing efficiency (Option C) violate responsible AI governance. Furthermore, classifying the prohibition as merely an issue of system technical reliability (Option D) ignores the fundamental imperative of ethical and human accountability.
NEW QUESTION # 127
You are reviewing a Claude-generated competitor brief for unsupported claims.
Which is the correct order of review steps?
(1) Cross-check each specific claim against the supplied source material or trusted external references.
(2) Decide whether to correct, re-prompt, or escalate based on the findings.
(3) Document each unsupported or inconsistent claim with its location and severity.
(4) Identify the specific factual claims, statistics, and citations in the brief.
(5) Read the brief from beginning to end and flag any claims that feel surprising.
Answer: D
Explanation:
Answer C is correct. A defensible fact-check begins with a complete reading of the brief (5), which reveals its overall argument and flags surprising or unusually confident assertions. The reviewer then identifies the concrete factual claims, statistics, and citations that can actually be tested (4). Those items are checked against supplied evidence or trustworthy external sources (1). Any unsupported, contradictory, or inaccurate claim is then recorded with its location and severity (3), creating a usable audit trail. Only after the evidence is assembled should the reviewer decide whether to correct the text, re-prompt Claude, or escalate the issue (2).
This produces the sequence 5, 4, 1, 3, 2. Option D reverses the first two activities, while Options A and B attempt verification before a proper claim inventory exists. Anthropic's guidance recommends structured research, explicit success criteria, and verification across sources; the AI Fluency framework's Discernment and Diligence competencies also keep evaluation and accountability with the human reviewer. The PDF marks D, but that order is weaker and should be corrected to C. Anthropic prompting best practices
NEW QUESTION # 128
A manager is deciding what changes to make while conducting a Memory maintenance review for a long- running workflow.
Which two actions best support Memory quality over time? (Select two.)
Answer: B,D
Explanation:
Option B creates a recoverable checkpoint before maintenance. Exporting the current Memory allows the manager to restore important context if a necessary entry is accidentally removed or rewritten incorrectly.
Option E performs the actual quality maintenance by removing stale or inaccurate information and adding durable context that the workflow still requires.
Deleting everything, as proposed in Option A, discards valid context and forces the system to reconstruct it imperfectly from later activity. Option C assumes that the absence of a reported problem proves every stored entry remains accurate; stale Memory may affect future tasks even when current outputs appear acceptable.
Option D eliminates useful continuity instead of correcting the specific quality problems.
Anthropic allows users to view, edit, and export Memory. Its documentation states that exported Memory can serve as a backup or be transferred to another service. Users can also add information that Claude has not automatically retained. A responsible maintenance cycle should therefore back up the current state, inspect entries for accuracy and relevance, remove obsolete details, add missing durable context, and verify future outputs. This preserves continuity while reducing the risk that outdated assumptions contaminate later sessions. Anthropic's Memory import and export guidance
NEW QUESTION # 129
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