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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Output Evaluation and Validation | 21% | - Identifying hallucinations, inconsistencies, and bias - Determining when human review or verification is required - Editing, adapting, and refining outputs - Fact-checking and validation - Evaluating output accuracy and completeness |
| Topic 2: Prompting and Task Execution | 14% | - Iterating prompts to improve output - Task decomposition - Creating effective prompts - Adapting prompting strategies to task types |
| Topic 3: Product and Model Selection | 12% | - Selecting Claude models - Selecting appropriate Claude product features - Managing context limitations and memory - Balancing quality, speed, and cost |
| Topic 4: Workflow Integration and Solution Design | 16% | - Solution design and iteration - Analyzing requirements and use cases - Research, planning, and process optimization - Integrating Claude into existing workflows - Communicating Claude's value and limitations |
| Topic 5: Troubleshooting and Optimization | 10% | - Diagnosing underperforming prompts and outputs - Adjusting approaches based on feedback and results - Optimizing workflows for efficiency and effectiveness |
| Topic 6: Configuration and Knowledge Management | 12% | - Configuring Claude Projects - Managing uploaded knowledge and connectors - Managing instructions and knowledge sources - Maintaining configuration and knowledge sources |
| Topic 7: Governance, Risk, and Responsible Use | 15% | - Data sensitivity, privacy, and regulatory considerations - Organizational AI governance and policies - Ethical and responsible AI use - Identifying appropriate and inappropriate AI use cases |
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NEW QUESTION # 14
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,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 # 15
You are a customer-success lead running an interactive workflow where each turn must respond quickly and the per-turn reasoning is light.
Which selection best aligns with the requirements?
Answer: D
Explanation:
Option A correctly matches the model tier to the workload. An interactive, high-volume workflow with light reasoning places greater importance on response speed, operational efficiency, and cost than on maximum reasoning depth. Haiku is positioned as the fast, lightweight Claude model for responsive tasks, making it the appropriate starting choice.
Option B uses a criterion unrelated to performance. Option C is incorrect because model tiers differ in capability, latency, and cost characteristics. Option D prioritizes advanced reasoning even though the scenario explicitly states that reasoning demands are light and rapid responses are required. Selecting a more capable model than the task needs can increase latency and expense without producing a meaningful business benefit.
Model selection should still be validated empirically. The team should define an acceptable quality threshold, test representative customer-success requests, measure response latency and task accuracy, and confirm that Haiku meets the required service level. If the real workload contains complex exceptions, the workflow could route only those cases to a more capable model. Anthropic's current learning materials characterize Haiku as its fastest option for lightweight work, while its prompt-engineering guidance identifies model selection as a direct lever for improving cost and latency. Anthropic's Claude learning resources
NEW QUESTION # 16
A project manager is transforming a Claude-generated draft into a polished board memo. The board reads the memo before the meeting and expects it to be scannable, factually verified, and consistent with the organization's standard terminology.
Which set of refinements is most appropriate?
Answer: B
Explanation:
Under the objective Edit, adapt, refine, and compare the output generated by Claude model(s) for the intended audience (Domain 2: Output Evaluation and Validation), preparing deliverables for executive boards requires comprehensive structural, linguistic, and factual refinement.
Option A covers all four essential post-generation refinement criteria: applying standardized heading hierarchies for visual navigation, condensing prose to maximize executive scanability, aligning terminology with enterprise glossaries, and rigorously validating every metric and claim against primary source records.
Expanding narrative sections unnecessarily (Option B) reduces scanability for board members. Ignoring heading structure and vocabulary alignment (Option C) produces an unpolished deliverable. Relying blindly on the model's internal factual correctness without external verification (Option D) introduces governance and hallucination risks into board-level decisions.
NEW QUESTION # 17
You are organizing connected data sources for a team and must complete the planning steps before authorizing any connection.
Which two planning steps must be completed BEFORE authorizing a new Connector? (Select two.) Each correct answer presents part of the solution.
Answer: A,E
Explanation:
Options A and D are correct because Connector authorization creates a security and data-governance boundary. Before connecting a service, the team must confirm that the provider is trusted and approved under organizational vendor-management requirements. It must also determine what data Claude could access, how that data is classified, and whether the proposed users have legitimate permission to use it.
Anthropic explains that Connectors can retrieve data and take actions in connected services. Claude inherits the user's source-system permissions, while custom Connectors may connect to services that Anthropic has not verified. Anthropic therefore advises users to connect only to trusted servers, carefully review requested permissions, minimize unnecessary scopes, and remain aware that connected tools may read, modify, or delete data. These considerations directly support approved-vendor and data-authorization checks.
Training may be necessary before rollout, but it does not have to be completed before the authorization decision itself. Decommissioning legacy Connectors and migrating reports are implementation or lifecycle activities that ordinarily follow approval and transition planning. They do not replace the initial trust, classification, and permission assessment. Therefore, A and D are the required pre-authorization controls. See Anthropic's Connector overview and custom Connector security guidance .
NEW QUESTION # 18
You are a customer-success manager considering whether to escalate a Claude-generated response.
Which factor most strongly raises the impact of the output and supports escalation before external use?
Answer: B
Explanation:
Under the objective Determine when human review or additional verification is required , the necessity of human-in-the-loop oversight is directly proportional to the risk profile, audience, and external consequences of the generated content.
Internal-facing drafts, meeting notes, and casual operational updates (Options A, B, and C) carry low stakes because potential factual errors or awkward phrasing can be easily corrected internally with negligible business impact. Conversely, external communications sent to paying customers that state formal Service Level Agreements (SLAs), contractual commitments, or financial guarantees (Option D) represent high-stakes organizational exposure. Unverified claims in this context can bind the company legally or damage customer trust, making rigorous human review, policy validation, and escalation mandatory prior to dispatch.
NEW QUESTION # 19
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