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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Workflow Integration and Solution Design | 16% | - Solution design and iteration - Research, planning, and process optimization - Integrating Claude into existing workflows - Communicating Claude's value and limitations - Analyzing requirements and use cases |
| Topic 2: Troubleshooting and Optimization | 10% | - Optimizing workflows for efficiency and effectiveness - Diagnosing underperforming prompts and outputs - Adjusting approaches based on feedback and results |
| Topic 3: Governance, Risk, and Responsible Use | 15% | - Organizational AI governance and policies - Data sensitivity, privacy, and regulatory considerations - Ethical and responsible AI use - Identifying appropriate and inappropriate AI use cases |
| Topic 4: Configuration and Knowledge Management | 12% | - Managing uploaded knowledge and connectors - Configuring Claude Projects - Managing instructions and knowledge sources - Maintaining configuration and knowledge sources |
| Topic 5: Output Evaluation and Validation | 21% | - Fact-checking and validation - Identifying hallucinations, inconsistencies, and bias - Evaluating output accuracy and completeness - Determining when human review or verification is required - Editing, adapting, and refining outputs |
| Topic 6: Product and Model Selection | 12% | - Selecting Claude models - Balancing quality, speed, and cost - Managing context limitations and memory - Selecting appropriate Claude product features |
| Topic 7: Prompting and Task Execution | 14% | - Task decomposition - Creating effective prompts - Iterating prompts to improve output - Adapting prompting strategies to task types |
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NEW QUESTION # 19
You are a product designer using Claude in a solution-design exercise.
Which solution-design step should be performed first when using Claude?
Answer: D
Explanation:
Option C is correct because effective solution design begins by defining the problem, desired outcome, success criteria, and relevant constraints. Without this foundation, Claude may generate polished concepts that solve the wrong problem or violate important business, technical, regulatory, budgetary, or audience requirements. Anthropic's AI Fluency Delegation guidance calls this "Problem Awareness": understanding the goal and the work required before deciding how to involve AI. Its applied Delegation process likewise begins with establishing the project vision and defining what a successful result looks like before breaking the work into tasks.
After the problem and constraints are clear, Claude can productively generate diverse solution concepts, making Option A a later ideation step. Option B follows concept generation because alternatives cannot be compared until they exist and the evaluation constraints have been established. Option D comes later still, after a candidate has been evaluated and selected. Starting with mock-ups would prematurely commit effort to an unvalidated solution. Therefore, articulating the problem and constraints first creates the grounding Claude needs for relevant ideation, systematic comparison, and defensible design decisions. This sequence is consistent with Anthropic's AI Fluency Delegation guidance and Applying Delegation lesson .
NEW QUESTION # 20
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: C
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 # 21
You are an operations lead and notice that a Project's outputs have been drifting from the team's current expectations.
Which maintenance action should be tried first?
Answer: D
Explanation:
Project output drift commonly occurs when persistent instructions, reference documents, terminology, policies, or expected formats no longer reflect the team's current operating standard. Option C addresses the most direct and controllable cause by reviewing the Project's instructions and knowledge sources before replacing the Project or changing models.
Anthropic explains that Project instructions define how Claude should behave and respond across chats in that Project. Documents uploaded to Project knowledge provide persistent context that Claude uses throughout those chats. If either component contains obsolete requirements, conflicting guidance, or superseded source material, Claude can consistently produce results that no longer meet the team's expectations. The appropriate maintenance response is to compare instructions and knowledge against current standards, remove obsolete files, resolve contradictions, clarify requirements, and test the revised configuration with representative requests.
Option A is unnecessarily destructive before the cause has been diagnosed. Option B changes the model without correcting stale or conflicting instructions and therefore may preserve the underlying problem. Option D institutionalizes degraded output rather than correcting it. Reviewing and updating the Project configuration is the lowest-risk and most evidence-based first action. Anthropic's Project management guidance
NEW QUESTION # 22
Your conversation context has grown long, and the model is producing inconsistent outputs.
Which context-management step should be performed first?
Answer: C
Explanation:
Option C is the appropriate first step because summarization compresses a lengthy and potentially conflicting conversation into a clear working state. The summary should retain the current objective, confirmed decisions, relevant facts, active constraints, required output format, unresolved questions, and the next action Claude must perform. Obsolete instructions, rejected alternatives, and repetitive discussion should be removed.
Option A does not directly address the cause of the inconsistency. A more capable model may still struggle when the conversation contains conflicting requirements or excessive irrelevant material. Option B provides no corrective action and allows the context problem to continue. Option D may eventually be necessary, but immediately moving to a new conversation risks losing important information or reproducing the same confusion if the essential context has not first been identified.
After producing the summary, the user should compare it with the original requirements and correct any omissions. The validated summary can then be reasserted in the current conversation. If performance remains inconsistent, it can serve as the starting context for a new chat. This staged approach preserves useful progress while reducing context noise and limiting unnecessary repetition.
NEW QUESTION # 23
A customer-success associate is drafting a prompt that asks Claude to summarize a customer issue from the case file. The case file includes the customer ' s full government identifier, home address, name, account number, and the description of the issue. The summary the associate needs is about the issue.
Which approach is most appropriate?
Answer: B
Explanation:
Under the objective Apply data, privacy sensitivity, regulatory policies, and privacy considerations (Domain
6: Governance, Risk, and Responsible Use), data minimization is a primary privacy requirement.
When processing enterprise documents with AI tools, users must never submit highly sensitive Personally Identifiable Information (PII)-such as national government identifiers (SSNs/National Insurance numbers) or private home addresses-unless strictly required for the analytical task. Because the associate only needs a summary of the core customer problem, redacting or removing those non-essential high-sensitivity fields prior to input ingestion (Option C) enforces sound privacy hygiene. Relying on in-prompt instructions to " ignore " ingested PII (Option A) still exposes raw sensitive tokens to the model context. Swapping identifiers for other sensitive account data (Option B) fails data protection standards.
NEW QUESTION # 24
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