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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Workflow Integration and Solution Design | 16% | - Research, planning, and process optimization - Communicating Claude's value and limitations - Integrating Claude into existing workflows - Solution design and iteration - Analyzing requirements and use cases |
| Topic 2: Prompting and Task Execution | 14% | - Creating effective prompts - Iterating prompts to improve output - Adapting prompting strategies to task types - Task decomposition |
| Topic 3: Configuration and Knowledge Management | 12% | - Maintaining configuration and knowledge sources - Managing uploaded knowledge and connectors - Configuring Claude Projects - Managing instructions and knowledge sources |
| Topic 4: Governance, Risk, and Responsible Use | 15% | - Identifying appropriate and inappropriate AI use cases - Data sensitivity, privacy, and regulatory considerations - Ethical and responsible AI use - Organizational AI governance and policies |
| Topic 5: Product and Model Selection | 12% | - Selecting Claude models - Selecting appropriate Claude product features - Balancing quality, speed, and cost - Managing context limitations and memory |
| Topic 6: Troubleshooting and Optimization | 10% | - Diagnosing underperforming prompts and outputs - Adjusting approaches based on feedback and results - Optimizing workflows for efficiency and effectiveness |
| Topic 7: Output Evaluation and Validation | 21% | - Editing, adapting, and refining outputs - Fact-checking and validation - Determining when human review or verification is required - Identifying hallucinations, inconsistencies, and bias - Evaluating output accuracy and completeness |
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NEW QUESTION # 108
You are writing system-level instructions for a Project that supports recurring writing tasks.
Which step should be performed first when writing the system-level instructions?
Answer: A
Explanation:
Under the objective Create effective system-level instructions (Domain 5: Configuration and Knowledge Management), engineering project-level system instructions follows a top-down operational framework.
Before authoring specific formatting rules, edge-case handlers, or role prompts, the author must first establish the high-level boundary architecture: defining the core objective and purpose of the Project, the target audience, and the explicit constraints/guardrails the model must respect (Option C). These foundational boundaries dictate what the model should do, what it must refuse or escalate, and how it must interact with attached reference knowledge. Attempting to validate instructions against test requests (Option B) or iteratively refine them (Option A) requires a drafted system prompt to exist first. Distributing instructions to team collaborators (Option D) is the final operational rollout step after validation is complete.
NEW QUESTION # 109
You are a content creator. A first response from Claude is accurate but uses inconsistent terminology that does not match your organization's glossary.
Which iterative change addresses the issue most directly?
Answer: B
Explanation:
Option C directly addresses both causes of the problem: Claude lacks the organization's authoritative terminology, and the prompt does not explicitly require adherence to it. Supplying the glossary gives Claude the necessary reference material, while instructing it to use only those terms converts the desired style into a clear and testable constraint.
Option A leaves terminology selection to Claude, so the resulting language may be internally consistent but still conflict with the organization's standards. Option B creates avoidable manual work and increases the possibility that some mismatched terms will be overlooked. Option D asks for consistency but fails to define which terminology is correct. Claude might consistently apply a term that is not approved by the organization.
Effective prompt iteration should diagnose the observed defect and introduce the missing information or instruction that directly resolves it. In this case, the issue is not factual accuracy, response length, or general writing quality. It is compliance with a controlled vocabulary. The revised prompt could also instruct Claude to flag concepts that have no approved glossary equivalent rather than inventing a replacement. For recurring work, the glossary and terminology rule could be placed in Project knowledge and Project instructions so that the constraint is applied consistently across related conversations.
NEW QUESTION # 110
A knowledge worker has noticed that the same prompt for a recurring weekly task produces inconsistent outputs across runs. Some weeks the output uses the right sections but the wrong tone; other weeks the tone is right but a section is missing. The source inputs change each week but follow the same structure.
Which cause is most worth investigating first?
Answer: D
Explanation:
Answer A is correct. The pattern shows inconsistent compliance with requirements that should have been stated explicitly: the audience, tone, output structure, and mandatory sections. If those requirements are vague or implied, Claude may emphasize different aspects of the request across runs, especially as the weekly source content changes. The first troubleshooting step should therefore be to inspect the prompt and convert each expectation into a clear, testable instruction.
Option B is incomplete. Model outputs can vary, but non-determinism should not be used to excuse recurring failures to satisfy defined requirements. Strong prompts and evaluation criteria can substantially reduce unacceptable variation. Option C is less likely because the weekly inputs follow the same structure; changing facts alone should not cause required sections or tone instructions to disappear. Option D adds unnecessary workflow complexity. Claude can follow tone and structural requirements within one prompt when those requirements are clearly separated and explicitly defined.
The prompt should identify the intended audience, prescribe the tone, list every required section, specify the output format, and state that no section may be omitted. Anthropic recommends clear, direct instructions and precise output constraints, particularly when order or completeness matters. Anthropic prompting best practices
NEW QUESTION # 111
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: B
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 # 112
An HR business partner is configuring two separate Projects for two distinct client engagements.
Which configuration best prevents context bleed between the two engagements?
Answer: C
Explanation:
Under the Claude objective Configure Claude Projects with instructions and knowledge sources , preventing context bleed and data crossover between distinct clients is a critical privacy, security, and project governance requirement. Projects in Claude serve as isolated contextual containers designed to compartmentalize project-specific knowledge files, custom system instructions, and chat histories.
Option A correctly implements strict boundary isolation by creating dedicated, independent Projects for each client engagement. By giving each Project its own discrete knowledge base and unique system prompt, reference material and conversational state from one client remain entirely inaccessible to the other. In contrast, combining client documentation into a single shared Project (Option B) or sharing memory configurations across workstreams (Option C) directly causes cross-tenant contamination and context bleed.
Similarly, using unstructured general chats without Project containers (Option D) lacks scoped memory and persistence boundaries, significantly elevating operational and confidentiality risks.
NEW QUESTION # 113
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