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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prompting and Task Execution | 14% | - Task decomposition - Creating effective prompts - Iterating prompts to improve output - Adapting prompting strategies to task types |
| Topic 2: Governance, Risk, and Responsible Use | 15% | - Data sensitivity, privacy, and regulatory considerations - Identifying appropriate and inappropriate AI use cases - Ethical and responsible AI use - Organizational AI governance and policies |
| Topic 3: Workflow Integration and Solution Design | 16% | - Solution design and iteration - Analyzing requirements and use cases - Communicating Claude's value and limitations - Integrating Claude into existing workflows - Research, planning, and process optimization |
| Topic 4: Configuration and Knowledge Management | 12% | - Configuring Claude Projects - Maintaining configuration and knowledge sources - Managing instructions and knowledge sources - Managing uploaded knowledge and connectors |
| Topic 5: Product and Model Selection | 12% | - Selecting Claude models - Managing context limitations and memory - Balancing quality, speed, and cost - Selecting appropriate Claude product features |
| 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% | - Fact-checking and validation - Evaluating output accuracy and completeness - Determining when human review or verification is required - Identifying hallucinations, inconsistencies, and bias - Editing, adapting, and refining outputs |
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NEW QUESTION # 112
A knowledge worker is explaining context-window basics to a teammate.
Which statement most accurately describes a context window?
Answer: B
Explanation:
Answer B is correct. A context window is the bounded amount of information that a model can consider when generating a response. Depending on the Claude interface or implementation, this can include the current user message, earlier messages in the active conversation, system instructions, uploaded documents, tool results, and other supplied content. The capacity is finite and is normally measured in tokens rather than words or pages.
Option A incorrectly defines the context window as only the information the model considers most relevant.
Relevance selection or retrieval can determine which materials are placed into the window, but the context window itself is the total bounded processing capacity. Option C incorrectly treats it as unlimited cross- session memory. A model does not automatically receive every previous conversation whenever it responds in a different chat. Any available memory or Project knowledge is a separate feature and must be supplied or retrieved into the active context. Option D is also incorrect because users generally do not configure the context-window size independently for each chat.
When a conversation becomes too long or cluttered, the user may need to summarize essential information, remove irrelevant material, or start a focused new conversation. This preserves relevant context and reduces conflicts between current and obsolete instructions. Anthropic: Context windows
NEW QUESTION # 113
You are an operations manager preparing a quick reference card for a team.
Which description correctly characterizes the three Claude model tiers in general terms?
Answer: C
Explanation:
Under the objective Differentiate between Claude model types (e.g., Haiku, Sonnet, Opus) (Domain 3:
Product and Model Selection), Anthropic structures its model family across three clear tiers of intelligence, latency, and cost:
* Claude Haiku : The fastest, most cost-efficient, and lightweight model designed for high-volume, low- latency tasks and simple classification.
* Claude Sonnet : The balanced, versatile enterprise workhorse combining high intelligence and throughput for everyday knowledge-work tasks.
* Claude Opus : The frontier model offering deep reasoning and analytical capacity for complex, multi- step problem solving at higher latency and operational cost.
Option C perfectly summarizes these structural trade-offs. Options A, B, and D misrepresent the capability hierarchy, speed profiles, and architectural tiering of the Claude family.
NEW QUESTION # 114
You are a project manager at Orinexa, Inc. You are picking a Claude model for a new production workload and must complete the requirements steps before testing candidates.
Which two steps must be completed BEFORE testing candidate models on a representative sample? (Select two.) Each correct answer presents part of the solution.
Answer: B,D
Explanation:
Under the objective Align model selection with task requirements (e.g., cost, speed, quality) , model evaluation follows a structured engineering workflow prior to benchmark testing.
Before running comparative empirical tests on a representative sample dataset, the team must first establish evaluation criteria: defining the required output quality bar, maximum acceptable latency, throughput targets, and budget constraints (Option A). Next, the team maps those requirements against known model family characteristics (Haiku, Sonnet, Opus) to select the appropriate candidate models for testing (Option C). Once requirements are set and candidates selected, empirical sample testing can proceed. Promoting models to production (Option D), securing final platform/security rollout sign-offs (Option E), and ongoing regression testing for new model releases (Option B) are downstream deployment activities that occur after testing is completed.
NEW QUESTION # 115
A knowledge worker notices that a Claude output may have repeated a confidential financial figure from an internal source that the user was instructed to treat as restricted. The output has not yet been shared outside the user.
Which immediate action is most appropriate?
Answer: A
Explanation:
Option A is the appropriate containment and reporting response. The appearance of restricted information in a generated output indicates a possible data-handling or access-control failure. The user should stop the output from progressing, preserve enough information to investigate what occurred, and follow the organization's established governance or security incident procedure. Relevant evidence normally includes the prompt, generated response, implicated source, users or systems involved, and whether any further distribution occurred.
Deleting the figure from the visible output, as suggested in Option B, does not address how the restricted information entered the workflow or whether it remains present in conversation history, logs, connected sources, or other generated material. A personal log is not a substitute for the approved reporting mechanism.
Starting a new chat under Option C may prevent additional use of that conversation but does not remediate or report the original incident. Option D incorrectly assumes that an internal exposure is harmless.
Organizational confidentiality requirements can be violated even before information is distributed externally.
Claude users remain accountable for applying data classifications, limiting sensitive inputs, reviewing generated outputs, and escalating suspected exposure through authorized channels. Early reporting enables containment and corrective action.
NEW QUESTION # 116
When refining a Claude output that does not meet expectations, which iterative refinement step should be performed first?
Answer: C
Explanation:
Answer D is correct. Iterative refinement should begin by diagnosing the current output against the original requirements. The reviewer must identify specific weaknesses such as missing information, unsupported claims, incorrect tone, poor organization, excessive length, or failure to follow the requested format. This diagnosis provides evidence for deciding what must change in the prompt.
After identifying the weaknesses, the reviewer can modify the prompt to address them, which corresponds to Option C. The revised prompt is then submitted and the resulting output captured, as described in Option A.
Finally, the new output is compared with the original criteria to determine whether the revision improved performance, as stated in Option B. The logical sequence is therefore D, C, A, and B.
Beginning with prompt modification before diagnosing the failure encourages random changes and makes it difficult to determine which revision produced an improvement. Similarly, a new output cannot be submitted or evaluated before a targeted revision has been created. Anthropic's guidance recommends defining clear success criteria, evaluating results against those criteria, and using a structured self-correction process:
generate a draft, review it, and then refine it based on identified deficiencies. Effective iteration is therefore diagnostic and evidence-based rather than trial and error. Anthropic prompting best practices
NEW QUESTION # 117
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