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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Governance, Risk, and Responsible Use | 15% | - Manage data sensitivity and privacy risks - Understand limitations and ethical boundaries - Apply responsible AI principles and policies |
| Topic 2: Troubleshooting and Optimization | 10% | - Adjust prompts and configuration to improve results - Identify causes of poor or unexpected outputs - Diagnose context and performance issues |
| Topic 3: Prompting and Task Execution | 14% | - Iterate prompts to improve output quality - Apply task decomposition to structure complex requests - Create effective prompts for business and technical tasks - Adapt prompting strategies by task type |
| Topic 4: Workflow Integration and Solution Design | 16% | - Plan end-to-end Claude-augmented workflows - Integrate Claude into existing business workflows - Design AI-assisted processes and solutions |
| Topic 5: Output Evaluation and Validation | 21% | - Apply fact-checking and validation methods - Detect hallucinations, biases, and inconsistencies - Evaluate outputs for accuracy, completeness, and consistency - Determine when human review is required |
| Topic 6: Product and Model Selection | 12% | - Understand model differences and use cases - Select appropriate Claude product features - Match model capabilities to task requirements |
| Topic 7: Configuration and Knowledge Management | 12% | - Manage knowledge sources and context - Configure Claude Projects with instructions - Maintain consistent system-level behavior |
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NEW QUESTION # 60
You are an operations lead sorting Claude-driven research tasks by which deliver the most value.
Which two Claude-driven research tasks deliver the highest value in a typical analysis workflow? (Select two.) Each correct answer presents a complete solution.
Answer: A,B
Explanation:
Options A and D provide the greatest analytical leverage because they require Claude to process substantial information, identify patterns, organize evidence, and produce a usable synthesis. Reviewing a large collection of interview transcripts manually is time-consuming and can produce inconsistent coding. Claude can accelerate the initial thematic analysis by grouping recurring needs, objections, pain points, and outlier observations while preserving links to the underlying source material for human verification.
Similarly, preparing a comparative summary from public materials requires information organization across multiple competitors or solutions. Claude can structure features, positioning, pricing claims, advantages, and limitations into a consistent comparison, allowing an analyst to concentrate on validating evidence and interpreting strategic implications.
Option B is mainly a presentation-formatting activity and provides less research leverage. Option C can save administrative time but is fundamentally transcription and restructuring rather than higher-value analysis.
Option E is inappropriate because invented "representative" quotations may be mistaken for authentic customer statements. Claude should summarize verified themes or use clearly attributed excerpts from real transcripts, not fabricate evidence.
The strongest research applications therefore combine high-volume source processing with structured synthesis, while retaining human review for source accuracy, interpretation, and final decisions.
NEW QUESTION # 61
You are a project manager working through a long planning conversation with Claude and must keep the conversation productive as it grows.
Which two practices keep a long planning conversation productive? (Select two.) Each correct answer presents a complete solution.
Answer: B,E
Explanation:
Under the objective Understand, manage context limitations and memory considerations and when to restart, summarize, or persist , managing conversational state across long planning sessions is essential for maintaining output quality and coherence. Large Language Models operate within finite context windows where attention can degrade as extraneous tokens accumulate.
Option B is a best practice because shifting subtopics often dilutes earlier instructions; explicitly restating key constraints and strategic goals ensures the model anchors its attention on active parameters without being distracted by obsolete conversational turns. Option C represents context compression-a core technique where key milestones, agreed parameters, and decisions are consolidated into a dense summary. This reduces token overhead, frees up working memory, and prevents hallucination or drift. Conversely, opening a new thread for every minor question (Option A) fractures contextual continuity, switching topics arbitrarily (Option D) pollutes attention heads, and avoiding summarization (Option E) accelerates context bloat and degrades performance.
NEW QUESTION # 62
You are a senior analyst using Claude to analyze requirements from several stakeholder sources.
Which is the correct order of steps?
(1) Group related requirements and flag conflicts or ambiguities for stakeholder review.
(2) Validate the structured output against the original sources for accuracy and completeness.
(3) Submit the consolidated inputs to Claude with a structured extraction prompt.
(4) Gather and label the source materials so each requirement can be traced.
(5) Document the final requirements set with source traceability and status.
Answer: B
Explanation:
Option D is correct: 4, 1, 3, 2, 5. First, gather and label the source materials so every requirement can be traced to a stakeholder interview, process document, or other authoritative input. Second, organize related material and identify apparent conflicts or ambiguities. This preparation prevents unrelated statements from being blended and highlights issues that Claude should preserve rather than silently resolve.
Third, submit the consolidated material using a structured extraction prompt that defines the requested fields, such as requirement identifier, statement, source, owner, priority, ambiguity, and status. Anthropic's prompting guidance emphasizes clear instructions, contextual information, and explicit output formats.
Fourth, validate Claude's structured output against the original sources. This step is essential because a well- formatted answer can still omit, distort, or combine requirements. Anthropic recommends auditable citations and verification against supporting text, retracting claims that cannot be grounded.
Finally, document the approved requirements set with traceability and resolution status. Options A and C begin analysis or prompting before the sources have been properly gathered and labelled. The missing text in Option B cannot establish a valid alternative, so it has been preserved as missing rather than invented. This sequence combines task decomposition with source-grounded validation. See Anthropic's prompting best practices and hallucination-reduction guidance .
NEW QUESTION # 63
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: C
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 # 64
You are a product designer using Claude in a solution-design exercise.
Which solution-design step should be performed first when using Claude?
Answer: A
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 # 65
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