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| Section | Weight | Objectives |
|---|---|---|
| Configuration and Knowledge Management | 12% | - Manage uploaded knowledge and connectors - Configure Claude Projects with instructions and knowledge sources - Create effective system-level instructions - Maintain and update configurations |
| Product and Model Selection | 12% | - Align model selection with task requirements - Select appropriate Claude product features - Differentiate between Claude model types - Manage context limitations and memory |
| Troubleshooting and Optimization | 10% | - Adjust approaches based on feedback and results - Optimize workflows for efficiency and effectiveness - Identify and diagnose issues with underperforming prompts or poor outputs |
| Governance, Risk, and Responsible Use | 15% | - Understand ethical implications of Claude use - Apply data sensitivity, regulatory, and privacy considerations - Follow organizational AI policies and governance standards - Identify appropriate and inappropriate use cases |
| Workflow Integration and Solution Design | 16% | - Support solution design through iteration - Analyze requirements and use cases - Integrate Claude into existing workflows and communicate its value and limitations - Use Claude for research, planning, and process optimization |
| Output Evaluation and Validation | 21% | - Apply fact-checking and validation techniques - Organize and curate information and select appropriate output formats - Edit and adapt outputs for the intended audience - Determine when human review or additional verification is required - Evaluate Claude-generated outputs for accuracy and completeness - Identify hallucinations, inconsistencies, and biases in responses |
| Prompting and Task Execution | 14% | - Adapt prompting strategy by task type - Create effective prompts for business and technical tasks - Apply task decomposition techniques to structure complex requests - Iterate prompts to improve output quality |
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NEW QUESTION # 13
A workflow analyst is designing a hand-off between Claude and a human reviewer in an existing process. The reviewer needs to know what to check and what is in scope for their review.
Which hand-off design is most appropriate?
Answer: B
Explanation:
Answer B is correct. A reliable Claude-to-human hand-off requires explicit, documented responsibilities for both stages of the workflow. The workflow should define the content and quality standards Claude's output must satisfy and separately identify what the human reviewer must verify before approval. Review criteria might include factual accuracy, source support, completeness, policy compliance, tone, confidentiality, and authorization to publish or act.
Option A produces inconsistent oversight because individual reviewers may apply different standards or overlook critical controls. Domain judgment remains important, but it should supplement documented minimum requirements rather than replace them. Option C defines Claude's responsibilities but leaves the human review scope ambiguous, creating accountability gaps. Option D introduces another weakness: Claude should not independently determine the controls used to validate its own output. A stable, human-approved checklist may be appropriate, but dynamically generated criteria could omit the exact risks requiring independent review.
Explicit acceptance and review criteria make the workflow repeatable, auditable, and easier to improve.
Anthropic's evaluation guidance emphasizes defining clear success criteria and using task-specific evaluation methods. Applying that principle to a human hand-off means documenting both the expected AI deliverable and the reviewer's approval responsibilities. Anthropic: Define success criteria and build evaluations
NEW QUESTION # 14
You are a knowledge worker who decomposed a "draft a board memo" request into fifteen tiny steps and noticed the output now feels disjointed because related ideas were split apart.
Which adjustment is most appropriate?
Answer: A
Explanation:
Task decomposition is valuable only when the resulting stages represent meaningful, logically connected units of work. Here, dividing a board memo into fifteen extremely small steps has fragmented related ideas and weakened the document's narrative coherence. Option B corrects over-decomposition by combining naturally connected activities into broader sections, such as framing the decision, analyzing evidence, presenting risks, and stating the recommendation.
Option A removes dependency order entirely, which would make the workflow less reliable. Option C intensifies the existing problem by creating even smaller fragments. Option D goes too far in the opposite direction by discarding all useful structure. The appropriate solution is neither maximal decomposition nor a single unstructured instruction; it is a limited number of cohesive stages with clear dependencies.
Anthropic explains that explicit prompt chaining is useful when intermediate outputs must be inspected or when a specific pipeline structure is required. However, its guidance also warns against unnecessary delegation and overengineering when simpler direct work preserves context more effectively. The correct granularity should therefore improve control and reviewability without separating ideas that must be developed together. Anthropic's prompting best practices
NEW QUESTION # 15
You are a knowledge worker evaluating which Claude feature matches each proposed use case.
Which two feature-to-use-case matches are appropriate? (Select two.)
Each correct answer presents a complete solution.
Answer: C,D
Explanation:
Option A appropriately uses Claude's long-context capabilities for a task involving many source documents.
A quarterly report may require Claude to compare evidence across financial results, operational updates, customer information, and prior reporting periods. Supplying the relevant sources in a well-organized context allows Claude to synthesize the material while following a consistent report structure. Source boundaries and attribution should remain clear so the final output can be validated.
Option E is also an appropriate match. A calculator or code-execution tool is suitable for complex, multi-step pricing calculations because it provides deterministic computation that can be inspected and tested. Claude can structure the calculation and interpret the result, while the tool performs the arithmetic more reliably.
Option B does not require long-context processing because a single short email can normally be handled directly in chat. Option C unnecessarily invokes web search for a stable, established formula that does not require current external information. Option D mismatches the tool to the task: simplifying product language is a writing and audience-adaptation task, not a numerical calculation.
Effective feature selection depends on the actual workload. Large source collections benefit from long-context synthesis, whereas precise arithmetic benefits from deterministic computational tools and explicit result validation.
NEW QUESTION # 16
You are a Claude associate troubleshooting a recurring poor output.
Which is the correct order of diagnostic steps?
(1) Form a hypothesis about which prompt element most likely caused the deficiency.
(2) Apply a single targeted change and observe the effect on the output.
(3) Read the output end to end and identify the specific deficiency.
(4) Compare the deficient output to the prompt to spot mismatches between input and output.
(5) Document the root cause and the resolution for future reference.
Answer: A
Explanation:
Under the objective Identify, diagnose, and resolve issues with underperforming prompts and/or poor outputs (Domain 7: Troubleshooting and Optimization), troubleshooting follows a structured diagnostic protocol.
The troubleshooting sequence begins with symptom identification: reading the output end-to-end to categorize the exact flaw (e.g., factual error, structural omission, tone mismatch) (Step 3). Second, the practitioner performs an input-output diff: comparing the deficient output against the prompt instructions to spot instruction omissions or ambiguities (Step 4). Third, the practitioner forms a testable hypothesis regarding which prompt element caused the failure (Step 1). Fourth, they execute a controlled experiment by applying a single targeted prompt adjustment and observing the revised output (Step 2). Finally, they document the verified root cause and solution (Step 5). This establishes the order 3, 4, 1, 2, 5 .
NEW QUESTION # 17
A knowledge worker is explaining how to use feedback to improve Claude-supported work over time.
Which description is most accurate?
Answer: A
Explanation:
Under the objective Adjust approach based on feedback and results , iterative refinement requires tight, closed feedback loops characterized by concrete observation, targeted modification, and empirical evaluation.
Option A details the gold-standard iteration cycle: capturing specific, granular defects immediately following an execution run (e.g., tone misalignment, omitted edge cases, formatting errors), translating those observations into targeted, discrete adjustments in prompt instructions or workflow logic, and validating whether subsequent outputs resolve the issue without regressions. In contrast, deferring feedback collection across multiple unmonitored cycles (Option B) delays error detection. Relying solely on high-level aggregate numerical ratings (Option C) lacks the actionable diagnostic detail needed to locate root causes. Applying sweeping, unfocused prompt overhauls (Option D) obscures cause-and-effect relationships and frequently introduces new failure modes.
NEW QUESTION # 18
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