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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Troubleshooting and Optimization | 10% | - Adjusting approaches based on feedback and results - Optimizing workflows for efficiency and effectiveness - Diagnosing underperforming prompts and outputs |
| Topic 2: Governance, Risk, and Responsible Use | 15% | - Identifying appropriate and inappropriate AI use cases - Data sensitivity, privacy, and regulatory considerations - Organizational AI governance and policies - Ethical and responsible AI use |
| Topic 3: Output Evaluation and Validation | 21% | - Identifying hallucinations, inconsistencies, and bias - Determining when human review or verification is required - Editing, adapting, and refining outputs - Evaluating output accuracy and completeness - Fact-checking and validation |
| Topic 4: Configuration and Knowledge Management | 12% | - Configuring Claude Projects - Managing uploaded knowledge and connectors - Maintaining configuration and knowledge sources - Managing instructions and knowledge sources |
| Topic 5: Workflow Integration and Solution Design | 16% | - Communicating Claude's value and limitations - Solution design and iteration - Analyzing requirements and use cases - Research, planning, and process optimization - Integrating Claude into existing workflows |
| Topic 6: Prompting and Task Execution | 14% | - Iterating prompts to improve output - Adapting prompting strategies to task types - Creating effective prompts - Task decomposition |
| Topic 7: Product and Model Selection | 12% | - Selecting appropriate Claude product features - Selecting Claude models - Managing context limitations and memory - Balancing quality, speed, and cost |
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NEW QUESTION # 65
A Claude associate is evaluating whether to enable a Skill that was provided by an unfamiliar third party.
Which two checks best support a sound trust decision? (Select two.)
Answer: C,E
Explanation:
A Skill from an unfamiliar party must be treated as untrusted until its origin and behavior are reviewed.
Option A verifies provenance and confirms that the Skill is permitted by the organization's governance process. Option B applies least privilege by examining what the Skill can access or execute and determining whether those capabilities are necessary for its stated task.
Public availability, popularity, or favorable reviews do not establish security. A Skill can contain instructions, scripts, dependencies, external calls, or other resources capable of causing unintended actions or exposing data. Therefore, Options C and D substitute reputation for technical and organizational validation. Option E is also unsafe because Skills may execute code or access resources available in the surrounding environment; assumed workflow boundaries are not a reliable security control.
Anthropic warns that malicious or compromised Skills can create prompt-injection, unauthorized-access, and data-exfiltration risks. Its guidance instructs users to install Skills only from trusted sources and to inspect bundled files, scripts, dependencies, network calls, and requested operations before enabling them.
Organizational allowlists and permission reviews provide complementary controls: one establishes trust in the source, while the other limits what the Skill may do. Anthropic's Skill security guidance
NEW QUESTION # 66
An operations assistant is redesigning a recurring data-processing workflow step to use Code Execution.
Which two design choices best support reliable integration? (Select two.)
Answer: A,C
Explanation:
Options A and E are correct because reliable automation requires controlled inputs and validated outputs.
Standardizing the expected input format reduces parsing failures, inconsistent field mappings, and uncontrolled variation between weekly runs. The integration should define required columns, data types, date formats, permitted null values, file naming conventions, and error-handling behaviour. This makes results comparable and supports repeatable testing.
Human verification remains necessary because successful code execution proves only that the program completed without a runtime error. It does not prove that the source data was correct, the implemented calculation matched the business requirement, or the result is operationally reasonable. A sanity check against an expected range, reconciled total, sample calculation, or known baseline helps detect silent logical and data- quality errors before downstream use.
Option B introduces avoidable variability and should instead be handled through standardized preprocessing or explicit adapters. Option C creates an undocumented interface that downstream consumers cannot reliably interpret. Option D confuses sandbox isolation and successful execution with result validation. Anthropic confirms that Code Execution can process files, perform calculations, and generate analytical outputs in a sandbox, but its outputs must still be evaluated against task-specific success criteria. See Anthropic's Code Execution documentation and evaluation guidance .
NEW QUESTION # 67
A finance consultant is adapting a Claude-generated narrative for a quarterly investor update. Investors have a fixed time window for the call and expect the headline result and forward outlook to be clear early.
Which adaptation is most appropriate for this audience?
Answer: D
Explanation:
Under the objective Edit, adapt, refine, and compare the output generated by Claude model(s) for the intended audience (Domain 2: Output Evaluation and Validation), adapting generated text requires restructuring content to match the executive decision-making style of the stakeholders.
Executive and investor audiences operate under strict time constraints and prioritize the bottom-line financial outcome before examining underlying mechanics (following the Minto Pyramid / Bottom-Line-Up-Front principle). Option C correctly structures the executive narrative: opening immediately with the headline financial figures, following with a concise breakdown of key operational drivers, and concluding with forward-looking guidance. Structuring the briefing chronologically (Option D) or burying core financial results in the middle of operational prose (Option A) creates cognitive friction and wastes time. Leading with the forward outlook before establishing current financial performance (Option B) lacks context.
NEW QUESTION # 68
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: A
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 # 69
You are an analyst reviewing a Claude response that addresses two of three required questions and skips the third.
Which prompt element most likely needs strengthening?
Answer: A
Explanation:
Under the objective Identify, diagnose, and resolve issues with underperforming prompts and/or poor outputs , diagnosing incomplete outputs requires mapping the specific failure mode directly to the corresponding prompt component.
When a model reliably answers some inquiries but completely omits another, the root cause is typically prompt ambiguity or buried instructions within dense prose. Strengthening the core task statement by explicitly enumerating the required deliverables (e.g., using numbered lists or tagged XML blocks like < question_1 > , < question_2 > , < question_3 > ) and mandating a complete response for each item (Option D) provides clear structural constraints that prevent omissions. Adding generic background context (Option A), changing persona framing (Option C), or relaxing output formatting constraints (Option B) fails to address the underlying instruction-following issue.
NEW QUESTION # 70
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