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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Product and Model Selection | 12% | - Select appropriate Claude product features - Match model capabilities to task requirements - Understand model differences and use cases |
| Topic 2: Troubleshooting and Optimization | 10% | - Identify causes of poor or unexpected outputs - Adjust prompts and configuration to improve results - Diagnose context and performance issues |
| Topic 3: Governance, Risk, and Responsible Use | 15% | - Apply responsible AI principles and policies - Understand limitations and ethical boundaries - Manage data sensitivity and privacy risks |
| Topic 4: Output Evaluation and Validation | 21% | - Apply fact-checking and validation methods - Determine when human review is required - Detect hallucinations, biases, and inconsistencies - Evaluate outputs for accuracy, completeness, and consistency |
| Topic 5: Workflow Integration and Solution Design | 16% | - Design AI-assisted processes and solutions - Plan end-to-end Claude-augmented workflows - Integrate Claude into existing business workflows |
| Topic 6: 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 7: Configuration and Knowledge Management | 12% | - Configure Claude Projects with instructions - Manage knowledge sources and context - Maintain consistent system-level behavior |
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NEW QUESTION # 144
An support representative is configuring a Project for drafting customer-facing apology emails after service outages. The current custom instructions tell Claude to "be empathetic and casual, keep it short, and offer a goodwill credit." The customer-service team has flagged that recent drafts have been too brief, have offered goodwill credits in cases where policy does not allow them, and have used contractions that the brand voice guide prohibits.
Which adjustment best aligns the configuration to the use case?
Answer: A
Explanation:
Under the Claude objective Configure Claude Projects with instructions and knowledge sources , effective project configuration requires replacing vague, conflicting directives with explicit operational boundaries and grounded criteria. In this scenario, the root causes of underperformance are conflicting requirements: the prompt requests a "casual" tone while brand guidelines prohibit contractions, asks Claude to
"keep it short" leading to overly brief outputs, and unconditionally directs it to "offer a goodwill credit" without verifying eligibility criteria.
Option C properly resolves all three deficiencies directly within the project instructions. First, it explicitly operationalizes the tone attributes (empathetic, formal, avoiding contractions) rather than relying on ambiguous descriptors. Second, establishing concrete length constraints (such as minimum paragraph or sentence counts) prevents the model from generating under-scoped drafts. Third, it conditions the business logic-instructing Claude to evaluate goodwill credit awards against the attached policy document in the Project knowledge base. In contrast, relying solely on uploaded knowledge files without aligning custom instructions (Options B and D) leads to prompt collisions, while Option A removes necessary policy guidance altogether.
NEW QUESTION # 145
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: D
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 # 146
You are a Claude associate assessing a proposed Claude use case before approving it for your team.
Which is the correct order of assessment steps?
(1) Compare the use case against the organization's policies and the relevant regulatory considerations.
(2) Document the assessment outcome and the rationale for traceability.
(3) Identify the audience, decision impact, and reversibility of the outputs.
(4) Decide whether the use case proceeds, requires modification, or is declined.
(5) Identify the data types involved and whether sensitive data is implicated.
Answer: B
Explanation:
A sound assessment begins by establishing who will receive or rely on the output, the consequences of an incorrect result, and whether the resulting action can be reversed. This initial impact analysis determines the level of scrutiny required. The assessor should then identify the data involved, especially personal, confidential, regulated, or otherwise sensitive information. Once the use case and its information exposure are understood, it can be compared meaningfully against organizational AI policies, legal requirements, and regulatory restrictions.
The next step is to decide whether the use case should proceed, be modified with safeguards, or be rejected.
Documentation comes last because it records the completed assessment, decision, supporting evidence, restrictions, and rationale for auditability. Therefore, the correct order is 3,5,1,4,2.
This sequencing reflects the AI Fluency principles of Delegation and Diligence: humans determine whether and how AI should be used and remain responsible for the consequences. Anthropic also advises users to be thoughtful about highly sensitive personal and business information. Options A, C, and D place policy review or data classification before the use case's audience and impact have been properly established. Anthropic AI Fluency
NEW QUESTION # 147
You are using Claude to redesign an employee onboarding workflow and must complete the high-level decomposition steps before specifying detailed artifacts.
Which two decomposition steps must be completed BEFORE specifying exact email content and the schedule? (Select two.) Each correct answer presents part of the solution.
Answer: D,E
Explanation:
Under the objective Apply task decomposition techniques to structure complex requests , complex business problems must be decomposed hierarchically-from macro objectives down to granular operational artifacts.
Before generating low-level execution components such as scheduled email templates or individual checklists, the architect must first establish the high-level structure. This entails defining the overall strategic objective of the program (Option D) to establish success metrics, and identifying the sequential lifecycle phases (e.g., Pre-boarding, Day 1, Week 1, 30-60-90 days) of the onboarding journey (Option B). Once the overarching framework and chronological phases are clearly defined, prompt engineers can systematically task Claude with drafting the supporting content for each specific phase. Piloting (Option A) and full rollout (Option E) occur downstream after artifact creation, while budgetary negotiations (Option C) are outside workflow decomposition.
NEW QUESTION # 148
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: B,E
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 # 149
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