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| Section | Weight | Objectives |
|---|---|---|
| Output Evaluation and Validation | 21% | - Fact-checking and validation - Evaluating output accuracy and completeness - Determining when human review or verification is required - Editing, adapting, and refining outputs - Identifying hallucinations, inconsistencies, and bias |
| Configuration and Knowledge Management | 12% | - Maintaining configuration and knowledge sources - Managing uploaded knowledge and connectors - Configuring Claude Projects - Managing instructions and knowledge sources |
| Prompting and Task Execution | 14% | - Adapting prompting strategies to task types - Task decomposition - Iterating prompts to improve output - Creating effective prompts |
| Workflow Integration and Solution Design | 16% | - Research, planning, and process optimization - Communicating Claude's value and limitations - Solution design and iteration - Integrating Claude into existing workflows - Analyzing requirements and use cases |
| Product and Model Selection | 12% | - Balancing quality, speed, and cost - Selecting appropriate Claude product features - Selecting Claude models - Managing context limitations and memory |
| Troubleshooting and Optimization | 10% | - Adjusting approaches based on feedback and results - Diagnosing underperforming prompts and outputs - Optimizing workflows for efficiency and effectiveness |
| Governance, Risk, and Responsible Use | 15% | - Identifying appropriate and inappropriate AI use cases - Organizational AI governance and policies - Ethical and responsible AI use - Data sensitivity, privacy, and regulatory considerations |
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NEW QUESTION # 22
You are an analyst fact-checking a Claude-generated report. Two cited sources give conflicting dates for the same event.
Which approach is correct?
Answer: D
Explanation:
When cited sources conflict, the analyst must resolve the discrepancy through source hierarchy and corroboration. Option C is correct because a primary or authoritative source-such as the original announcement, official record, regulatory filing, or responsible organization's publication-provides stronger evidence than assumptions based on publication timing or repetition.
Publishing both dates without resolution transfers the analyst's verification responsibility to the reader.
Averaging dates is logically invalid because event dates are categorical facts, not measurements that can be meaningfully averaged. Selecting the newer source is also unreliable; a later publication can repeat an earlier error, refer to a different milestone, or introduce a transcription mistake. The analyst should determine whether the sources describe the same event, locate the most authoritative record, and retain a citation showing how the final date was confirmed.
Anthropic recommends making claims auditable through citations and checking generated statements against supporting source material. Its hallucination guidance further advises retracting a claim when adequate support cannot be found. Consequently, if no authoritative source resolves the discrepancy, the report should clearly disclose the uncertainty rather than manufacture a definitive date. Anthropic's guidance on reducing hallucinations
NEW QUESTION # 23
You are a marketing manager deciding what to include in custom instructions for a writing-focused Project.
Which two items belong in the custom instructions? (Select two.)
Each correct answer presents a complete solution.
Answer: B,D
NEW QUESTION # 24
An analyst has been asked to compute summary statistics from an uploaded sales data file and produce a chart for a weekly report.
Which output approach best fits the task?
Answer: A
Explanation:
Under the objective Organize and curate information for optimal outputs and select appropriate output formats , quantitative data analysis and visual charting require deterministic tooling rather than probabilistic text generation alone.
Large Language Models can occasionally make arithmetic errors when calculating summary statistics directly over raw text tokens in large files. Leveraging Claude's built-in Code Execution (such as Python in Artifacts or Analysis Tool) allows the model to run exact programmatic calculations (mean, median, variance) across complete datasets and render visualization charts (Option D). Furthermore, adhering to validation best practices by checking computed figures against source data ensures factual integrity. Options A, B, and C fail standard analytical quality bars by relying on unverified estimates, generating incomplete placeholders, or omitting essential metrics and visual deliverables.
NEW QUESTION # 25
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: D
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 # 26
You are setting up a Connector for a shared data source that several colleagues will use.
Which Connector setup step should be performed first?
Answer: C
Explanation:
Option D is the necessary first step because authorization and data suitability must be established before the shared source is connected to Claude. The team should verify that the connector and its provider are approved under organizational AI, security, procurement, and privacy policies. It must also determine the source's data classification and whether information in that source may legally and contractually be processed through the connector.
Connecting the source first, as proposed in Option C, could expose restricted information before governance review has occurred. Testing a representative task under Option B likewise assumes that access has already been approved. Documenting the connector's purpose and suitable use cases under Option A is important, but that operational documentation should follow the initial determination that the connector and relevant data are permitted.
After approval, the team can connect the source using an account with appropriate permissions, verify authentication, test access boundaries, and confirm that Claude retrieves only information available to the authenticated user. The team should then document approved use cases, prohibited data, human-review requirements, and reporting procedures. This sequence follows least-privilege and data-governance principles and avoids treating successful authentication as evidence that a connector is organizationally authorized or suitable for every type of information.
NEW QUESTION # 27
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