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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 |
| Topic 2: Output Evaluation and Validation | 21% | - Evaluate Claude-generated outputs for accuracy and completeness - Edit and adapt outputs for the intended audience - Apply fact-checking and validation techniques - Organize and curate information and select appropriate output formats - Identify hallucinations, inconsistencies, and biases in responses - Determine when human review or additional verification is required |
| Topic 3: Prompting and Task Execution | 14% | - Apply task decomposition techniques to structure complex requests - Iterate prompts to improve output quality - Create effective prompts for business and technical tasks - Adapt prompting strategy by task type |
| Topic 4: Troubleshooting and Optimization | 10% | - Optimize workflows for efficiency and effectiveness - Adjust approaches based on feedback and results - Identify and diagnose issues with underperforming prompts or poor outputs |
| Topic 5: Workflow Integration and Solution Design | 16% | - Integrate Claude into existing workflows and communicate its value and limitations - Support solution design through iteration - Analyze requirements and use cases - Use Claude for research, planning, and process optimization |
| Topic 6: Configuration and Knowledge Management | 12% | - Manage uploaded knowledge and connectors - Maintain and update configurations - Create effective system-level instructions - Configure Claude Projects with instructions and knowledge sources |
| Topic 7: Product and Model Selection | 12% | - Select appropriate Claude product features - Differentiate between Claude model types - Align model selection with task requirements - Manage context limitations and memory |
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NEW QUESTION # 82
A knowledge worker is adjusting an approach after a successful pilot revealed two opportunities for further improvement. The pilot's core results were positive, and the team is preparing for a broader rollout.
Which adjustment is most appropriate?
Answer: A
Explanation:
Answer B is correct. The pilot has already demonstrated that the core approach is effective, so the team should retain the successful elements while making targeted changes for the two identified improvement opportunities. A controlled comparison before full rollout then provides evidence that the revised approach performs at least as well as the original pilot and that the modifications produce the expected improvement.
Option A ignores known improvement opportunities and unnecessarily carries identified weaknesses into production. Option C implements the changes during the broader rollout without validating them first.
Monitoring is necessary, but it is not a substitute for controlled pre-rollout testing because defects could affect many more users. Option D tests only the two opportunities in isolation. This may fail to evaluate how the new changes interact with the rest of the successful workflow.
A controlled comparison can evaluate the pilot configuration and the revised configuration against the same acceptance criteria, representative cases, and quality measures. This preserves what worked while reducing the risk of unintended regression. Anthropic's evaluation guidance recommends measurable success criteria, task-specific tests, and evaluations that distinguish between competing approaches. Therefore, B represents the strongest evidence-based adjustment before scaling the workflow. Anthropic: Define success criteria and build evaluations
NEW QUESTION # 83
Before uploading a customer file for a Code Execution analysis, a customer-success associate at Noventra, Ltd. is reviewing the planned task.
Which two practices best align with sound data handling? (Select two.)
Answer: B,C
Explanation:
Options B and E apply organizational governance and data-minimization requirements before the file enters Claude's execution environment. The associate must first establish whether Code Execution is an approved processing method for the file's classification. If the information exceeds the approved sensitivity level, the user must escalate to privacy, security, legal, or another designated authority rather than proceeding independently.
The file must also be inspected for customer identifiers, confidential fields, regulated information, credentials, or data unrelated to the calculation. Unnecessary fields should be removed, redacted, or anonymized. A sandbox limits interaction with the surrounding system, but it does not change the sensitivity of uploaded data, provide authorization, or override the organization's handling rules. Therefore, Options C and D incorrectly treat technical isolation as a governance exemption. Option A allows urgency to displace mandatory review.
Anthropic explains that Code Execution runs in an isolated container, while also warning that file-processing configurations and network access create security considerations. Its privacy guidance recommends careful treatment of highly sensitive personal and confidential information. The correct control is to minimize and authorize the data before uploading it, not rely solely on the sandbox afterward. Anthropic's Code Execution security guidance
NEW QUESTION # 84
A Claude associate is identifying the bottleneck in a content workflow that takes too long to publish. The workflow has five steps: research, drafting, internal review, legal review, and publication. There is cycle-time data for each step from the past month.
Which signal most directly identifies the bottleneck?
Answer: B
Explanation:
Option A is correct because a persistent queue or growing backlog directly indicates that work is arriving faster than a step can process it. That step is constraining the throughput of the overall workflow: earlier work accumulates while later stages wait for completed items. Queue length should still be examined over a representative period to distinguish a sustained bottleneck from a temporary spike.
Option C is useful supporting evidence but is not always the most direct signal. A step may have a long per- item cycle time because the work is inherently complex while still possessing sufficient capacity to handle incoming demand. Conversely, a relatively short step can become the bottleneck when its available capacity is too low for the volume arriving. Employee-reported workload in Option B is subjective and may reflect effort, stress, or simultaneous responsibilities rather than workflow throughput. Option D relies on an unsupported assumption: a recently added step is not necessarily the constraint.
A sound Claude-assisted optimization process should map the workflow, collect measurable evidence, identify the constraint, test a targeted improvement, and compare results against a baseline. Anthropic recommends measurable, task-relevant success criteria and evaluation methods rather than vague impressions.
Therefore, the largest sustained queue or backlog is the strongest direct indicator. See Anthropic's guidance on defining measurable success criteria and evaluations .
NEW QUESTION # 85
A knowledge worker is iterating a prompt that has produced a partly acceptable response.
Which refinement practice produces the strongest learning across iterations?
Answer: B
Explanation:
Under the objective Iterate prompts to improve output quality , systematic prompt optimization relies on disciplined, hypothesis-driven iteration using controlled variables.
When a prompt yields a partially acceptable result, modifying a single isolated element at a time-such as refining role framing, adjusting format constraints, or adding specific negative constraints-allows the user to establish clear causality between the prompt modification and the resulting output change (Option A).
Documenting the effect of each adjustment ensures reproducible, predictable improvements. Conversely, making multiple simultaneous revisions (Options C and D) confounds the evaluation, making it impossible to determine which specific change helped or degraded output quality. Discarding work entirely on each run (Option B) eliminates incremental learning and increases iteration time.
NEW QUESTION # 86
You are a project manager reviewing Claude usage approaches that teammates have proposed for a project workflow.
Which two workflow steps are well-suited for Claude support? (Select two.) Each correct answer presents a complete solution.
Answer: C,D
Explanation:
Under the objective Identify appropriate and inappropriate use cases , determining suitable application domains hinges on evaluating AI capabilities against risk severity and the presence of human oversight.
Claude excels at information extraction, multi-document synthesis, and structured text generation.
Synthesizing complex findings from multiple research papers (Option D) and compiling meeting notes into organized, structured status summaries (Option E) are classic high-leverage knowledge-work tasks that streamline team operations while keeping final outputs subject to team review. Conversely, allowing an AI model to autonomously authorize external corporate press releases without human sign-off (Option A), bind an organization to contractual liabilities (Option B), or determine career-altering personnel promotions (Option C) represents dangerous and inappropriate delegation that violates responsible AI principles, legal compliance, and human accountability standards.
NEW QUESTION # 87
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