Instant Access Anthropic CCAR-P Exam Premium Dumps - FreeCram

The compliance team has authored a regulatory disclosure procedure that must be applied identically across customer service, sales, and onboarding workflows. The procedure changes when regulators issue updates, currently four to six times per year. You are designing how the procedure will be packaged for use by Claude across all three workflows.
Which two design decisions should you include? (Select two.)
Each correct answer presents part of the solution.

• A. Have each workflow team rewrite the procedure for its own context.
• B. Store the procedure in a shared retrieval corpus accessed by all three workflows.
• C. Reference the same Claude Skill from all three workflow integrations.
• D. Package the procedure as a Claude Skill owned directly by the compliance team.
• E. Embed the procedure text into each workflow's system prompt at integration time.

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You are evaluating retrieval-strategy claims used by a peer team.
For each claim, select yes if the statement is generally accurate. Otherwise, select no.

Explanation:
Yes, Yes, Yes, No, No
Dense vector retrieval represents semantic similarity, making it suitable for matching paraphrases and conceptually related language even when the query and source do not share identical words. Sparse lexical retrieval, including BM25-style matching, retains strong sensitivity to exact tokens and is therefore valuable for identifiers, product codes, technical names, and uncommon terminology.
Structured query retrieval is appropriate when the required operation depends on explicit fields, predicates, joins, counts, grouping, or aggregation over a relational schema. In that situation, generating or invoking a constrained database query is more precise than approximating the operation through semantic similarity.
Hybrid retrieval is not identical to dense retrieval. It combines semantic and lexical candidate sets, typically followed by rank fusion or reranking. Anthropic's contextual-retrieval guidance explains that semantic search captures meaning and paraphrases, while BM25 captures exact terminology; combining them improves coverage. Contextual Retrieval , Contextual Retrieval Cookbook Random sampling is not a relevance strategy. It provides no systematic relationship between the query and selected evidence, producing unstable coverage and preventable hallucination risk. Production Q & A requires deterministic or evaluated relevance mechanisms, access filters, suitable indexes, and measurable retrieval metrics such as recall at k .
Study Guide references/topics: Dense retrieval; sparse retrieval; structured queries; hybrid search; rank fusion; retrieval evaluation; production RAG design.

You are a solution architect evaluating candidate use cases for a Claude-based program.
For each scenario, select Yes if Claude is appropriate as the primary solution at the architectural level.
Otherwise, select No.

Explanation:
* Drafting first-pass investigative reports from semi-structured incident logs for analyst review - Yes
* Computing real-time fraud scores at sub-50-millisecond latency on a streaming pipeline - No
* Long-context contract review with structured clause extraction and deviation flagging - Yes
* Replacing a vector index for semantic retrieval over a multi-million-document corpus - No
* Routing inbound support tickets into 30 categories with reasoning for the selected route - Yes Claude is well suited to language-intensive analysis, summarization, extraction, classification, and drafting.
Investigative-report drafting, contract analysis, and support-ticket routing all require interpretation of unstructured or semi-structured language and can incorporate human review. A sub-50-millisecond fraud- scoring path requires deterministic, specialized real-time processing and should not place LLM inference on the critical path. Claude also does not replace the retrieval index used to search millions of documents; it consumes the results produced by that infrastructure. The correct architectural decision separates tasks requiring semantic language reasoning from deterministic computation and information-retrieval infrastructure. Model selection and evaluation must then confirm that the chosen Claude model satisfies quality, latency, and cost requirements.

A senior architect is managing stakeholder expectations for a Claude-based reporting assistant midway through development. Stakeholders have escalating concerns about response latency.
Which two actions most directly address stakeholder expectation alignment in this situation? (Select two.)

• A. Pause all development and reallocate engineering resources entirely to latency optimization.
• B. Revise the SLA definition collaboratively with stakeholders if current targets are not achievable given production constraints.
• C. Communicate that latency concerns are a known LLM limitation and outside the architecture team's control.
• D. Replace the current Claude model with a third-party model that may offer lower latency without evaluation.
• E. Present measured p50 and p95 latency baselines against the agreed SLA thresholds so stakeholders have accurate data.

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You are integrating Claude Code into the team's pull-request workflow. The team wants AI-assisted review without removing human approval.
Which integration design best fits this requirement?

• A. Claude Code silently deletes pull requests it assesses as low quality without posting a comment or notifying the author.
• B. Claude Code merges every pull request automatically after completing its analysis, bypassing human approval and the existing branch-protection rules.
• C. Claude Code reviews the pull request and posts a structured analysis as a comment, while a human reviewer retains the approval decision under the existing branch-protection rules.
• D. Claude Code disables all existing branch-protection rules to streamline the merge process, removing human approval as a required gate.

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