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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Developer Productivity & Operational Enablement | 7% | - Claude tooling configuration for teams - Developer enablement - AI-assisted developer workflows - Debugging and operational issue resolution |
| Topic 2: Solution Design & Architecture | 17% | - End-to-end architecture design - Multi-agent systems and orchestration - Alignment with business value, cost, performance, and SLAs - Architectural patterns
- Translating business problems into Claude-based AI solutions |
| Topic 3: Stakeholder Communication & Lifecycle Management | 14% | - Stakeholder management - Discovery and requirements gathering - Communicating architectural decisions - Service-level agreements - Architecture documentation - Solution lifecycle management |
| Topic 4: Evaluation, Testing & Optimization | 16% | - Cost and performance optimization - System issue diagnosis - Evaluation framework design - Evaluation metrics and datasets - A/B testing - Production monitoring and optimization |
| Topic 5: Integration | 19% | - Authentication and authorization analysis - Enterprise system integration - RAG pipeline design
|
| Topic 6: Governance, Safety & Risk Management | 14% | - AI safety and guardrails - Ethical AI considerations - Regulatory and compliance requirements - Human-in-the-loop validation - Security and risk management |
| Topic 7: Claude Models, Prompting & Context Engineering | 13% | - Prompt reuse and context engineering strategies - Claude model selection and trade-offs - System prompts and prompt templates - Context window optimization - Guardrails |
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NEW QUESTION # 62
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.
Answer:
Explanation:
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.
NEW QUESTION # 63
After a prompt-template update, several previously passing test cases now produce unexpected outputs.
Which test type is specifically designed to detect this category of failure?
Answer: B
Explanation:
Regression tests determine whether behavior that previously met requirements still passes after a change. A stable reference set provides identical inputs, expected outcomes, and scoring rules across prompt versions, allowing the team to attribute failures to the update. Integration tests verify interactions among components but may not measure detailed output behavior. Adversarial tests target attacks and misuse cases, while smoke tests establish only that essential system paths remain available. The failed cases should be added to or retained within the regression suite, and the old and new prompt should be compared under the same model, parameters, tools, and retrieval context. Anthropic describes regression evaluations as protection against behavioral backsliding. Demystifying evals for AI agents
NEW QUESTION # 64
You are classifying chunking strategies by the corpus type each is best suited to.
For each chunking strategy, select the appropriate corpus type: "Long Structured Documents,"
"Heterogeneous Short Records," or "Code or Hierarchical Specifications."
Answer:
Explanation:
Explanation:
Function-level or section-level chunking for code modules - Code or Hierarchical Specifications Tree-aware chunking that follows code or specification hierarchy - Code or Hierarchical Specifications Per-record chunking where each record is one chunk - Heterogeneous Short Records Semantic chunking along clause or paragraph boundaries - Long Structured Documents Fixed-size chunking with overlap for short records of similar length - Heterogeneous Short Records Hierarchical chunking that mirrors document section structure - Long Structured Documents Chunking must preserve the structural unit that carries meaning in the source corpus. Code and hierarchical specifications are best divided at function, module, class, or tree boundaries because arbitrary token cuts can separate definitions from their implementation or parent context. Heterogeneous short-record collections should generally preserve each record as an independent chunk. Fixed-size overlapping chunks are also effective when records have broadly similar lengths and lack meaningful internal hierarchy. Long structured documents benefit from semantic boundaries such as clauses and paragraphs, while hierarchical chunking preserves relationships among sections, subsections, and parent headings. Anthropic notes that chunk size, boundaries, and overlap materially affect retrieval performance; therefore, one universal chunking method is inappropriate. Anthropic Contextual Retrieval
NEW QUESTION # 65
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.
Answer:
Explanation:
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.
NEW QUESTION # 66
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.
Answer:
Explanation:
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.
NEW QUESTION # 67
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