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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Tools and MCPs | 10.6% | - Building Custom Tools and MCP Servers - Tool Use and Tool Schemas - Model Context Protocol |
| Topic 2: Eval, Testing, and Debugging | 2.6% | - Evaluation, Testing, and Debugging |
| Topic 3: Security and Safety | 8.1% | - Prompt Injection and Untrusted Content - Secure Tool Use and Guardrails - Safety and Responsible Development - Application Security |
| Topic 4: Agents and Workflows | 14.7% | - Agent Construction with Claude - Agent Architecture - Agent Patterns and Frameworks |
| Topic 5: Prompt and Context Engineering | 11% | - Context Management and Long-Context Techniques - Context Engineering - Prompt Engineering |
| Topic 6: Applications and Integration | 33.1% | - Software Engineering Fundamentals - Multimodal and Structured Outputs - Streaming, Error Handling and Reliability - Claude API and Client SDKs - Message Batches and Prompt Caching - API Integration and Application Development |
| Topic 7: Claude Code | 3.1% | - Claude Code Configuration and Extensibility |
| Topic 8: Model Selection and Optimization | 16.8% | - Cost and Latency Optimization - Performance Optimization - Model Capabilities and Trade-offs - Model Selection |
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NEW QUESTION # 53
You are starting a new Claude application and have a small set of well-labeled examples that demonstrate the desired output format. You want to use these examples to guide Claude's behavior.
How would you guide the application's behavior?
Answer: C
Explanation:
The supplied examination set identifies A as correct. A small collection of high-quality labeled examples is ideally suited to few-shot or multishot prompting . The examples demonstrate concretely what acceptable input/output behavior looks like, allowing Claude to infer formatting, structure, tone, and task-specific conventions without requiring model retraining.
Anthropic's official prompting guidance states that examples are among the most reliable ways to steer Claude's output format, tone, and structure. It recommends using relevant, diverse examples and clearly separating them from the surrounding instructions. Anthropic currently recommends approximately three to five examples where practical and suggests XML structures such as < examples > and < example > to make prompt organization explicit.
B discards useful supervision by relying exclusively on zero-shot behavior. C is unnecessary for a small fixed set and does not ensure those examples are actually visible to Claude unless additional retrieval logic is created. D introduces unnecessary training complexity for a behavior that prompting already addresses efficiently.
Therefore, A provides the lowest-complexity, highest-leverage solution.
Relevant Claude Developer topics: Agent Construction, multishot prompting, few-shot learning, labeled examples, prompt design, output formatting, behavioral steering, and prompt evaluation .
NEW QUESTION # 54
You maintain a Claude application that uses Claude Sonnet 4.5 across several production workflows.
Anthropic released Claude Sonnet 4.7, which your evaluation suite shows performing 8% better on your highest-volume task. However, this version produces different output formatting on two of your structured- extraction prompts that downstream consumers parse with regex-based code.
To roll out the upgrade, you would...
Answer: A
Explanation:
Option A is correct because a model upgrade should be treated as a controlled application change, not a simple identifier substitution. Anthropic's evaluation guidance recommends defining measurable success criteria and running task-specific evaluations that mirror real production behavior, including edge cases. Its model-migration guidance likewise recommends testing replacement models before moving production workloads.
Here, the new model improves the highest-volume task but changes output formatting on structured-extraction prompts. That means the migration has both a quality benefit and a compatibility risk. The correct response is to tighten the output contract, re-run evaluations against the parsing/schema boundary, then deploy progressively with a feature flag and a per-workflow rollback path. This limits blast radius and preserves a known-good recovery option.
Option B pushes an unverified behavior change directly into production. Option C makes downstream parsing permissive, which can conceal schema drift instead of enforcing a stable contract. Option D permanently preserves a fragile implementation and discards the measured quality improvement.
Taking the model version stated in the question as the scenario, A is the correct lifecycle strategy. Relevant Study Guide topics: model migration, regression evaluation, structured output, compatibility testing, progressive rollout, rollback, and production change management.
NEW QUESTION # 55
You are writing a system prompt for a Claude application that needs to produce output in a specific JSON shape. The downstream system will reject any output that does not match the schema.
Your prompt would need to...
Answer: B
Explanation:
The supplied question selects D . If a downstream component requires an exact machine-readable structure, the expected structure must be communicated explicitly rather than left to Claude's discretion. The prompt should define required fields, types, nesting, permissible values where relevant, and instruct Claude not to emit surrounding prose.
Anthropic's consistency guidance states that developers should precisely specify the desired output format when format consistency matters. More importantly, current Claude APIs provide Structured Outputs for cases requiring guaranteed JSON Schema conformance; Anthropic explicitly recommends Structured Outputs instead of prompt-only techniques when valid schema-compliant JSON is mandatory.
Therefore, D is the strongest prompt choice among the listed alternatives. In a contemporary production implementation, the design can be strengthened further by supplying the schema through Claude's structured- output configuration and performing downstream semantic validation where business rules exceed JSON Schema.
A permits arbitrary formatting. B intentionally creates inconsistent representations. C assumes post- processing can reliably reconstruct missing or ambiguously formatted information, which is significantly less robust than specifying the contract up front.
Relevant Claude Developer topics: system prompts, JSON formatting, structured outputs, schema constraints, output contracts, validation, and downstream integration reliability .
NEW QUESTION # 56
You are designing an agent that handles a complex claim-processing workflow. Each claim moves through fact extraction, eligibility evaluation, and a decision step. The three subtasks have distinct success criteria, and some claims require iteration between fact extraction and eligibility evaluation before a decision can be reached.
Which agent pattern would you apply?
Answer: D
Explanation:
A is correct because the workflow is state-dependent, non-linear, and iterative . The source explicitly identifies the graph-based pattern as the intended architecture for this scenario. Each stage-fact extraction, eligibility evaluation, and decision-making-has its own completion criteria, and the process may need to move backward from eligibility evaluation to fact extraction when information is incomplete. A graph representation naturally models these conditional transitions and loops.
Anthropic's current orchestration guidance supports workflows containing branching, loops, filtering, staged execution, and state-dependent control flow , rather than forcing every task through one fixed sequence.
Dynamic workflow orchestration can use explicit control logic so the next processing stage depends on current state and previous results.
B concerns progressive output delivery, not workflow-state transitions. C provides a generic agentic tool loop but does not explicitly model distinct states or transition criteria. D is unsuitable because it prohibits the required return path between extraction and eligibility evaluation.
Therefore, a graph-based architecture provides the necessary conditional routing, iteration, and stage-specific validation.
Relevant Claude Developer topics: Agent Patterns, graph workflows, state transitions, conditional branching, loops, stage-specific success criteria, and agent orchestration .
NEW QUESTION # 57
The Anthropic API deprecated a request parameter that your Claude application uses in approximately 40 places across the codebase. The deprecation notice gives a six-month window before the parameter is removed and recommends a replacement parameter with slightly different semantics.
You would respond to the deprecation by...
Answer: A
Explanation:
The supplied examination source selects C . Because the replacement parameter has different semantics , this is not a mechanical rename. The application must establish what existing behavior is important, encode that behavior in regression tests, and migrate incrementally so deviations can be detected and isolated.
Anthropic's deprecation guidance follows the same lifecycle principle. Deprecated components remain temporarily available but receive a retirement deadline and a recommended replacement. Anthropic advises migrating before retirement and thoroughly testing applications against replacements well in advance of the cutoff. Its API versioning documentation also emphasizes compatibility contracts while acknowledging that APIs evolve and deprecated versions eventually become unavailable.
Batch migration reduces blast radius. If one migrated group fails regression tests, the team can diagnose the semantic difference before changing remaining call sites. It also avoids concentrating all migration risk near the retirement deadline.
A delays risk until the worst possible time. B only hides the dependency and does not complete migration. D changes all 40 usages simultaneously, making regression diagnosis and rollback substantially harder.
Relevant Claude Developer topics: API lifecycle, deprecation management, regression testing, incremental migration, compatibility, technical debt, and controlled change management .
NEW QUESTION # 58
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