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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Agents and Workflows | 14.7% | - Subagents and Agentic Frameworks
|
| Topic 2: Tools and MCPs | 10.6% | - Tool Development and Integration
|
| Topic 3: Applications and Integration | 33.1% | - Application Development and Integration
|
| Topic 4: Model Selection and Optimization | 16.8% | - Model Selection
|
| Topic 5: Eval, Testing, and Debugging | 2.6% | - Testing and Debugging
|
| Topic 6: Security and Safety | 8.1% | - Safety and Guardrails
|
| Topic 7: Prompt and Context Engineering | 11% | - Prompt Engineering
|
| Topic 8: Claude Code | 3.1% | - Claude Code Configuration and Usage
|
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NEW QUESTION # 55
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: C
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 # 56
Your Claude application's outputs are inconsistent in format. The team's instructions are scattered across the system prompt and user messages, with some instructions stated only once and others repeated.
How would you fix the inconsistency?
Answer: C
Explanation:
Option B creates a clear separation between persistent application instructions and per-request user content.
Stable behavioral rules-role, output conventions, formatting constraints, policies, and other instructions intended to apply across requests-belong in the system-level instruction layer. User messages should primarily carry task-specific input and requirements that vary from interaction to interaction.
Anthropic's Messages API defines the system prompt specifically as the mechanism for providing overarching context and instructions to Claude. Current guidance likewise recommends the top-level system field for instructions that should apply from the beginning of the conversation, while more transient information belongs in conversation messages.
A mixes persistent application behavior with user-controlled content and weakens separation of concerns. C preserves the inconsistent architecture responsible for the problem. D duplicates instructions unnecessarily, wastes context tokens, and increases the possibility that future edits create conflicting versions of the same rule.
Consolidating stable instructions also makes prompts easier to version, evaluate, review, and maintain.
Therefore, B is the correct design. Relevant Study Guide topics: system prompts, instruction hierarchy, prompt organization, formatting consistency, separation of concerns, and maintainable prompt construction.
NEW QUESTION # 57
The team is debating whether to build a new capability as a custom tool or to use an existing built-in tool that nearly covers the use case but lacks one specific feature.
How would you decide?
Answer: C
Explanation:
The supplied Claude Certified Developer Foundations material identifies A as the best answer. The decision should begin with the actual capability gap rather than a predetermined preference for built-in or custom tooling. If the existing capability can satisfy the requirement with a small supported extension or composition, using it avoids unnecessary implementation and maintenance. If the missing capability is fundamental, a custom tool becomes justified.
Anthropic's tool architecture explicitly supports both categories. Claude provides Anthropic-managed or predefined tools while also allowing developers to define custom tools for operations specific to their application. With a custom client tool, the application defines the contract and executes the operation before returning its result to Claude.
A is more complete than C because it evaluates both sides of the boundary : whether the existing mechanism can realistically meet the missing requirement and, if not, whether custom implementation is warranted. B prioritizes precedent over current requirements. D only determines whether the unmodified built-in tool is sufficient and does not address what to do when it is almost-but not entirely-sufficient.
Relevant Claude Developer topics: built-in tools, custom tools, capability-gap analysis, tool contracts, maintenance cost, extensibility, and agent construction decisions .
NEW QUESTION # 58
A teammate has asked you to explain the difference between context engineering and prompt engineering.
They have heard the terms used interchangeably and are unsure how each applies to a Claude application that processes long-running multi-step tasks.
How would you describe the distinction?
Answer: C
Explanation:
Option C accurately captures Anthropic's distinction. Prompt engineering primarily concerns how instructions are written, structured, and organized to obtain the desired behavior from a particular model invocation.
Techniques include explicit instructions, examples, roles, XML structure, output requirements, and task- specific prompt construction. Context engineering operates at a broader architectural level: it determines which information should actually be present in the model's context at each inference step.
Anthropic defines prompt engineering as methods for writing and organizing LLM instructions, whereas context engineering encompasses strategies for curating and maintaining the optimal set of tokens during inference. For long-running agents, context can contain system instructions, tools, MCP resources, retrieved documents, prior messages, tool results, summaries, and memory.
This distinction matters because multi-step agents continuously generate new state. Effective systems may prune obsolete results, retrieve information just in time, compact earlier conversation history, isolate subagent contexts, or store persistent state externally. B is incorrect because context engineering has not simply replaced prompt engineering; the two operate at different scopes. A defines context too narrowly, and D obscures an important architectural distinction.
Therefore, C correctly represents Claude Developer coverage of prompt engineering versus context engineering, context curation, agent state, long-horizon workflows, and context-window optimization.
NEW QUESTION # 59
Your Claude application requests structured JSON output from the model. Most of the time the JSON is well- formed, but occasionally Claude returns malformed JSON that breaks downstream processing.
How would you handle the malformed output?
Answer: B
Explanation:
Option B establishes a controlled boundary between probabilistic model output and deterministic downstream code. When structured data is machine-consumed, malformed JSON must be recognized as an explicit application error rather than allowed to propagate into parsers, databases, or other services.
Anthropic's Structured Outputs documentation identifies malformed JSON, missing fields, inconsistent types, and schema violations as exactly the kinds of failures that can break downstream systems when unconstrained output is used. Current Claude capabilities can constrain responses using JSON Schema, and SDK helpers can provide parsing and validation.
Even when an application uses an older or unconstrained generation path, it should parse against the expected schema, record validation failure, and enter a bounded recovery path such as retry, repair, fallback, or controlled rejection. A human review of every request is unscalable. C removes a useful structured interface instead of making it reliable. D performs uncontrolled blind retries and provides no schema-aware error handling or bounded fallback strategy.
Therefore, B gives the application explicit failure semantics and protects downstream systems. Relevant Study Guide topics: structured output, JSON validation, schema enforcement, retries, fallback handling, defensive parsing, and downstream reliability.
NEW QUESTION # 60
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