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| Section | Weight | Objectives |
|---|---|---|
| Applications and Integration | 33.1% | - Vision capabilities - SDK and third-party integration - Claude Messages API - Streaming and Batch API |
| Model Selection and Optimization | 16.8% | - Claude model family characteristics - Latency and performance trade-offs - Cost and token optimization |
| Tools and Model Context Protocol (MCP) | 10.6% | - MCP server development - Tool integration and usage |
| Agents and Workflows | 14.7% | - Memory and context management - Agent architecture principles - Claude Agent SDK usage - Workflow vs autonomous agents |
| Claude Code | 3.1% | - Claude Code configuration and usage |
| Evaluation, Testing, and Debugging | 2.6% | - Error handling and debugging - Output evaluation and validation |
| Security and Safety | 8.1% | - Guardrails and safety controls - AI application security |
| Prompt and Context Engineering | 11% | - Structured output handling - Context window management - Prompt design and structuring |
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NEW QUESTION # 76
A teammate has asked how the Claude SDK handles transient API errors, such as a temporary network issue or a brief rate-limit response. They want to know whether the application code needs to handle every transient error or whether the SDK provides any default behavior.
How would you describe the SDK's default behavior?
Answer: C
Explanation:
Option D precisely matches Anthropic's SDK behavior. Official SDK documentation states that transient conditions are automatically retried two times by default with exponential backoff. The default retry set includes connection failures, HTTP 408 Request Timeout, 409 Conflict, 429 Rate Limit, and 5xx server errors. Developers can alter or disable this behavior through the SDK's retry configuration.
This directly eliminates A because the retry count is configurable. B is incorrect because rate-limit responses are among the conditions automatically retried by the supported SDKs; the API also provides retry-after information where applicable. C is incorrect because the SDK does not merely log these failures and continue without attempting recovery.
Application-level handling is still necessary after the configured retry policy is exhausted. Production code should distinguish permanent errors from transient failures, preserve request IDs for diagnostics, and decide whether higher-level fallback, queuing, or user-visible failure handling is appropriate.
Therefore, D correctly characterizes the SDK abstraction: useful retry defaults combined with developer control. Relevant Study Guide topics: Claude SDKs, transient errors, HTTP 429, retries, exponential backoff, configuration, and resilient API integration.
NEW QUESTION # 77
Your Claude application is hitting context window limits when processing long customer service transcripts.
A junior developer suggests increasing the temperature parameter to fix the issue.
How would you respond?
Answer: A
Explanation:
Option A correctly separates sampling configuration from context management. Temperature historically controlled the randomness of token selection; it did not increase the number of tokens Claude could accept within a request. Anthropic's current Messages API documentation continues to describe temperature in terms of randomness and, for newer model generations, marks manual temperature control as deprecated. Therefore, changing temperature cannot solve a context-capacity problem.
Long transcripts instead require context-engineering techniques. Appropriate approaches include chunking documents, summarizing earlier material, retrieving only relevant sections, or using context editing
/compaction so high-value information remains visible while unnecessary material is removed. Anthropic's context-editing guidance explicitly supports summarization and replacement of growing conversation history to keep long-running workloads within usable context limits.
B incorrectly conflates generation parameters with context capacity. C may save some tokens but removes persistent application instructions and is therefore architecturally unsound. D modifies an unrelated parameter without addressing the root cause. Relevant Study Guide topics: context windows, token budgets, sampling parameters, summarization, chunking, and context engineering.
NEW QUESTION # 78
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: A
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 # 79
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: D
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 # 80
Your Claude application is hitting context window limits when processing long customer service transcripts.
A junior developer suggests increasing the temperature parameter to fix the issue.
How would you respond?
Answer: A
NEW QUESTION # 81
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