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| Section | Weight | Objectives |
|---|---|---|
| Model Selection and Optimization | 16.8% | - Performance Optimization - Cost and Latency Optimization - Model Selection - Model Capabilities and Trade-offs |
| Prompt and Context Engineering | 11% | - Prompt Engineering - Context Engineering - Context Management and Long-Context Techniques |
| Tools and MCPs | 10.6% | - Tool Use and Tool Schemas - Building Custom Tools and MCP Servers - Model Context Protocol |
| Security and Safety | 8.1% | - Prompt Injection and Untrusted Content - Secure Tool Use and Guardrails - Safety and Responsible Development - Application Security |
| Applications and Integration | 33.1% | - Message Batches and Prompt Caching - Streaming, Error Handling and Reliability - Software Engineering Fundamentals - API Integration and Application Development - Claude API and Client SDKs - Multimodal and Structured Outputs |
| Eval, Testing, and Debugging | 2.6% | - Evaluation, Testing, and Debugging |
| Agents and Workflows | 14.7% | - Agent Construction with Claude - Agent Architecture - Agent Patterns and Frameworks |
| Claude Code | 3.1% | - Claude Code Configuration and Extensibility |
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NEW QUESTION # 35
Your Claude application produces good responses for typical inputs but struggles with edge cases. You have several labeled examples of edge-case inputs and the desired response for each. You want to use these examples to improve the model's handling of edge cases.
What is the best way to use these examples?
Answer: B
Explanation:
Option B applies few-shot, or multishot, prompting, one of Anthropic's recommended techniques for steering Claude when examples of desired behavior are available. Labeled input/output pairs give Claude concrete demonstrations of how it should respond, which is particularly valuable when edge cases are difficult to express completely through abstract rules.
Anthropic states that examples are among the most reliable mechanisms for steering output format, tone, and structure. Its prompting guidance recommends relevant, diverse examples that cover edge cases while avoiding accidental patterns. For best results, examples should be clearly separated from the main instructions, such as by using < example > and < examples > tags.
A retrieval database could be useful if a very large or dynamically selected example collection were required, but that adds unnecessary complexity for the small labeled set described. C is disproportionate: a few examples do not justify replacing the application's Claude integration with custom model training. D avoids rather than solves the identified failure mode.
Therefore, B directly uses the available supervision at inference time and allows rapid iteration through evaluation. Relevant Claude Developer topics are prompt construction, few-shot prompting, edge-case handling, example selection, evaluation-driven iteration, and behavioral steering.
NEW QUESTION # 36
Your enterprise has a contract with AWS that requires Claude API calls to flow through Amazon Bedrock rather than the direct Anthropic API. Your team is building a new Claude application and is unfamiliar with this constraint.
How would you build the application?
Answer: A
Explanation:
Option C satisfies both the enterprise routing requirement and sound application architecture. Claude is available through Amazon Bedrock, and Anthropic provides Bedrock-specific SDK integration rather than requiring applications to call api.anthropic.com directly. Current Anthropic documentation describes Claude in Amazon Bedrock as operating through AWS-managed infrastructure with AWS-native authentication, billing, and security boundaries. Newer Bedrock integrations use the Messages API shape, allowing substantial application logic to remain consistent across provider environments.
Anthropic SDKs also provide dedicated Bedrock clients-for example, Python includes AnthropicBedrockMantle for current Bedrock deployments. Keeping business logic separated from provider- specific authentication, endpoints, model identifiers, and transport configuration reduces migration and maintenance risk.
A violates architectural simplicity by duplicating every call unnecessarily. B knowingly violates the enterprise requirement until migration occurs. D directly ignores the contractual routing constraint and is therefore invalid regardless of technical feasibility.
The correct approach is to make Bedrock the configured inference provider while keeping higher-level application and agent behavior decoupled from provider-specific implementation details. Relevant Claude Developer topics are Claude API mechanics, cloud-provider integrations, Amazon Bedrock, SDK configuration, authentication boundaries, model invocation, and provider abstraction.
NEW QUESTION # 37
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 # 38
Your Claude agent performs database operations. A recent incident occurred where the agent ran a destructive query that affected production data. The team wants to add deterministic controls to prevent similar incidents.
How would you prevent similar incidents?
Answer: A
Explanation:
Option B is correct because destructive production operations require deterministic enforcement outside the model's probabilistic reasoning. Claude Code hooks can intercept lifecycle events before tool execution and explicitly allow, deny, or request further handling based on concrete rules.
Anthropic's hooks documentation provides this exact security pattern. A PreToolUse hook can inspect a proposed command before execution and return a blocking decision. Anthropic's example demonstrates blocking destructive operations such as drop table, while other commands proceed normally.
That mechanism can be adapted to database controls: block DROP, destructive DELETE, unauthorized schema modifications, or production writes; require explicit approval for high-risk operations; and allow read- only or known-safe queries automatically.
A merely increases the probability that someone might notice an unsafe operation and does not prevent execution. C assumes model capability can replace access controls, which is an unacceptable safety boundary.
D is useful behavioral guidance but remains probabilistic and cannot guarantee prevention.
Therefore, B creates a deterministic control between model intent and side-effect execution. Relevant Study Guide topics: Claude hooks, PreToolUse, tool governance, deterministic enforcement, approval gates, least privilege, and destructive-operation protection.
NEW QUESTION # 39
A teammate has asked you to explain when a Skill would be the right choice over an MCP server. The teammate is unsure how the two differ in practice when both can be reused across teams.
How would you explain the distinction?
Answer: C
Explanation:
The supplied Claude Developer question explicitly identifies D . The two mechanisms solve different extension problems. A Skill is a reusable package of domain expertise and workflow guidance. Anthropic describes Agent Skills as modular, filesystem-based capabilities containing instructions, metadata, and optional supporting resources such as scripts and templates, which Claude can load when relevant.
MCP, by contrast, defines a standardized mechanism for exposing external capabilities and context.
Anthropic's MCP integration supports MCP tools and, through client-side helpers, MCP prompts and resources. This makes MCP appropriate when Claude must communicate with an external server or reusable service interface rather than simply load packaged expertise.
Thus, Skills are appropriate for packaging repeatable instructions, procedures, scripts, templates, or domain knowledge. MCP servers are appropriate for exposing callable operations, external data resources, and shared service capabilities using the Model Context Protocol.
A incorrectly treats them as interchangeable. B incorrectly describes the difference as merely generational. C invents a universal efficiency advantage that is not the architectural distinction.
Relevant Claude Developer topics: Agent Construction, Agent Skills, MCP, reusable capabilities, resources, prompts, tools, extension architecture, and cross-team component reuse .
NEW QUESTION # 40
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