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| Section | Weight | Objectives |
|---|---|---|
| Eval, Testing, and Debugging | 2.6% | - Evaluation, Testing, and Debugging |
| Tools and MCPs | 10.6% | - Building Custom Tools and MCP Servers - Model Context Protocol - Tool Use and Tool Schemas |
| Security and Safety | 8.1% | - Prompt Injection and Untrusted Content - Secure Tool Use and Guardrails - Application Security - Safety and Responsible Development |
| Model Selection and Optimization | 16.8% | - Cost and Latency Optimization - Performance Optimization - Model Selection - Model Capabilities and Trade-offs |
| Claude Code | 3.1% | - Claude Code Configuration and Extensibility |
| Prompt and Context Engineering | 11% | - Context Engineering - Context Management and Long-Context Techniques - Prompt Engineering |
| Applications and Integration | 33.1% | - Message Batches and Prompt Caching - Streaming, Error Handling and Reliability - Software Engineering Fundamentals - Multimodal and Structured Outputs - API Integration and Application Development - Claude API and Client SDKs |
| Agents and Workflows | 14.7% | - Agent Architecture - Agent Patterns and Frameworks - Agent Construction with Claude |
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NEW QUESTION # 27
Your team is choosing how to add a capability to a Claude application. You want to apply the appropriate option, whether built-in tool, custom tool, Skill, or MCP server, based on the use case.
You would choose the option that...
Answer: A
Explanation:
Option D reflects the correct architecture-selection principle: extension mechanisms should be selected according to what the capability must accomplish rather than familiarity, novelty, or implementation convenience. Anthropic explicitly distinguishes Claude extension mechanisms by purpose. Built-in tools cover common capabilities already supported by the platform. Custom tools expose application-specific callable operations through defined schemas. Skills package reusable knowledge, instructions, and workflows.
MCP connects Claude to external systems, APIs, databases, and services through a standardized protocol.
The decision therefore depends on scope and integration boundaries. A reusable procedural workflow may belong in a Skill; access to an external enterprise system may warrant MCP; a narrowly application-specific operation can be a custom tool; and a built-in tool should generally be preferred when it already satisfies the requirement.
A makes prior team experience the architectural criterion rather than requirements. B assumes newer technology is inherently better. C optimizes implementation convenience without considering maintainability, interoperability, or reuse.
Therefore, D is the appropriate selection rule. Relevant Study Guide topics: built-in tools, custom tools, Skills, MCP, extension architecture, reuse boundaries, and capability selection.
NEW QUESTION # 28
Your Claude application is producing inconsistent outputs for similar inputs, even when using the same model and prompt. You want to debug the issue systematically.
Your debugging approach would...
Answer: A
Explanation:
The examination source explicitly identifies D . Non-determinism is a normal LLM property, but that fact does not justify assuming every inconsistency is unavoidable. Systematic diagnosis begins by capturing the complete execution conditions for successful and unsuccessful runs.
A useful trace should expose the actual user input, system prompt, prior messages or context, tools and tool results where applicable, model identifier, request parameters, output, stop reason, and relevant execution events. Current Anthropic agent infrastructure exposes session, span, and agent events specifically to provide observability into session state and execution progress. This makes comparison possible: the engineer can determine whether apparently "same" calls actually differ in accumulated context, tool state, model configuration, parameters, or external data.
A changes a variable before establishing the cause and, on newer Claude releases, temperature may not even be configurable. B generates additional samples but does not ensure the underlying conditions are understood.
C prematurely classifies unexplained behavior as acceptable nondeterminism.
The correct sequence is observe, compare, form a hypothesis, reproduce, modify, and evaluate .
Relevant Claude Developer topics: observability, tracing, debugging, model nondeterminism, request- state capture, failure reproduction, and production diagnostics .
NEW QUESTION # 29
A new Claude model release includes performance improvements for several reasoning tasks but has changed the format of its responses to system prompts that use multi-section instructions. Your application uses multi- section system prompts heavily. Initial evaluation on the application's actual workload shows the new model performs 8 percent better on reasoning tasks but produces malformed output on roughly 3 percent of requests because of the format change. The team is debating whether to upgrade.
How would you decide?
Answer: A
Explanation:
The supplied examination page marks B . The scenario already demonstrates why model upgrades must be treated as evaluated software changes rather than automatic replacements: the new model improves one metric while introducing a regression in another.
Anthropic's official model-selection guidance recommends creating benchmark tests specific to the application's use case, testing models with the application's actual prompts and data, comparing response quality and edge-case performance, and weighing performance against operational tradeoffs. Therefore, the correct action is to adapt the multi-section system prompt to the new model's behavior and repeat the evaluation. Only after the formatting regression is eliminated-or reduced below an explicitly acceptable threshold-should the upgrade proceed.
A incorrectly assumes that an 8% reasoning improvement numerically compensates for a 3% malformed- output rate; these metrics measure different consequences and cannot simply be subtracted. C treats the known incompatibility only downstream instead of first correcting the prompt/model interaction. D permanently rejects future improvement and is inconsistent with controlled lifecycle evolution.
The engineering principle is migration through regression testing and adaptation , not blind upgrading or permanent version avoidance.
Relevant Claude Developer topics: Systems Life Cycle, model migration, regression evaluation, prompt adaptation, compatibility testing, deployment gates, and continuous evolution .
NEW QUESTION # 30
You are explaining to a stakeholder why running the same Claude prompt twice can produce slightly different results. The stakeholder is concerned this means the application is broken.
How would you address the stakeholder's concern?
Answer: A
Explanation:
Option C correctly explains a fundamental property of generative language models. The supplied examination material identifies C as the correct answer. Claude generates subsequent tokens probabilistically rather than retrieving a single fixed answer for each prompt. Consequently, identical or highly similar requests can produce variations in wording, ordering, explanation depth, and sometimes substantive details.
Anthropic's API documentation explicitly states that sampling parameters control randomness and, importantly, that even configurations historically using a temperature of 0.0 were not fully deterministic .
Current newer Claude models increasingly manage sampling behavior internally, so application design should not assume byte-for-byte identical responses across executions.
Production systems therefore handle variation through architecture: schema validation for machine-consumed output, deterministic business-rule checks, retries where appropriate, evaluations for acceptable behavioral ranges, and application-level safeguards. Temperature adjustment may reduce variation on models that support the parameter, but it does not fundamentally convert an LLM into a deterministic function.
A incorrectly labels normal model behavior as a defect. B confuses network latency with generation variability. D overstates model snapshot behavior; pinning a model prevents silent model-version changes but does not eliminate sampling variability.
Relevant topics: sampling, nondeterminism, validation, retries, model versions, and robust Claude API integration .
NEW QUESTION # 31
You are designing a Claude application that processes user-submitted text. Some of that text could include sensitive information such as account numbers or passwords that the application should not send to Claude.
How would you design the application?
Answer: D
Explanation:
Option A is correct because sensitive-data protection must occur before prohibited information crosses the application's trust boundary. If account numbers, passwords, or other secrets must not be sent to Claude, asking Claude to "ignore" those values after transmission does not satisfy the requirement. The application must determine which data is permitted to leave its controlled environment and transform or reject prohibited content before constructing the Claude request.
This principle is consistent with Anthropic's defense-in-depth guidance. Anthropic recommends input screening, limiting Claude's access to sensitive information, and applying least privilege so the model receives only information necessary for the task.
B is fundamentally too late: the sensitive information has already been transmitted to the model context before the system instruction can influence behavior. C is worse because logging can create an additional store containing the sensitive information and only detects exposure after it occurs. D implements some boundary filtering but deliberately leaves coverage incomplete until production incidents reveal additional patterns.
Therefore, A requires the boundary and its permitted data flows to be defined first, then enforced comprehensively through filtering, redaction, tokenization, or rejection. Relevant Study Guide topics:
application boundaries, data minimization, sensitive-data handling, filtering, redaction, least privilege, and privacy-preserving design.
NEW QUESTION # 32
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