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| Section | Weight | Objectives |
|---|---|---|
| Prompt and Context Engineering | 11% | - Prompt Engineering
|
| Eval, Testing, and Debugging | 2.6% | - Evaluation
|
| Claude Code | 3.1% | - Claude Code Configuration and Usage
|
| Agents and Workflows | 14.7% | - Subagents and Agentic Frameworks
|
| Tools and MCPs | 10.6% | - Tool Development and Integration
|
| Applications and Integration | 33.1% | - Application Development and Integration
|
| Security and Safety | 8.1% | - Safety and Guardrails
|
| Model Selection and Optimization | 16.8% | - Performance and Cost Optimization
|
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NEW QUESTION # 80
A teammate has asked why your Claude application sometimes produces a response that includes the prompt text repeated back, and other times produces a response with the prompt text rephrased. They suspect a bug in the application's request construction.
How would you respond?
Answer: A
Explanation:
Option B reflects the fundamental generative behavior of large language models. Claude does not retrieve a fixed response template for a given prompt. Instead, it generates the response incrementally, selecting subsequent tokens according to the probability distribution produced by the model. Consequently, semantically equivalent runs can differ in wording, ordering, whether prompt language is quoted, and whether it is paraphrased.
Anthropic's API documentation explicitly describes sampling parameters as controlling randomness in subsequent-token selection and also states that even configurations intended to minimize randomness do not make output completely deterministic. Current Claude model generations increasingly manage sampling internally, but the underlying principle remains: generative output is not guaranteed to be textually identical across equivalent calls.
Therefore, seeing the original prompt echoed in one response and paraphrased in another does not, by itself, demonstrate malformed request construction. A real request-construction problem should instead be established through traces showing different system instructions, messages, parameters, or context.
A, C, and D incorrectly infer specific implementation faults without supporting evidence.
The supplied question identifies B as the correct response. Relevant topics: SW Eng Foundations, token generation, sampling, nondeterminism, debugging, and model behavior.
NEW QUESTION # 81
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: B
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 # 82
Your Claude application uses tool calling to fetch patient data and generate summary reports. The flow occasionally fails because the model returns a tool_use block that references arguments not present in the schema, and your application code does not handle this case gracefully.
How would you address this?
Answer: A
Explanation:
The question presents a trust-boundary failure: model-generated tool arguments are being accepted without sufficient validation before an external operation executes. A tool call should therefore be treated like input arriving at any typed application interface-the application must ensure that the tool name and arguments satisfy the declared contract before dispatch.
Anthropic describes tool use as a contract in which the application defines the tool's schema and Claude returns structured tool_use input. Current Claude functionality also supports strict: true, which guarantees schema-compliant tool inputs through constrained generation and specifically addresses invented parameters, incompatible types, and missing required fields. For applications not using strict tool use, explicit application- side validation and a controlled error path remain essential.
B knowingly executes invalid input. C relies on uncontrolled repetition rather than deterministic validation and bounded recovery. D eliminates a useful capability instead of correcting its execution boundary.
Therefore, A provides the correct defensive design. Relevant Study Guide topics: tool calling, tool_use, input_schema, strict tool use, schema validation, defensive execution, error handling, and secure tool dispatch.
NEW QUESTION # 83
Your team's Claude agent has accumulated several customizations that bypass the SDK's defaults, including custom history management, retry logic, and error handling. A new team member has proposed reverting all the customizations to maintain the codebase more easily. The tech lead disagrees and says each customization was added for a reason.
How would you advise the team?
Answer: A
Explanation:
The correct approach is to evaluate each customization independently against both its original requirement and the capabilities currently supplied by the Claude SDK. Custom code has an ongoing maintenance cost, but automatically removing it is unsafe because some modifications may encode genuine application requirements that the standard SDK behavior still does not address. Conversely, automatically retaining every customization can preserve obsolete complexity after the SDK has evolved.
This is particularly relevant because Anthropic's SDKs already provide capabilities that developers might otherwise implement themselves. Official Claude API documentation states that SDKs provide built-in request formatting, error handling, retries, timeouts, and other integration functionality. For example, transient connection, rate-limit, and server errors are retried automatically with exponential backoff.
Therefore, each custom history, retry, or error-management layer should be reviewed for its actual purpose. If an SDK primitive now provides equivalent behavior, removing duplicate code improves maintainability. If the customization still satisfies a distinct requirement, it should remain.
The supplied exam source explicitly identifies D as the intended answer. Relevant topics: Agent Construction, SDK capabilities, abstraction boundaries, technical-debt management, retries, error handling, and agent state management.
NEW QUESTION # 84
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: C
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 # 85
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