CCAR-F Practice Test 17 — 15 Questions

Question 1

Which TWO of the following practices should be implemented to ensure Claude Code operates securely within a production-adjacent environment?

Accepted Answer

Use custom instructions to explicitly forbid 'rm -rf' or database deletion commands.. Security in AI-assisted coding requires a defense-in-depth approach. By enforcing the principle of least privilege through custom instructions and strictly auditing shell execution commands, architects can mitigate risks associated with autonomous code generation. These practices protect against inadvertent destructive actions and ensure that the agent remains within defined operational boundaries, which is essential for maintaining the integrity of production-adjacent environments.

Answer

Grant Claude Code full sudo permissions to debug system-level issues.

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Enable 'auto-approve' for all shell command executions to speed up workflows.

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Hardcode the Anthropic API key into the project's config file for convenience.

Question 2

A developer is using Claude Code to refactor a large TypeScript repository. They notice that Claude Code is attempting to read and analyze thousands of generated log files in the /dist folder, slowing down performance. Which configuration change should the developer make to optimize this workflow?

Accepted Answer

Add 'dist/' to the .claudeignore file in the repository root.. To optimize Claude Code's performance, developers must effectively manage the context window and search scope. By configuring the .claudeignore file, users explicitly instruct the tool to skip non-essential directories like /dist. This ensures that the model focuses its attention only on relevant source code, reducing latency, improving accuracy, and preventing token exhaustion during complex refactoring tasks, which is critical for large-scale enterprise development projects.

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Run the command with the --no-search flag permanently.

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Set the CLAUDE_MAX_TOKENS environment variable to a lower value.

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Use the --exclude-files flag during every terminal session.

Question 3

Refer to the exhibit. A developer is experiencing slow response times during complex refactoring tasks. Based on the provided configuration, what is the most likely cause of performance issues?

Accepted Answer

The max_tokens setting is causing extended generation times.. The configured max_tokens value of 8192 is significantly high, which directly impacts the latency of the API responses. During complex refactoring, if the model attempts to generate very long sequences, the time-to-first-token and total generation time increase. Reducing this value to a more constrained scope ensures faster turnaround times while still allowing sufficient room for code generation, optimizing the developer's workflow during iterative coding sessions.

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The temperature of 0.2 is too high for code generation.

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The model version is experimental and suffers from high latency.

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The configuration is missing the system_prompt field.

Question 4

When designing an MCP tool that interacts with a high-latency external API, which strategy should be implemented to ensure a smooth interaction for the end-user?

Accepted Answer

Utilize MCP progress notifications to inform the client about the long-running operation's status.. Implementing asynchronous status updates or progress reporting is critical for long-running tool operations in MCP. Since the protocol supports tool-initiated notifications, the server can inform the client about the status of a request before final completion. This prevents timeouts and provides transparency, ensuring the user experience remains responsive even when the underlying tool depends on slow, external network-bound API calls.

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Increase the timeout values on the client side to wait indefinitely for the tool response.

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Cache all API responses locally on the server to avoid calling the API during tool execution.

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Split the request into multiple smaller tools to reduce the overall latency of the single API call.

Question 5

An application uses Claude 3.5 Sonnet to summarize legal documents. Occasionally, the model hallucinates clauses not present in the source text. What is the most effective architectural approach to ground the model's output?

Accepted Answer

Use RAG to inject the relevant document snippets into the system prompt and instruct the model to only use that data.. Implementing Retrieval-Augmented Generation (RAG) forces the model to rely on provided documents rather than internal training weights. By injecting the specific legal clauses into the system prompt context, you create a rigid boundary for the model's knowledge. This architectural pattern is essential for high-stakes domains like legal or medical analysis, where accuracy is non-negotiable and hallucinations can lead to significant liability or incorrect decision-making.

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Increase the temperature setting to 1.0 to ensure the model explores more creative possibilities.

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Fine-tune the model on the full legal corpus to embed the documents directly into its weights.

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Reduce the maximum tokens allowed for the response to prevent the model from generating extra text.

Question 6

Which mechanism does Claude Code use to maintain session state across multiple prompts during a single refactoring task?

Accepted Answer

It manages conversation history in memory and sends it with each request.. Claude Code utilizes a stateless API connection, but the CLI maintains session state by managing a conversation history that is passed back to the model with every request. This history includes previous turns, tool outputs, and file modifications. This approach ensures that the model remains aware of previous decisions and code changes, allowing it to perform multi-step refactoring tasks logically and coherently while minimizing unnecessary re-computation of the base context.

Answer

It keeps a long-running TCP connection open to the server.

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It saves the full conversation history to a local SQLite database.

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It uses a global state variable on the Anthropic API servers.

Question 7

Which property of a well-structured prompt contributes most significantly to the reliability of a model's output in a zero-shot scenario?

Accepted Answer

Providing clear, domain-specific instructions and output format constraints.. Clear, role-based, and specific instructions define the 'context' in which the model operates. Providing explicit constraints (such as 'no markdown', 'format as JSON', or 'be concise') reduces ambiguity. Reliability is highest when the model's output space is narrow and clearly defined. Without these constraints, models may interpret requests differently, leading to unpredictable formatting or content that violates the requirements of the downstream consuming application.

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Using a highly complex and verbose prompt to cover all possible edge cases.

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Writing the prompt in a conversational tone to make the model feel more comfortable.

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Omitting examples to allow the model to show its full range of reasoning.

Question 8

When designing tools that perform destructive actions, such as deleting a cloud resource or sending a high-priority email, which architectural pattern is most recommended by Anthropic?

Accepted Answer

Require a Human-in-the-Loop (HITL) step where a human must review and approve the tool call before execution.. Safety and control are paramount when granting LLMs the ability to change the state of external systems. Implementing a Human-in-the-Loop (HITL) pattern for sensitive operations prevents autonomous errors that could have significant financial or operational consequences. This design choice balances the efficiency of AI with the necessary oversight for high-stakes tasks.

Answer

Implement a 'dry_run' parameter that the model must set to 'true' before it is allowed to set it to 'false'.

Answer

Use a specialized model with lower temperature settings to ensure the tool calls are always predictable and safe.

Answer

Encrypt the tool's API key and store it in a secure environment that only the model can access during runtime.

Question 9

When designing an MCP server, which TWO of these factors primarily influence the model's performance in selecting the right tool?

Accepted Answer

The semantic quality and uniqueness of the 'name' field.. Performance in tool selection depends on the clarity of the interface presented to the model. The tool name and its description are the first indicators the model evaluates to determine relevance. Architects must ensure these are highly descriptive, specific, and concise. Proper naming and documentation reduce ambiguity, ensuring the model reliably maps user requests to the correct tools, which is essential for consistent automation in complex, multi-tool environments.

Answer

The total amount of system memory allocated to the MCP server.

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The character count of the internal code implementation.

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The version number of the MCP server protocol.

Question 10

Which of the following describes the 'Chain of Thought' prompting strategy, and why should it be used for complex logic tasks?

Accepted Answer

Asking the model to outline its reasoning process step-by-step before providing the final answer.. Chain of Thought involves asking the model to show its reasoning process before delivering the final answer. This is critical for complex tasks because it forces the model to decompose the problem into intermediate steps, which significantly reduces logical errors. By 'thinking' through the steps, the model is less likely to hallucinate the result, leading to more reliable and verifiable outputs in complex analytical scenarios.

Answer

Providing the model with a list of facts and asking it to select the most relevant one.

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Instructing the model to write its output in a specific programming language format.

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Forcing the model to repeat the user's prompt back to them to ensure comprehension.

Question 11

Which technique is most effective for ensuring an LLM correctly follows complex multi-step instructions?

Accepted Answer

Use Chain-of-Thought prompting.. Chain-of-Thought (CoT) prompting forces the model to articulate its reasoning process step-by-step before finalizing an answer. This is highly effective because it breaks down a complex task into manageable, sequential steps. When the model 'thinks aloud,' it is less likely to miss hidden constraints or make logical errors in the final result, which is crucial for reliability in complex workflows.

Answer

Keep the prompt as short as possible.

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Increase the frequency of model polling.

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Use a higher temperature setting.

Question 12

When Claude Code suggests a series of file edits, what is the best strategy to ensure the integrity of the project?

Accepted Answer

Apply changes in a feature branch and perform a full test suite run.. Treating Claude's suggestions as a draft is the safest workflow. By applying changes in a temporary branch, the developer can run tests, review the diffs, and ensure the logic is sound before finalizing the merge. This approach leverages the speed of the model for generation while maintaining the human oversight necessary for mission-critical code, preventing potential issues caused by subtle hallucinations or logic errors in the generated output.

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Accept all suggested changes immediately to save time.

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Use an automated script to verify the syntax of the generated code.

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Only use Claude Code for small, non-production helper functions.

Question 13

Which THREE of the following are benefits of using JSON Schema for MCP tool input definitions?

Accepted Answer

It enforces strict type-checking on the input arguments before the tool logic is invoked.. JSON Schema provides a formal contract that facilitates three key areas: automated validation, clear documentation for the model, and consistent error handling. By defining the structure, types, and constraints of inputs, you ensure that the server rejects invalid requests before they reach sensitive business logic. This proactive approach significantly reduces runtime errors and provides the model with the necessary constraints to generate accurate tool calls, leading to a more reliable agent.

Answer

It automatically generates a graphical user interface for the end-user to trigger tools manually.

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It allows the server to change the tool's input structure dynamically based on the current time.

Question 14

Refer to the exhibit. The developer notices that Claude occasionally truncates the sentiment analysis report. What is the most likely cause based on the JSON configuration?

Accepted Answer

The 'max_tokens' limit is insufficient for the length of the required sentiment analysis report.. The 'max_tokens' parameter in the JSON configuration limits the total number of tokens the model can generate. If the model's analysis exceeds this value, the response will be cut off, resulting in an incomplete output. This is a common reliability issue when the expected output length is variable or misunderstood. Setting a sufficiently high 'max_tokens' ensures the model has enough headroom to complete its reasoning and output process.

Answer

The temperature is set to 0, which prevents the model from generating longer, more descriptive responses.

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The model is receiving an error because the messages array is malformed.

Answer

The model version 'claude-3-5-sonnet-20240620' does not support sentiment analysis tasks.

Question 15

You are using Claude to generate complex JSON configurations. The model frequently truncates the output because the response exceeds the token limit. What is the most robust architectural solution to handle this?

Accepted Answer

Decompose the prompt into multiple sequential calls that generate parts of the configuration.. When complex output is required, breaking the task into sub-tasks or using a more granular approach is superior to just increasing token limits. Truncation is often a sign of a complex, monolithic prompt. By splitting the work into smaller, manageable chunks, you maintain high output quality, ensure structural integrity, and avoid the risks associated with hitting hard output length limits in a single turn.

Answer

Increase the max_tokens parameter to the absolute maximum allowed by the API.

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Use a system prompt to ask the model to summarize the configuration if it gets too long.

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Switch to a model with a smaller context window to force the model to be concise.