NVIDIA NCP-AAI 問題集

質問 1：
In designing an AI workflow which of the following best describes a comprehensive approach to improving the performance of AI agents?
A. Implementing benchmarking pipelines, collecting user feedback, and tuning model parameters iteratively
B. Implementing benchmarking pipelines and incorporating a dynamic dataset for a real-time fall-back
C. Implementing benchmarking pipelines, deploying physical agents and monitoring user engagement metrics
D. Monitoring agents' throughput and time-to-first-token from the scoring engine
正解：A
解説: (Topexam メンバーにのみ表示されます)

質問 2：
An agent is tasked with solving a series of complex mathematical problems that require external tools to find information. It often struggles to keep track of intermediate steps and reasoning.
Which prompting technique would be MOST effective in improving the agent's clarity and reducing errors in its reasoning?
A. Zero-shot CoT
B. ReAct
C. Symbolic Planning
D. Multi-Plan Generation
正解：B
解説: (Topexam メンバーにのみ表示されます)

質問 3：
A health assistant agent has been running on production environment for several weeks. The compliance team wants to audit how personal health data has been processed.
Which operational feature supports this requirement?
A. Adding more prompt examples to clarify privacy rules
B. Masking all output with a profanity and PII detector
C. Enabling full session logging with audit trail metadata
D. Increasing model temperature for diverse interpretations
正解：C
解説: (Topexam メンバーにのみ表示されます)

質問 4：
A technology startup is preparing to launch an AI agent platform to serve clients with unpredictable usage patterns. They face periods of high user activity and low demand, so their deployment approach must minimize wasted resources during slow times and automatically allocate more resources during busy periods
- all while keeping operational costs reasonable.
Given these requirements, which deployment strategy most effectively ensures both cost-effectiveness and adaptability for scaling agentic AI systems?
A. Scheduling periodic manual reviews to increase or decrease infrastructure based on predicted user numbers
B. Implementing autoscaling policies in a container orchestration environment to automatically adjust resources according to workload changes
C. Monitoring system logs for usage patterns and making infrastructure changes after monthly analysis
D. Using fixed-size virtual machine clusters to guarantee consistent resource allocation at all times
正解：B
解説: (Topexam メンバーにのみ表示されます)

質問 5：
You're employing an LLM to automate the generation of email responses for a customer service team. The generated responses frequently miss the mark, failing to address the customer's underlying concerns.
What's the most crucial element to add to the prompt to enhance the quality of the email responses?
A. Instructing the LLM to "understand the customer's issue" before generating a response.
B. Instructing the LLM to provide a response that "is the most helpful" before generating a response.
C. Instructing the LLM to use a simple template for all email replies before generating a response.
D. Instructing the LLM with a detailed prompt containing instructions on how to format and compose the response in an easy-to-understand structure.
正解：D
解説: (Topexam メンバーにのみ表示されます)

質問 6：
You are developing an agent that needs to perform a complex set of tasks repeatedly.
Why is periodic fine-tuning an important aspect of long-term knowledge retention for this type of agent?
A. It eliminates the need for external storage like RAG.
B. It guarantees the agent will produce the same output for the same input.
C. It prevents the agent from becoming overly specialized to a single task.
D. It prevents the agent from forgetting past successes and failures.
正解：D
解説: (Topexam メンバーにのみ表示されます)

質問 7：
When evaluating coordination failures in a multi-agent system managing distributed manufacturing workflows, which analysis approach best identifies state management and planning synchronization issues?
A. Track workflow throughput and task completions to measure performance trends and highlight workflow outcomes.
B. Monitor agent outputs individually to confirm local correctness and examine results of specific workflow steps.
C. Assess synchronization methods during design reviews and use simulations to evaluate coordination across representative workflow scenarios.
D. Deploy distributed state tracing across agents, analyze transition timing, study communication overhead, and verify synchronization accuracy.
正解：D

質問 8：
A senior AI architect at a public electricity utility is designing an AI system to automate grid operations such as outage detection, load balancing, and escalation handling. The system involves multiple intelligent agents that must operate concurrently, respond to changing data in real time, and collaborate on tasks that evolve over multiple interaction steps. The architect must choose a design pattern that supports coordination, flexible task delegation, and responsiveness without sacrificing maintainability.
Which design approach is most appropriate for this scenario?
A. Build a rule-driven control structure that maps task flows to predefined paths for fast and efficient execution under known operating conditions.
B. Design the system as a stepwise sequence of agent functions, where each stage processes and passes data to the next in a fixed functional chain.
C. Use an agent service architecture with decoupled execution units managed by a shared interface layer that handles communication and task routing.
D. Adopt a role-based agent model coordinated through a shared task planner, where agent decisions are informed by centralized policy logic and runtime context signals.
正解：D
解説: (Topexam メンバーにのみ表示されます)

質問 9：
A company operates agent-based workloads in multiple data centers. They want to minimize latency for users in different regions, maintain continuous service during infrastructure upgrades, and keep operational costs predictable.
Which deployment practice best supports low-latency, resilient, and cost-efficient agent operations at scale?
A. Apply static infrastructure allocation with centralized resource usage monitoring at a single data center.
B. Implement geo-distributed deployments with rolling updates and resource usage monitoring.
C. Schedule regular agent downtime for system updates and operational recalibration.
D. Prioritize high-performance GPUs for all agents in geo-distributed deployments.
正解：B
解説: (Topexam メンバーにのみ表示されます)