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| Section | Weight | Objectives |
|---|---|---|
| Agent Development & NVIDIA Platforms | 20% | - NVIDIA NeMo, NIM, Triton Inference Server integration - Development tools, frameworks, SDKs, deployment patterns - Scalability, performance optimization, GPU acceleration |
| Large Language Models & Generative AI for Agents | 20% | - LLM fundamentals, prompt engineering, optimization, fine-tuning - Inference optimization, model selection, integration patterns - Retrieval-Augmented Generation (RAG): design, optimization, evaluation |
| Evaluation, Governance & Production Deployment | 15% | - Agent evaluation: accuracy, reliability, safety, fairness, robustness - Observability, monitoring, logging, debugging, guardrails - Deployment, scaling, maintenance, security, ethical AI |
| Multi-Agent Systems & Orchestration | 25% | - Orchestration frameworks, workflow design, task decomposition - Multi-agent collaboration, coordination, communication protocols - Agent interaction patterns, consensus, conflict resolution |
| Foundations of Agentic AI | 20% | - Agent architectures: ReAct, Plan-Execute, Reflection, Tree-of-Thoughts - Key principles: memory, tools, perception, action, communication - Core concepts: intelligent agents, autonomy, reasoning, planning, execution |
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問題 #51
You are designing the architecture for a RAG (Retrieval-Augmented Generation) system, and you are concerned about ensuring data freshness and minimizing latency.
Which of the following is the most important consideration when designing the architecture?
答案：D
解題說明：
The rejected options are weaker because stuffing raw chunks into prompts or relying on model priors makes answers stale, irreproducible, and difficult to debug. Event-driven microservices separate ingestion, indexing, retrieval, and generation. That is the path to fresh data with low latency and maintainable updates. The architecture implied by Option D is the one that survives real workloads: separate responsibilities, explicit contracts, and measurable runtime behavior. The selected option specifically D states "Use a loosely coupled, event-driven micro-service architecture where separate services handle data indexing, retrieval, and LLM prompting.", which matches the operational requirement rather than a superficial wording match. In NVIDIA terms, RAG quality depends on data handling as much as generation; vector retrieval and reranking must be validated with their own metrics. The correct implementation surface is query transformation and fusion before generation so the model receives evidence-rich context rather than one brittle keyword match. This choice gives engineering teams the knobs they need for continuous tuning after deployment.
問題 #52
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?
答案：A
解題說明：
Autoscaling is the only choice that handles unknown usage without paying for peak capacity all day. Manual monthly analysis reacts after the cost or outage already happened. Option D fits the operating model because the problem describes an agent that must remain adaptive under changing inputs and infrastructure conditions.
The selected option specifically D states "Implementing autoscaling policies in a container orchestration environment to automatically adjust resources according to workload changes", which matches the operational requirement rather than a superficial wording match. That matters because containerized services, HPA/cluster autoscaling, GPU-aware scheduling, health probes, rolling updates, and metric-driven capacity control. This lines up with NVIDIA guidance because NVIDIA AI Enterprise deployments typically combine optimized containers, GPU Operator/DCGM visibility, and Kubernetes-native lifecycle management. The distractors fail because bare-metal scripts can benchmark well once but are weak for failover, rollback, capacity changes, and fleet observability. The result is a system that can be benchmarked, traced, and revised without destabilizing the whole agent fabric.
問題 #53
An AI Engineer is analyzing a production agentic AI system's compliance with responsible AI standards.
Which evaluation approaches effectively identify potential safety vulnerabilities and ethical risks in multi- agent workflows? (Choose two.)
答案：A,B
解題說明：
Operationally, the design depends on guardrail coverage that is tested against observed failures and adversarial prompts rather than assumed from policy text. For this scenario, the combination of Options B and D is defensible because it exposes the control plane that a senior engineer can test, scale, and harden. Audit trails, semantic policy checks, bias metrics, and adversarial tests expose ethical and safety risk. Latency is operational, not sufficient for responsible AI evaluation. Within the NVIDIA stack, Guardrails are most effective when paired with evaluation, red-team prompts, and audit metadata so coverage gaps become visible. Together, B states "Implement comprehensive audit trails using NVIDIA NeMo Guardrails with semantic similarity checks, tracking agent decisions across conversation flows and evaluating policy violations through automated compliance scoring."; D states "Deploy multi-layered evaluation combining bias detection metrics (demographic parity, equalized odds) with adversarial testing to probe agent responses for harmful outputs across diverse user populations", so the answer covers both sides of the requirement instead of solving only the model or only the infrastructure layer. The rejected options are weaker because keyword filters and one-time prompt disclaimers do not enforce policy under prompt injection, ambiguous requests, or regulated-domain escalation paths. It also creates clean evidence for audits, incident review, and root-cause analysis when behavior drifts.
問題 #54
A development team is building a customer support agent that interacts with users via chat. The agent must reliably fetch information from external databases, handle occasional API failures without crashing, and improve its responses by learning from user feedback over time.
Which of the following tasks is most critical when enhancing an AI agent to handle real-world interactions and improve over time?
答案：B
解題說明：
For this scenario, Option C is defensible because it exposes the control plane that a senior engineer can test, scale, and harden. The selected option specifically C states "Implementing retry logic for error handling and integrating user feedback loops for iterative improvement", which matches the operational requirement rather than a superficial wording match. Real systems fail at the boundaries: API outages, bad payloads, and unmodeled user feedback. Retry logic plus feedback loops closes that boundary. Operationally, the design depends on a plugin-style execution layer that keeps external systems outside the model while still letting the agent invoke them deterministically. Within the NVIDIA stack, a production NVIDIA deployment can put tool latency, errors, and schema validation into traces, then tune the workflow without changing the foundation model. The losing choices mostly optimize for short-term convenience; static or unvalidated integration choices cannot withstand transient outages, rate limits, malformed responses, or schema drift. It also creates clean evidence for audits, incident review, and root-cause analysis when behavior drifts.
問題 #55
After deploying a financial assistant agent, users report occasional inconsistencies in how transactions are categorized.
What is the best first step for diagnosing the issue?
答案：B
解題說明：
The runtime should therefore be built around a memory hierarchy that balances retrieval latency, relevance, privacy, and context-window cost. This is a lifecycle problem, not a wording problem, and Option D gives the team a controllable lifecycle for the agent behavior. Transaction categorization depends on tool inputs and outputs. Before retraining, inspect recent traces to see whether the model received incorrect or incomplete structured data. For a production build, memory is an orchestration concern as much as a model concern, because the agent must decide what to keep, retrieve, and forget. The selected option specifically D states
"Review tool call inputs and outputs in recent session logs", which matches the operational requirement rather than a superficial wording match. The rejected options are weaker because sending full history every turn inflates latency and cost, while stateless prompts lose unresolved tasks, user preferences, and multi-step plan continuity. The answer is therefore about engineered control planes, not simply model capability.
問題 #56
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