Free Microsoft AB-410 Exam Questions

Become Microsoft Certified with updated AB-410 exam questions and correct answers

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Total 111 Questions | Updated On: Oct 25, 2026

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Question 1

In a global financial firm, an AI Architect is building a multi-agent compliance assistant using an agentic AI framework. The system must manage short-term memory for multi-turn interactions and long-term memory for persistent user and policy context. It should enable contextual recall and adaptation across sessions using NVIDIA’s tool stack. Which architectural approach best supports these requirements?

A : Leverage NVIDIA NeMo Framework with modular memory management, integrating conversationalstate tracking, knowledge graphs, and vector store retrieval, while using LoRA-tuned models to adaptresponses overtime.

B : Leverage RAPIDS cuDF for memory tracking by streaming multi-turn conversation logs as GPUresident data frames, assuming transactional history can be recalled and reasoned over using dataframeoperations.

C : Rely exclusively on TensorRT to encode all prior knowledge into compiled model weights, allowinginference-only execution with no external memory dependencies across sessions.

D : Leverage NVIDIA Triton Inference Server with dynamic batching to cache session-level inputsbetween inference calls, and use an external Redis store for long-term memory.

Answer: A

Question 2

When evaluating a multi-agent customer service system experiencing unpredictable scaling costs and performance bottlenecks during peak hours, which analysis approaches effectively identify optimization opportunities for both infrastructure efficiency and service reliability? (Choose two.)

A : Maintain consistent resource allocation across all service hours, for a more precise view of baseline traffic impact on long-term infrastructure efficiency.

B : Scale agent infrastructure based on aggregate performance trends, using system-wide monitoring toolsto identify broader optimization patterns across resources.

C : Deploy agents with configurable scaling workflows, allowing analysis of resource adjustment strategies and their effects on service stability during variable demand periods.

D : Deploy distributed tracing with cost attribution per agent type, correlating resource consumption withbusiness value metrics to identify optimization opportunities in agent deployment strategies.

E : Implement comprehensive workload profiling using NVIDIA Nsight to analyze GPU utilization patterns, identify underutilized resources, and optimize batch sizing for dynamic scaling withKubernetes HPA.

Answer: D,E

Question 3

An engineer has created a working AI agent solution providing helpful services to users. However, during live testing, the AI agent does not perform tasks consistently. Which two potential solutions might help with this issue? (Choose two.)

A : Remove schema validations and assertions on tool outputs to avoid inconsistency.

B : Increase randomness (e.g., temperature) and remove fixed seeds to avoid determinism.

C : Identify where dividing the tasks into subtasks and handling them by multiple agents can help.

D : Refine the prompt given to the AI Agent; be clear on objectives

Answer: C,D

Question 4

When implementing stateful orchestration for agentic workflows using LangGraph, which memory management approach provides the best balance of performance and context retention?

A : Store complete conversation history in memory with periodic database syncing

B : Implement rolling window memory with fixed conversation length limits

C : Use session-ID based checkpointer with user-defined schema for selective state persistence

Answer: C

Question 5

You are designing an AI agent for summarizing medical documents that include images and text as well. It must extract key information and recognize dates. Which feature is most critical for ensuring the agent performs well across multiple input and output formats?

A : Use of guardrails to filter out hallucinated content

B : Retry logic implementation to ensure robustness during API failures

C : Chain-of-thought prompting for reasoning accuracy

D : Multi-modal model integration to handle both text and vision inputs

Answer: D

Page: 1 / 23

Total 111 Questions | Updated On: Oct 25, 2026

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