[Practice Exams] AWS Certified Generative AI Developer Pro (Udemy.com)

Preparing for AWS Certified Generative AI Developer Professional AIP-C01? This is THE practice exams course to give you the winning edge.

These practice exams have been co-authored by Stephane Maarek and Abhishek Singh who bring their collective experience of passing 20 AWS Certifications to the table. Abhishek has passed the exam for AWS Certified Generative AI Developer Professional AIP-C01 on day one of the beta release.

Why Serious Learners Choose These Practice Exams

• Human-crafted, exam-aware questions backed by real AWS expertise
  Every item is designed by an instructor with deep, hands-on AWS experience and insight into how AWS actually tests concepts, not mass-generated by generic AI tools.

• Authentic exam feel with blueprint-aligned difficulty and distractors
  Questions mirror the tone, complexity, and trap patterns used in actual certification exams, helping learners build confidence under realistic conditions.

• Enhanced with diagrams, flows, and AWS-doc-based explanations
  Answers include visually rich explanations, custom diagrams, and carefully written descriptions distilled from official AWS documentation.

• Updated to reflect real-world patterns and the latest AWS services
  Content stays aligned with how AWS evolves its exams, focusing on the topics and service combinations most likely to appear in current and upcoming versions.

• Designed to build actual problem-solving skill, not just memorization
  Scenarios train reasoning across architectures, security, data, and GenAI patterns, preparing learners to think like Generative AI Developers instead of guessing. Do not just ace the exam, become a stronger AWS Professional.

• New questions added to stay ahead of the exam curve

  Fresh scenarios and updated patterns are continuously published so learners always train with the latest question styles, service updates, and exam trends.

We want you to think of this course as the final pit-stop so that you can cross the winning line with absolute confidence and get AWS Certified! Trust our process, you are in good hands.

Quality speaks for itself:

SAMPLE QUESTION:

A large manufacturing company is experiencing a significant increase in unplanned downtime due to recurring production incidents. Each incident typically requires analyzing large volumes of sensor data, reviewing historical maintenance logs, and determining the most effective remediation plan. The engineering leadership wants to automate this multi step diagnostic workflow using a set of specialized AI agents that can work together, exchange findings, and dynamically delegate parts of the investigation based on their expertise.

As a GenAI developer, which approach should you implement to enable specialized agents to collaborate and coordinate effectively during root cause analysis? (Select two)

1. Configure a single Amazon Bedrock Agent with multiple action groups that perform sensor inspection, log parsing, and remediation recommendations within one agent. Add prompt engineering rules that instruct the agent to self coordinate expertise across these domains without using multi agent collaboration features

2. Configure specialized AWS Strands Agents for sensor analysis, maintenance log interpretation, and remediation planning, and attach clear role definitions and tools to each agent

3. Configure three AWS Strands Agents with their specialized capabilities but run them as independent agents without an orchestration layer. Instruct the application to call each agent sequentially and combine their outputs manually without enabling direct collaboration or shared state between them

4. Configure AWS Strands Agents for each specialization and use custom API Gateway endpoints for agents to exchange findings for centralized coordination system

5. Use AWS Agent Squad to orchestrate collaboration so that agents can exchange intermediate findings and dynamically delegate subtasks during incident analysis

What's your guess? Scroll below for the answer.

Answer: 2,5

Correct options:

Configure specialized AWS Strands Agents for sensor analysis, maintenance log interpretation, and remediation planning, and attach clear role definitions and tools to each agent

This approach directly addresses the requirement for specialized agents that work together on the same incident. You first define three AWS Strands Agents, each with a focused responsibility: one agent that reads and interprets time series sensor streams, a second agent that parses and reasons over maintenance logs, and a third agent that synthesizes remediation plans. Within Strands, you configure each agent with the right tools, prompts, and model configuration so that its reasoning loop is aligned with its domain. Specialization makes each agent easier to tune, reduces prompt complexity, and allows you to evolve capabilities independently over time.

Core concepts of Strands Agents - reference image 1

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Core concepts of Strands Agents - reference image 2

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Use AWS Agent Squad to orchestrate collaboration so that agents can exchange intermediate findings and dynamically delegate subtasks during incident analysis

AWS Agent Squad then acts as the orchestration fabric that allows these agents to work together instead of operating in isolation. You configure Agent Squad so that the sensor agent can request log insights from the maintenance agent, and the remediation agent can query both for the latest findings before proposing actions. The squad configuration defines how agents share partial conclusions, delegate tasks, and decide when an incident investigation is complete. This pattern matches the intended design of multi agent orchestration frameworks where different specialists collaborate through structured communication to solve complex, cross cutting problems like root cause analysis.

How Agent Squad works - reference image

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Incorrect options: